Phil. Trans. R. Soc. A (2007) 365, 273–295
doi:10.1098/rsta.2006.1910
Published online 21 November 2006
Biomimetic approaches to the control of
underwater walking machines
B Y J OSEPH A YERS *
AND
J AN W ITTING †
Department of Biology and Marine Science Centre, Northeastern University,
East Point, Nahant, MA 01908, USA
We have developed a biomimetic robot based on the American lobster. The robot is
designed to achieve the performance advantages of the animal model by adopting
biomechanical features and neurobiological control principles. Three types of controllers
are described. The first is a state machine based on the connectivity and dynamics of the
lobster central pattern generator (CPG). The state machine controls myomorphic
actuators based on shape memory alloys (SMAs) and responds to environmental
perturbation through sensors that employ a labelled-line code. The controller supports a
library of action patterns and exteroceptive reflexes to mediate tactile navigation,
obstacle negotiation and adaptation to surge. We are extending this controller to
neuronal network-based models. A second type of leg CPG is based on synaptic networks
of electronic neurons and has been adapted to control the SMA actuated leg. A brain is
being developed using layered reflexes based on discrete time map-based neurons.
Keywords: lobster; robot; biomimetic; behaviour; underwater; electronic neuron
1. Introduction
There has been considerable interest in the development of biomimetic robots based
on animal models (Taubes 2000). Arthropods have long been the models for the
study of the mechanisms of generation of locomotion and navigation (Evoy & Ayers
1982; Bassler & Buschges 1998; Durr et al. 2004; Ritzmann et al. 2004). We have
developed an ambulating underwater robot based on the biomechanics and
behavioural set of the American lobster (Ayers 2004b). These robots are intended
for use in shallow water remote-sensing operations (Ayers 2000). The behavioural
set of the lobster represents a proven solution of navigation and adaptation in these
complex and often turbulent environments (Atema & Voigt 1995). Implementation
of an underwater vehicle based on the lobster (figure 1) presents unique problems
with regard to actuation, sensing, hydrodynamics and particularly in adaptive
biomimetic control (Ayers 2004b). The controllers that we have developed are
based on the neurophysiology and behaviour of the animal model (Ayers 2002a,
2004a), and range from finite state machines to electronic neuronal networks.
* Author for correspondence (lobster@neu.edu).
†
Present address: Department of Oceanography, Sea Education Association, PO Box 6, Woods
Hole, MA 02543, USA.
One contribution of 15 to a Theme Issue ‘Walking machines’.
273
q 2006 The Royal Society
274
J. Ayers and J. Witting
Figure 1. The NU/DARPA/ONR lobster robot. The vehicle is based on a watertight electronics
bay and associated battery pack. Actuators include eight three degrees of freedom legs, as well as
claw- and abdomen-like hydrodynamic control surfaces. Motor-driven antennae are active flow
surfaces.
Although the understanding of the cellular mechanisms of operation of the
lobster nervous system is advanced (Selverston 1993), the understanding of the
higher order control mechanisms of navigation, obstacle negotiation and
investigative behaviour is incomplete (Factor 1995; Cattaert & Le Ray 2001).
The actual test of the role of interneurons in the control of behaviour requires
recording from the identified elements of the underlying neuronal circuitry in
freely behaving animals and presents enormous technical difficulties (Pinsker &
Ayers 1983). The embodiment of neuronal circuits in a behavioural controller
becomes a model to test the underlying hypotheses of how the nervous system
controls behaviour (Webb 2000, 2001). For a robotic instantiation to model
animal behaviour, it must both approximate the biomechanics as well as the
dynamics of the underlying neuronal components (Abarbanel et al. 2004).
2. Lobster biomechanics
When compared with terrestrial arthropods (Bassler & Buschges 1998), underwater walking arthropods, e.g. the lobster, face a set of unique challenges. First,
lobsters must deal with a variety of often complex bottom types such as sand,
cobble, rock fields or eel grass beds (Campbell & Stasko 1986). Dealing with
obstacles in the environment requires high manoeuvrability and lobsters have the
ability to change their walking direction on a step-by-step basis (Ayers & Davis
1977b). Rapid rotations in place are mediated by walking forward on one side and
backward on the other (Copp & Jamon 2001). The rotation is enhanced by close
placement of legs on the thorax (figure 2a). During walking, lobsters can yaw by
walking more rapidly on one side relative to the other (Domenici et al. 1998) and/
or differences in amplitude of movements on the two sides (Cruse & Saavedra
1996) or a combination of these mechanisms.
Phil. Trans. R. Soc. A (2007)
Biomimetic approach to underwater machines
275
(a)
(b)
Figure 2. (a) The biomimetic body plan of the lobster robot superimposed on an image of an
American lobster. (b) Stop-frame video image of a lobster waking head on into a 25 cm sK1 laminar
flow from left to right in a flume. Note the use of the chelipeds and abdomen as control surfaces.
Second, the mass distribution of the lobsters differs profoundly from that of insects
with the large chelipeds and abdomen extending rostrally and caudally with regard
to a comparably sized thorax (figure 2a). Unlike terrestrial organisms which must
bear their entire mass, lobsters are buoyed by seawater and weigh about one-eighth
of their mass in air. Thus, lateral hydrodynamic forces can be greater than vertical
gravitational forces (Martinez 2001). Decapods use their chelipeds and abdomen as
hydrodynamic control surfaces to achieve stability in the pitch plane (Maude &
Williams 1983). They also take advantage of a splayed posture of the walking legs to
provide stability in the roll plane. An additional consequence of low relative mass is
the reduced traction during surge. The only contact point of the body with substrate
is the dactyls, and when the gravitational forces are low, traction is reduced. Having
an additional pair of legs when compared with insects may be an adaptation to the
need for an increased traction. When faced with flow and surge, lobsters and crayfish
may also elevate their abdomen and depress their claws (figure 2b), to generate a
thrust vector into the substrate, substantially increasing traction.
3. Lobster locomotory movements
The walking movements of lobster legs occur primarily around three limb joints
(Ayers & Davis 1977b). Cyclic elevation/depression movements of the coxobasal (CB) joint (figure 3a–c), underlie the swing and stance phase of walking
Phil. Trans. R. Soc. A (2007)
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J. Ayers and J. Witting
joint angle
60
coxo-based joint
(a)
(c)
(b)
elevation
45°
35
0°
joint angle
10
depression
thoraco-coxal joint
40
(d )
0
joint angle
140
leading
trailing
forward
backward
–40
(f)
(e)
– 45°
retraction
0°
protraction
mero-carpopodite joint
(i)
(h)
(g)
135°extension
100
60
( j)
45°
0
0.5
phase
(k)
1.0
0
0.5
phase
1.0
early swing
late swing
stance
movement
phases
elevator
depressor
swing
stance
motor
synergies
45°
90°
flexion
Figure 3. Limb movement’s underling walking in different directions. (a–c) Coxo-basal joint
movements; (d–f ) thoraco-coxal joint movements and (g–i) mero-carpopodite joint movements.
(a, d and g) indicate joint movements during forward (closed circles) and backward (open circles)
walking. (b, e and h) indicate joint movements during lateral leading (closed circles) and trailing
(open circles) walking. (j) Three movement phases of the step cycle. (k) Epochs of muscle activity
in the four primary motor synergies.
in all directions (figure 3a–c). During forward and backward walking,
translational propulsive forces are generated by cyclic movements of
the thoraco-coxal (ThC) joint (figure 3d–f ). The swing phase of the
ThC joint is coupled with cyclic elevation (early swing) and depression
(late swing) of the CB joint, while the stance phase is coupled with the
antigravity (relatively isometric) phase of the CB depression movement. During
lateral walking, the translational movements are generated by cyclic movements of the mero-carpopodite (MC) joint and coordinated with CB joint
movements in an analogous fashion (figure 3g–i). During forward and backward
walking, the antagonist extensor and flexor muscles of the MC joint are
coactivated (figure 3g) to keep the joint stiff (Ayers & Clarac 1978), while the
ThC joint is stiffened throughout the step cycle during lateral walking
(figure 3e).
Phil. Trans. R. Soc. A (2007)
Biomimetic approach to underwater machines
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4. Neurobiology of lobster locomotion
Studies over the past 50 years have demonstrated that the innate behaviour of
animals is generated by the distributed networks of neurons in the central nervous
system (Delcomyn 1980). Lobsters and crayfish served as important model
systems in the development of the command neuron, coordinating neuron and
central pattern generator (CPG) model of the organization of innate motor
systems (Kennedy & Davis 1977; Pearson 1993). The basis of this model is that the
locomotory movements of different limbs are controlled by segmental CPGs
resident in the spinal cord or ganglionic chain (Sillar et al. 1986; Selverston 1999).
The CPGs of different body segments are coordinated among themselves by
neuronal populations, termed coordinating systems, that pass information from
a governing oscillator to a governed oscillator to maintain gait (Stein 1978;
Namba & Mulloney 1999). These systems are turned on and modulated by a
descending system of command neurons (Bowerman & Larimer 1974b). The
command neurons can operate by direct synaptic activation (Larimer 2000) or
through parametric control by neuromodulators (Harris-Warrick & Marder 1991).
These central mechanisms are subject to ongoing modulation by sensory
feedback (Pearson 1995). Proprioceptive reflexes operate on segmental CPGs to
alter the amplitude or reset the phase of ongoing central programs (Stein
1978). Amplitude modulating reflexes occur when the sensory feedback
operates at the level of the motor synergies of motor neurons (figure 3b).
Amplitude modulation affects the number, size and discharge frequency of the
active motor neurons. Phase-modulating reflexes operate at the level of the
neuronal oscillator and can perturb an ongoing rhythm on a cycle-by-cycle
basis (Pinsker & Ayers 1983). Intersegmental exteroceptive reflexes modulate
responses of the whole animal in response to optical flow, gravity,
chemosensory or other external inputs (Grasso 2001).
5. Biomimetic robot controllers
To approach the behavioural capabilities of the model organism, we base the
controller on existing models of the underlying lobster neuronal circuitry which are
incomplete (Chrachri & Clarac 1989). The intact walking pattern has three phases
with a late swing bringing the limb back into contact with the substrate. Several
investigators (Sillar & Skorupski 1986; Chrachri & Clarac 1990) have developed
preparations of the isolated crayfish thoracic chain, which generated elements of the
walking motor program that differed from the walking motor programs observed
in vivo. In particular, the central rhythm consisted of a rigid alternation between an
early swing-like state and a stance state with no intervening late swing. It would
appear that the walking commands (Bowerman & Larimer 1974b) are essential to
evoke the complete rhythm that necessarily includes a late swing phase. Test of this
hypothesis requires stimulation of walking commands in a deafferented preparation, an experiment that has yet to be performed. The role of walking commands
can be inferred indirectly (Ayers & Davis 1977b). The most parsimonious model
relies on a network of four interneurons to generate the four basic synergies forming
the step cycle (figures 3k and 4a). This connectivity is consistent with the existing
connectivity maps (Chrachri & Clarac 1989). Our simulations indicate that the
Phil. Trans. R. Soc. A (2007)
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J. Ayers and J. Witting
(a)
CN
CPG
CPG CoN
CoN
CPG CoN
CPG
(b)
neuronal
oscillator
pattern
generator
flexor
CPG
motor
synergies
extensor
optical flow
(c)
CN
MI
amplitude
modulating
CPG
CoN
surge
gravity
CPG
muscles
load
sensor
phase
modulating
bump
sensor
Figure 4. Neuronal circuit modules of the lobster locomotory system. (a) Modular organization of
the segmental circuitry. CPG, central pattern generator; CoN, coordinating system; CN, command
system. The open circles on the CoNs imply inhibitory synaptic influences and the open triangles
associated with the CNs imply parametric excitatory influences. (b) Details of the segmental CPG
and associated reflex pathways. Amplitude-modulating reflexes feed back from the sensor to motor
synergies, while phase-modulating reflexes feed back to the neuronal oscillator. (c) Exteroceptive
reflexes are mediated by bilaterally symmetric inputs to contralateral CPGs or to bias segmental
CPGs as in the control of pitch and roll by antigravity synergies.
commands may select the programs for walking in different directions by altering
connectivity within a common synaptic network (Chrachri & Clarac 1989). This
connectivity is capable of explaining both the rapid changes in walking direction
seen in behaving specimens as well as diagonal walking, where both the ThC and
MC joints participate in the generation of propulsive forces (Ayers 2002a). Here,
inter-joint reflexes serve to reinforce multi-joint synergies (Ayers & Davis 1977a,
1978). A further enigma of the walking system is that ‘passive traction’ is necessary
for expression of command neuron evoked walking (Bowerman & Larimer 1974b).
A plausible explanation of this observation is that load-sensitive feedback during
the stance phase, resulting from the impediment of limb movement, overwhelmingly phase delays the subsequent swing phase, so as to damp the relatively
week single neuron evoked central oscillation (Pinsker & Ayers 1983). This control
model differs from the predominate control model for insects that relies on
independent oscillators for the three analogous joints (Buschges 2005). The insect
model relies on segmental reflexes to mediate inter-joint coordination and although
Phil. Trans. R. Soc. A (2007)
279
Biomimetic approach to underwater machines
(a)
neuronal
oscillator
elev
dep
swing
stance
command
neurons
forward
backward
pattern
generator
protractor
retractor
motor
synergies
elevation
45˚
(b)
coordinating
input
phase modulating
reflexes
neuronal
oscillator
ele
dep
swing
roll and pitch
sensors
– 45˚
retraction
0˚
45˚
ele
stance
elevator
depressor
swing
stance
directional
command logic
propulsive
load
recruiter
compensation
pattern generator
compensator
0˚
depression
modulation
dep pro ret ext flx
protraction
Figure 5. (a) Synaptic connectivity model of the lobster walking CPG. Closed circles represent
inhibitory synapses and closed triangles represent excitatory synapses. Inhibitory synapses
associated with excitatory synapses mediate presynaptic inhibition. Elev, elevator; dep, depressor;
swing and stance comprise the neuronal oscillator. Forward and backward are walking command
neurons. Protractor and retractor are motor synergies controlling the ThC joint. Elev and dep
control the CB joint through post-synaptic motor synergies. (b) Message passing hierarchy of the
leg finite-state CPG. The compensator is the recruiter for the anti-gravity synergies. Pro,
protractor; ret, retractor; ext, extensor; and flx, flexor are the synergies controlling the ThC and
MC joints, respectively.
such reflexes are present in lobsters (Ayers & Davis 1977a), they only respond to leg
movements and not positions and therefore appear to play a role in reinforcing
coupling (Ayers & Davis 1978) rather than mediating coupling which must be
flexible to permit walking in different directions.
The primary requirements for the controller are that it generates a reasonable
replica of the lobster motor programs, has the hooks necessary to mediate the
adaptational capabilities of the animal model and operates in real time using
relatively low-power microcontrollers. This control model differs from behavioural-based control models (Brooks 1986) since the same neuronal control
structures can be used in a variety of combinations and contexts to mediate
different behavioural acts. Thus, behavioural processes such as oscillation,
superposition, sequencing and choice emerge as a result of network connectivity
and dynamics of the underlying neuronal circuit modules (figure 4) as opposed to
behavioural schema (Arkin 1998).
The existing vehicle1 is based on a network organization (figure 5a), in which a
set of finite state machines (figure 5b) implement both the organizational rules of
the neuronal circuit and the observed temporal structure of the motor programs
(Ayers & Davis 1977b). The organizational components of the state machines are
the neuronal oscillator generating the stepping pattern, the pattern generator
1
A movie of the vehicle in operation is available online at: http://www.neurotechnology.neu.edu/
latmaneuvering.html.
Phil. Trans. R. Soc. A (2007)
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J. Ayers and J. Witting
modulating coordination to specify the direction of walking and the recruiters
controlling the amplitude of the motor output, and the motor synergies that
directly activate actuators (figure 5b). There are several internal state variables
that specify details of the walking program. These state variables are quantized
into small number states. For example, walking speed has four states (low,
medium, high and load), based on characteristic walking speeds of the animal
model (Scrivener 1971). Other leg state variables include walking direction,
intensity and height. Walking in opposite directions (i.e. forward versus
backward) is mutually exclusive, but combinations of forward or backward
and lateral walking can generate hybrid behaviour such as diagonal walking. The
walking speed state acts on the neuronal oscillator and propulsive recruiter
specific to the direction of walking. The walking direction acts at the pattern
generator level and intensity and height act at the level of the antigravity and
propulsive recruiters. Changes in pitch and roll are mediated by biasing the
height state for the different segments or sides. Several other concatenated state
machines control the posture of the claw- and abdomen-like hydrodynamic
control surfaces and the antennae.
6. Myomorphic actuators
The most critical components in achieving animal-like behaviour in biomimetic
robots are the actuators. The most common actuators used in contemporary robots
are gear head motors. Motors mounted on moving appendages generate high
inertial masses and require relatively complex electronic interfaces. The
neuromuscular systems of animals use inherently different forms of control.
Animals make joint movements by alternating contractions of antagonist muscles
and regulate joint stiffness by coactivation of antagonist muscles. Contractions are
graded by sequential recruitment of motor neurons of the order of their size which is
proportional to their efferent effect (Davis 1971; Stuart & Enoka 1985).
Several forms of artificial muscle exist (Ayers et al. 2002). We have adapted
nitinol shape memory alloy (SMA) wires as artificial muscle for underwater
operation (Witting & Safak 2002). The individual actuators (figure 6a) consist of
a segment of acid-etched nitinol wire that have current source leads attached
with stainless steel crimp connectors around a loop of nitinol at the end. For
thermal and electric insulation, the body of the nitinol wire is covered with a
sheath of etched Teflon tubing. Kevlar connecting ‘tendons’ are threaded
through the loop and screw adjusters are cemented to the crimp connectors. The
exposed nitinol is then sealed with Aquaseal.
The shape memory effect is used to generate a contraction/relaxation cycle.
During manufacture, individual modules are annealed at high temperature
(greater than 6008C) to convert all of the nitinol to the dense austenite state
(figure 6b). When cooled, the modules can be strained into the less dense
martensite state with length changes up to 8% (Duering et al. 1990). Resistively
heating the wire with applied electrical current up to 708C proportionally
converts the nitinol from martensite to austenite with an associated contraction
(figure 6c). The modules are then trained by a series of active contractions
against a graduated mass until they achieve a highly reproducible stroke of
approximately 4.8% over their austenite length.
Phil. Trans. R. Soc. A (2007)
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Biomimetic approach to underwater machines
(b)
(c)
temperature
(a)
displacement (mm)
5
deformation
crimp
connector
(d) movement
4
70%
3
2
40%
1
33%
0
austenite
100
200
300
time (ms)
400
500
martensite
teflon
coated
nitinol
martensite austenite
(e) stiffening
martensite austenite
current
lead
Kevlar
tendon
martensite austenite
Figure 6. Nitinol actuators. (a) Individual SMA artificial muscle module; (b) shape memory effect
and (c) relationship of contraction dynamics to current duty cycle. The traces represent the
position of a 1 kg weight lifted by a muscle module. An upward displacement of the upper trace
represents shortening of the muscle. The percentages indicate the proportion of time (duty cycle)
that the electrical current is applied to the muscle. (d) Movement control with nitinol actuators.
(e) Stiffness control with nitinol actuators.
In the robot, nitinol actuators are employed in antagonistic pairs. Seawater
surrounding the modules (figure 7a) ensures rapid conversion from austenite to
martensite. In operation, the active contraction of one module stretches the
antagonist and vice versa (figure 6d). Active contraction of both modules
together compresses a series spring and stiffens the joint (figure 6e).
When operated under water, SMAs present several advantages. First, they are
extremely force dense, producing stresses of the order of 10 N. Second, when
cooled by seawater to speed the conversion from austenite to martensite, they
have an acceptable dynamic range and antagonist pairs can make repetitive
contractions at over 2 Hz. Third, they can be activated by trains of current
pulses to grade force in an analogous fashion to the way that motor neurons
activate animal muscle (figure 6c).
Crustaceans typically have a small number of motor neurons per muscle
(Atwood 1973). They grade muscular force by means of size principle of
recruitment, where motor units are recruited in the order of size to achieve
stronger contractions. In the lobster robot, we use quantized pulse width duty
cycle modulation to realize size ordered recruitment. The portion of nitinol that
converts from martensite to austenite is proportional to the duty cycle and
Phil. Trans. R. Soc. A (2007)
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J. Ayers and J. Witting
(a)
adjustment
screws
(b)
(c)
nitinol
muscle
modules
capstan
CB joint
elevation
compression
spring
depression
MC joint
adjustment
screws
KevlarTM
tendons
ThC joint
protraction
retraction
nitinol
muscle
modules
flexion
extension
adjustment
screws
Figure 7. Functional relations of the leg actuators. (a) Three degrees of freedom leg module. (b)
Capstan arrangement for control of the basalar (ThC) joint of the leg. (c) Muscle arrangements for
control of the CB and MC joints of the leg.
increases both the amplitude and velocity of the contraction (figure 6c). For each
muscle, we specify a low, medium and high recruitment level. Each level
corresponds to a pulse-width duty cycle specified in a lookup table. When the
intensity of behaviour is changed, the duty cycle associated with each
recruitment level is changed. In practice, the duty cycle necessary to initiate
contractions is higher than that necessary to maintain contractions, so the trains
which activate a muscle consist of a brief (150 ms) segment at high duty cycle
followed by the remainder of the train at the duty cycle appropriate to the speed
of walking.
The muscle modules actuate a three degrees of freedom walking leg (figure 7a).
The leg is bolted to the hull by a support that houses a vertical capstan
(figure 7b) forming a robotic analogue of the lobster ThC joint. The capstan is
actuated by a pair of muscle modules housed in adjacent vertical tubes
(figure 7a). Screw mounts and a pair of springs in series with the muscle modules
Phil. Trans. R. Soc. A (2007)
Biomimetic approach to underwater machines
(a)
(d)
283
(e)
(f)
(b)
(c)
( g)
Figure 8. Components of the ambulatory vehicle. (a) External laptop for interactive programming
and debugging. (b) Wireless modem for communications with the vehicle. (c) Embedded controller
housing the controller. (d) Pressure hull, mother and daughter boards. (e) Leg current driver
board. (f ) Sensor and motor interface board. (g) Sonar communications board.
allow adjustment. In the absence of proprioception, the springs allow the
modules to contract even when joint movements are physically constrained, thus
preventing the overstressing of the nitinol (which can eliminate the shape
memory effect; Duering et al. 1990). The CB joint pivots in the vertical capstan
and is actuated by a pair of modules that span the segment between the CB and
MC joints. A pair of modules that span the segment between the MC joint and
the tip of the leg, in turn, actuates the MC joint. Owing to the length of the leg,
the forces at the tip of the leg generated by the ThC joint are typically of the
order of 0.8 N. During load compensation, while walking into flow, the angle of
the MC joint can be reduced to shorten the lever and provide enhanced
translational force.
7. Electronics
The current lobster robot is based on a backplane modular board arrangement
(figure 5). The motherboard houses the serial bus, voltage regulators for the lowcurrent electronics, the compass and inclinometers. During debugging, the
vehicle can be operated by an external laptop (figure 5a) through a RF modem
connected to a float above the vehicle in a test pool (figure 5b).
For general use, the robot can be controlled by an embedded microcontroller
(figure 8c). The controller application has a laptop version and an embedded
microcontroller version. The embedded version is a subset of the laptop version
with all graphical user interface elements removed and linked to a runtime
Phil. Trans. R. Soc. A (2007)
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J. Ayers and J. Witting
library supporting the microcontroller (3V Motorola 68020, Persistor Instruments, Inc., Bourne, MA). Both the laptop and the microcontroller communicate
to the other boards through a serial bus.
Four leg-driver boards control the nitinol actuators of the walking legs. Each
board has a PIC16 microcontroller (Microchip Technology Inc., Chandler,
Arizona) that receives commands from the laptop or microcontroller and
provides pulse-width modulated (PWM) current trains to activate the muscles of
the two legs of one segment. The commands sent to the driver boards include
control bytes specifying a leg, a muscle and either one of the three recruitment
levels or stop. The three recruitment levels (low, medium and high) specify three
discrete PWM duty cycles, which correspond to small, medium and large motor
neurons in the crustacean neuromuscular recruitment scheme (Davis 1971). The
duty cycles associated with each recruitment level can be programmatically
changed on the fly to alter the intensity of the motor output. For example, the
levels of low, 10%; med, 30% and high, 70% might be increased to low, 20%; med,
40% and high, 80%. Thus, the leg-driver board receives a byte to turn on a
muscle at a particular duty cycle level and it continues applying a current pulse
train until it receives a stop byte. During a pulse train, the duty cycle can also be
switched from a high (pre-heating) duty cycle to one more appropriate for the
direction of walking.
A multifunction board controls the DC motors that control the posture of the
claw- and abdomen-like hydrodynamic control surfaces and the antennae. This
board also houses sensor electronics for the antennae and bump sensors. Finally,
a sonar communication board allows the robot to receive 64 byte commands
through a sonar transducer. The sonar commands consist of a 30 kHz tone
followed by a 40 kHz tone. The interval between these tones is discretized by
15 ms into eight intervals. The sonar board has a tuned filter for the 30 kHz tone
and upon reception starts a timer that determines the latency to a corresponding
40 kHz tone from another tuned filter. The tone pairs are sent in two pairs giving
rise to 64 possible command codes.
The robot has an external power supply that consists of four parallel sets of
five NiMH HR210AA batteries (Panasonic Industrial Company, Elgin, IL) in
series. Each set of batteries powers a pair of legs to reduce the effect of the lowresistive load of large numbers of active nitinol elements. The current required by
the motherboard and the low-current subsystems of the other leg driver boards
are powered through one of these battery legs.
8. Reactive control
The behaviour of the robot is presently specified by a set of nine internal state
variables having two to five states. These state variables specify the action of the
legs, the posture of the claw- and tail-like hydrodynamic control surfaces and the
action of the antennae. These state variables are derived from the observation
that a population of command neurons, each of which specifies movement or the
position of the appendages (figure 9a), controls the behaviour of crayfish
(Bowerman 1974a,b). For the purposes of control of the robot, we use a reduced
number of states (figure 9b). The complete set of internal state variables
currently implemented includes the following.
Phil. Trans. R. Soc. A (2007)
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Biomimetic approach to underwater machines
(c)
(a)
(b)
abpitch
ext
ele
norm
dep
flex
task group states
walking legs
left
right yaw
hard left
for
for
easy left
back
back
norm
lead
lead
easy right
trail
trail
hard right
roll
pitch
chpitch
ltup
rosup
chup
rlevel
plevel
clevel
rtup
rosdn
chdn
(e)
(d )
time
command stack
action 1
action 2
action 3
action 4
action component
command
intensity
the time
commands
forward walk
backward walk
leading walk
trailing walk
pitch up
pitch down
elevate abdomen
depress abdomen
time
antyaw
prot
nor
lat
ret
abpitch
ele
ext
norm
dep
flex
height
high
hnor
low
speed
fast
med
slow
stop
next
I forward
I lateral
I backward
I trailing
r forward
r lateral
r backward
r trailing
yaw
speed
height
roll
I antenna
r antenna
I chelae
r chelae
chelae pitch
pitch
abdomen
uropods
Figure 9. Finite state control of behaviour. (a) Examples of abdominal postures evoked by postural
commands in the crayfish (Bowerman & Larimer 1974a). (b) Quantized abdominal postural states
used in the control of the robot abdomen. (c) Radio button panel used in the reverse animation of
movies of behaving lobsters. (d) Bar chart indicating behavioural state changes controlling
rheotaxic behaviour in the robot. The upper part of this panel indicates the state of walking
directional commands, while the green part indicates the other state variables as labelled on the
right. This figure illustrates the command state transitions underlying rheotaxic behaviour pushed
on the stack at two different temporal compressions.
—Thorax pitch. Rostrum up, level and rostrum down.
—Thorax roll. Left up, level and right up.
—Thorax height. High, normal and low.
—Thorax yaw. Hard left, easy left, straight, easy right and hard right.
—Walking direction. Standing, forward, backward and lateral leading lateral
trailing.
—Walking speed. Slow, medium, fast, load and stop.
—Chelae pitch. Up, normal, down and low.
—Antennae yaw. Protracted, normal, lateral and retracted.
—Abdominal pitch. Extended, elevated, normal, depressed and flexed.
The behaviour controller for these robots is organized at two levels. At the
lowest level, sensors modulate internal state variables directly to generate
adaptive responses. At the highest level, the controller is organized around
behavioural libraries and a sequencer. The sequencer maintains the state of the
Phil. Trans. R. Soc. A (2007)
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J. Ayers and J. Witting
vehicle by switching between different command states in a temporal sequence
specified by both orientation and magnitude of sensor input and the response
patterns specified by the behavioural libraries.
To generate behaviour libraries, movies of behaving lobsters are subjected to
reverse animation (Ayers 2000a). A program allows an investigator to step
through a movie on a frame-by-frame basis and specify the state of the animal
with a panel of radio buttons (figure 9c). The state of each task group of the
animal is approximated as one of the set of states (figure 9b). The result of this
analysis is a table where each column corresponds to different internal states and
each row specifies the states in one frame of the movie. Thus, this table defines a
behaviour as a sequence of command state transitions (Ayers 2000a).
Ongoing behaviour is comprised a sequence of actions, maintained on an event
stack (figure 9d). Each of these action components, in turn consist of a behavioural
command and associated intensity and timing parameters. The action components
are pushed to the stack, of the order of time, by releaser objects. These releaser
objects are triggered by specific messages from sensor inputs on the robot. The
primary advantage of this arrangement is that compatible action patterns can be
superimposed and the layers corresponding to different task groups can be managed
in parallel as occurs in lobster command systems (Davis & Kennedy 1972). When
the behaviour is released, a releaser (i) saves the current state of the system, (ii)
queues the times of parametric modulation events, and (iii) queues the times of
commands and transitions, and then returns to the event loop to pop transitions off
the queue. A releaser can thus activate multiple action components in parallel,
orchestrating a hybrid behavioural act.
An example of such a behavioural sequence is rheotaxic behaviour or orientation
and compensation for flow or surge (figure 9e). During response to surge sensed by
flow sensors, the controller first lowers the body, depresses the claws and orients
into the current (figure 9e). It then reduces the depression in anterior segments that
will pitch the hull forward and elevates the tail. A set of such behavioural sequences
constitutes the behavioural set of the robot. Each of the behaviours is associated
with a releaser or specific sensory input that triggers the behaviour (see below).
In many cases, behavioural acts, which operate in parallel, superimpose upon
each other at the level of the effectors (Frye & Dickinson 2004). In other cases,
especially when presented with the releasers for two incompatible behavioural
acts, the animal typically chooses to perform one act over the other and one
behavioural act suppresses the other. In our controller, suppressor objects
triggered by releasers mediate behavioural choice. These objects embody lateral
inhibitory connections between commands and are the locus of implementation
of behavioural choice (Kovac & Davis 1980; Edwards 1991). Suppressor objects
for a particular behavioural act maintain a lookup table of action components
that the act suppresses and then clear incompatible action components from the
event stack as the behaviour is released.
9. Tactile navigation
Lobsters must adapt to changing flow and surge. During locomotion, lobsters
operate in still water, relatively unidirectional tidal flows or wave surge
(Martinez 2001). Surging water changes direction with periods of the order of
Phil. Trans. R. Soc. A (2007)
Biomimetic approach to underwater machines
velocity (m s–1)
seaward
shoreward
(a)
0.9
wave-swept site
accelerating
decelerating
287
(c)
antennae forward: lateral surge
0.6
0.3
0
– 0.3
– 0.6
20
(b)
40
60
time (s)
80
100
(d)
antennae lateral: axial surge
high medial
medium medial
low medial
neutral
low lateral
medium lateral
high lateral
buckle
Figure 10. Sensors for adaptation to surge. (a) Characteristics of the wave surge seen in wave swept
environments (Martinez 2001). (b) Coding in the antennular sensor. The analogue signal from a
strain gauge is quantized into seven degrees of bending, each of which corresponds to a bit in the
antennal byte. The 8-bit codes for buckling, which is detected by a left–right bend within 150 ms.
Buckling is caused by head on collisions of the antenna. A motor drive moves the antenna into
different positions. When held forward (c), the antennae are responsive to lateral surge. When held
laterally (d) they are responsive to axial surge along the body.
tens of seconds (figure 10a). Surging water creates problems for stability in the
pitch and roll planes as well as in the maintenance of traction with the substrate.
Since lobsters are generally active in the dark, they have to rely on flow and
contact sensors to detect environmental perturbations (Wilkens et al. 1996), a
process we call tactile navigation.
The lobster robot uses antennal sensors that embody labelled line coding to
detect flow and contact (Bullock et al. 1977). Each antenna is represented
internally by a byte, each bit of which codes for different degrees of antennal
bending or buckling (figure 10b). During the operation, the sensor boards are
polled for input and the board returns 2 bytes representing the state of the two
antennae. We realize selective attention by varying the polling rate for different
sensors. Therefore, as inputs from a particular modality become critical, the
sensor interface focuses on that modality. How the antennae are used to mediate
rheotaxis is described below under electronic neurons (ENs).
Phil. Trans. R. Soc. A (2007)
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J. Ayers and J. Witting
(a)
accelerometer analogue output
rectified high-pass filtered
threshold
low-pass filtered
bump signal
(b)
walk over
circumnavigate
climb over
Figure 11. Bump sensors for tactile navigation. (a) Signal processing of the bump releaser. An
analogue accelerometer is mounted in each cheliped. The high-pass filtered bump signal is rectified,
and then low-pass filtered and thresholded to generate a bump signal. (b) Decision making using
collision sensors.
Studies using a lobster-mounted camera allow us to observe interactions of
moving lobsters with environmental obstacles. Lobsters often collide with rocks
with their antennae and claws. If a collision occurs with just one claw, the
response is to make a yawing turn away from the collision. This implies that the
lobsters are using their claws as bump sensors. The chelipeds are innervated by a
variety of joint receptors that would be activated by collision (Laverack 1976).
We use an analogue accelerometer as a basis for a bump detector (figure 11a).
The combination of the bump sensors and the antennal-based collision sense
gives the vehicle basic decision-making capability for tactile navigation. If the
vehicle collides with an object with its antennae, it will be necessary to
circumnavigate the obstacle. If it hits an obstacle with its claws, but not by its
antennae, it can probably climb over the obstacle. If it does not hit the obstacle
with either, it can probably just walk over the obstacle.
10. Electronic nervous systems
For a deterministic program to control adaptive robotic behaviour, it must be
preprogrammed to anticipate all environmental contingencies. In contrast, the
innate behaviour of animals emerges from the properties and connectivity of intrinsic
neuronal networks (Delcomyn 1980). For robots to achieve the adaptive capabilities
of animals, they need to be extended to neuronal control schemes. In particular,
neuronal network-based controllers allow flexibility and robustness unavailable to
deterministic programs (Abarbanel & Rabinovich 2001). We have been evaluating
two different types of neuronal-based controllers for application to the lobster
robot, ENs (Pinto et al. 2000) and discrete time map-based neurons (Rulkov 2002).
Phil. Trans. R. Soc. A (2007)
Biomimetic approach to underwater machines
289
elevator
depressor
stance
swing
protractor
retractor
forward command
backward command
Figure 12. Modulation of the activity of a walking CPG composed of electronic neurons. The upper
four traces indicate the membrane voltage of the four neurons of the neuronal oscillator indicated in
figure 4a. The fifth and sixth traces (protractor and retractor) are the waveforms of the motor neurons
of the ThC joint. The lower two traces are command neurons for forward and backward walking.
UCSD ENs are based on a dynamical model of lobster neurons formalized by the
Hindmarsh & Rose (1984) equations. The neurons are analogue computers that solve
the equations in real time. They can be configured to emulate different types of
neurons by adjusting trim pots that specify coefficients of the equations.
UCSD electronic synapses emulate chemical synapses and can be configured to
different postsynaptic reversal potentials and time constants. We have
instantiated a circuit (figure 4a) with UCSD ENs and synapses to generate
walking motor patterns (figure 12). This involved developing a presynaptic
inhibitory circuit. The application of a bias current to the neuronal oscillator
activated all neurons except the commands. When the commands were
activated, the motor programs for forward and backward walking were selected.
Using a simple comparator/power FET interface, we were able to interface this
EN CPG to one of the lobster robot’s legs. A movie of the EN CPG circuit
causing the leg to walk in different directions as in figure 12 is available online2.
EN CPGs can be perturbed and entrained by synaptic input to mediate gait
coordination as well as phase- and amplitude-modulating reflexes.
To be useful for robotic control, component-based ENs need to be reduced to
analogue VLSI and we have been successful in getting subthreshold VLSI simulations
of UCSD ENs to generate excellent replicas of lobster neuron behaviour (Lee et al.
2004). These studies have involved development of ENs, electronic chemical
synapses, presynaptic inhibition and the simulation of complex motor programs.
2
http://www.neurotechnology.neu.edu/EN_CPGWalking.html.
Phil. Trans. R. Soc. A (2007)
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J. Ayers and J. Witting
The generation of walking motor programs is only one aspect of the control of
the robot. A more critical aspect of control is at the level of exteroceptive
reflexes. The exteroceptive reflexes act on multiple task groups to modulate the
behaviour of the whole organism. In invertebrates, the brain is the locus of
integration of exteroceptive inputs to activate descending commands (Sandeman
et al. 1992). At the level of innate behaviour, the brain mediates layered
exteroceptive sensory-motor reflexes to control processes such as taxes, tactile
navigation, righting and rheotaxis (Kennedy & Davis 1977).
Instantiation of the circuitry for the adaptive control of complex exteroceptive
reflexes in analogue VLSI ENs is presently unattractive for two reasons. First,
the circuits require large numbers of neurons and large expensive dies. Second,
these reflexes are going to require a lot of annealing; hence there is a need for
interactive programming. Discrete time map-based neurons provide an attractive
alternative for implementation of the complex circuitry necessary for the control
of adaptive behaviour by a robotic brain (Rulkov 2002). The map-based neurons
are available for rapid prototyping in the LABVIEW (National Instruments,
Austin, TX) environment3.
We have used map-based neurons and synapses to simulate the antennal
mediation of flow reflexes in the lobster robot (Ayers & Rulkov 2006). Rheotaxic
behaviour in lobsters has two components. The first occurs in response to lateral
surge and involves turning into the flow by walking forward on one side and
backward on the upstream side. With the antennae held forward (figure 10c),
such lateral surge from the left would cause medial bending of the upstream
antenna and lateral bending of the downstream antenna. The resulting input is
then processed by a neuronal network (figure 13a) to mediate rheotaxis. A pair of
bilateral rheotaxic interneurons receives inputs from the highest threshold
medial bend element of the same side and the highest threshold lateral bend
element of the contralateral side. The output of this neuron is to activate the
backward command on the same side and the forward command on the opposite
side. This reflex response is demonstrated in LABVIEW simulation in figure 13b.
The second component of rheotaxis is gradual yawing into the flow in result
from asymmetric flow along the long body axis. The rheotaxic interneurons in
figure 13a would also command the antennae to move to the lateral position
(not shown). As the vehicle orients into the flow, the upstream antenna would
bend more than the downstream antenna. The medial lower threshold afferents
project to a surge interneuron on the same side, while the lateral afferents
project to a surge interneuron on the opposite side. The surge interneurons
activate the forward command on both sides. In asymmetric surging flow along
the long body axis, this arrangement would cause the vehicle to oppose and
yaw into the flow when coming from front to rear, and run with and yaw into
the flow when coming from rear to front. This reflex response is demonstrated
in figure 13c.
The combination of EN CPGs and discrete time map-based neuron brains
provides a feasible neuronal network-based controller for biomimetic robots such
as the lobster robot (figure 1). The brain could be implemented on a small
microcontroller and by the use of D/A converters to convert the command
signals (figure 13b,c) to analogue voltages that modulate the EN CPGs (figure 12)
3
http://inls.ucsd.edu/wrulkov/demo/neuron/map/ndemo.html.
Phil. Trans. R. Soc. A (2007)
291
Biomimetic approach to underwater machines
(a)
left antennae sensors
lateral
medial
H M L
L M H
right antennae sensors
medial
lateral
H M L
L M H
left
surge
right
surge
rheotaxic
interneurons
surge
interneurons
(b)
F
B
(c)
B
F
surge
surge
high medial bend
high medial bend
high lateral bend
high lateral bend
rheotaxic interneuron
rheotaxic interneuron
surge interneuron
surge interneuron
walking command left
forward command
backward command
walking command right
walking
commands
walking command left
forward command
walking command right
backward command
Figure 13. Simulation of rheotaxic behaviour mediated by antennae using discrete time map-based
neurons. (a) The neuronal circuit. Range-fractionating sensory afferents project to rheotaxic and surge
interneurons. The highest threshold bending afferents project to the rheotaxic interneurons. The low
and medium threshold bending afferents project to the surge interneurons. (b) Rheotaxic (turning)
response to lateral surge when the antennae are deployed forward as in figure 10c. The two panels
represent the activity on the neurons in figure 13a when the surge (top two panels) oscillates from left to
right to left with a long period. (c) Yawing responses to off centre axial surge (from the right forward
quadrant) when the antennae are deployed laterally as in figure 10d. Note the difference in amplitude of
the antennal movements (downstream antenna bends less) and the resulting asymmetry in output.
and an integrated CNS could be realized. The primary advantage of EN-based
controllers is that they can incorporate chaotic organizational principles. We
hold that the use of chaos in controllers will allow the adaptability of robots
unfeasible with deterministic programming (Rabinovich & Abarbanel 1998). As
both invertebrate and vertebrate locomotor systems are organized by common
principles (Stein 1978; Pearson 1993), it would appear that this neurotechnology
might have applications both to robotics and neuroprosthetics (Selverston
et al. 2004).
Phil. Trans. R. Soc. A (2007)
292
J. Ayers and J. Witting
This study is supported by DARPA/ONR grant N00014-98-1-038, ONR grant N000014-04-1-286.
We thank Allen Selverston and Scott Currie for their comments on the manuscript.
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