Hybrid Routing Algorithms for Navigation
Control of a Semi-Autonomous Robotic
Platform
Faculty Advisors:
Dr. Charles Liu
Dr. Helen Boussalis
3/18/2016
NASA Grant URC NCC NNX08BA44A
Student Assistants:
Airs Lin
Garth Herman
Aleksander Milshteyn
Evan Tsai
1
Presentation Overview
• Design of a Semi-Autonomous Robotic Platform
– Background
– Specification of a Mobile, Networked, and Intelligent Platform
• Overview of System Architecture
– Kinematics, Networking, Intelligence, and Data Acquisition Interface
– Hardware and PSoC Firmware Implementation
• Hybrid Routing Overview
– Host PC Software Implementation of Level Maps
– D* (Dynamic A*) Routing Algorithm
• Conclusion
3/18/2016
NASA Grant URC NCC NNX08BA44A
2
Project Background
• International Lunar Networking (ILN) is a proposed network of
robotic landing stations. Each station will monitor geological
activity, while robots investigate the Lunar environment.
• Mars Science Laboratory/Curiosity, landed on August 6th, 2012 and
is an automated Mars rover vehicle exploring the Martian
surface. Rovers present advantages over stationary nodes including
directed observation and autonomous orientation to sunlight during
winter.
• The proposed design envisions a
network of multiple Semi-Autonomous
Multi-Mission mobile platforms to
provide real-time data acquisition and
communication relays.
3/18/2016
NASA Grant URC NCC NNX08BA44A
3
Overview of System Architecture
Control
Center
• Environmental mapping
• Environmental data collection
• Multimedia acquisition
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NASA Grant URC NCC NNX08BA44A
4
3/18/2016
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5
Specification of a Mobile,
Networked, Intelligent Platform
•
Sensors for kinematics and obstacle detection:
– Sonar, Infrared, Accelerometer, Magnetometer, Gyroscope, Motor Shaft Encoders
•
On-board wireless communications:
– Wixel
– Nordic RF24
•
Sensors for Environmental/Atmospheric Conditions:
– Radiation, Temperature, Gas, Acoustic, etc
•
Sensors for 3D environmental Observation and Mapping:
– Microsoft Kinect
– Headtracking Virtual Reality Stereoscopic Camera
•
Embedded System:
– A full but miniature-size computer system to do heavy-general computing including a
PC and Programmable System on a Chip.
3/18/2016
NASA Grant URC NCC NNX08BA44A
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Prototype Architecture
Implementation
Distance Sensors
Programmable
System
On a Chip (PSoC)
Kinematic Sensors
Motor Controllers
This mobile unit shall allow semi-autonomous and
remote-controlled navigation.
3/18/2016
NASA Grant URC NCC NNX08BA44A
7
List of Sensors
Ultrasonic
Infrared
Accelerometer/Magnetometer
Gyroscope
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NASA Grant URC NCC NNX08BA44A
Encoder
8
Kinect Hardware
• 4 Microphones
• 1 IR Transmitter
• 1 visual-spectrum camera
• 1 IR camera for
depth detection
• 1 Motor (pitch-only)
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What is a Programmable
System-on-Chip (PSoC)?
•
•
•
•
32-bit ARM Cortex-M3 CPU @ 67MHz
Digital, Analog, and Communication Interfaces
• ADC/DAC, Digital Filter, PWM, Timers, I2C, SPI, UART, USB, DMA,
Interrupt, basic logic gates…
Voltage levels of I/O: 5V, 3.3V, 1.8V, and user defined
All Pins can be configured as Digital, Analog (Input or Output)
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NASA Grant URC NCC NNX08BA44A
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Top-level
UDB Routing Diagram
UDBs are utilized
when implementing
multiple digital
components with
different internal
settings.
• UARTs
• PWMs
• Separate SPI /
I2C Masters
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NASA Grant URC NCC NNX08BA44A
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PSoC Components
Motors
Communications / Digital Sensors
Motor Encoders
Analog Sensors
3/18/2016
NASA Grant URC NCC NNX08BA44A
Servo Turret Control
12
PSoC Raw Data Output
Raw Data Values: ~85 updates/sec over cable Serial UART @ 115200bps
Servo
Position
3/18/2016
Infrared
Gyro
X-Axis
Gyro
Y-Axis
NASA Grant URC NCC NNX08BA44A
Gyro
Z-Axis
Sonar
(analog)
13
PSoC Converted Data Output
3/18/2016
NASA Grant URC NCC NNX08BA44A
14
Overview of System Architecture
Control
Center
Visually interactive, real-time maps.
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Hybrid Routing Overview
L1: 1 km x 1km
L2
Three levels of mapping are
used:
• A L1 map is composed of
100 L2 (10000 sq. meter)
L3: 10 m x 10 m
maps.
• An L2 map is composed of
100 L3 (100 sq. meter)
maps.
• An L3 map is composed of
100 (1 sq. meter) blocks.
L2
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Great-Circle Distance
•
a are geographical latitude and longitude of two points.
• Radius of the Earth, r = 6,371,009 m
•
is the central angle between these two points
• Law of cosines:
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Static Mapping on a L1 Map
• UAV captures an image and transmits it to the operator.
• Operator plots the observed obstacle placement.
• Obstacle data is wirelessly transmitted to the Host PC.
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Static Mapping on a L1 Map
• This application will provide communication with the operating station and the robot platform.
• It accumulates sensor values, maps the environment, and provides algorithmic control.
3/18/2016
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Obstacle Information Acquisition and
Sensor Ranges on the L2 block
(L3 Map)
Infrared
Microsoft Kinect
Ultrasonic Sensor
Scale: Each square = 1 sq. m.
• Each sensor will be utilized to inform the controlling PC
of obstacles at specific distances, based on the unit
capabilities (sensor range, amount of sensor units)
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Integration of 3rd party Kinect
Module
3/18/2016
02/22/13
NASA Grant URC NCC NNX08BA44A
22
What is an A * Algorithm?
• A* is based on an approximate cell-decomposition approach, that
fills grid-based world model as more information about structural
surrounding environment is gathered.
• It is an extension of Edsger Dijkstra's 1959 algorithm. Three
computer scientists in the field of artificial intelligence - Peter Hart,
Nils Nilsson and Bertram Raphael first described the algorithm in
1968
• Dijkstra's algorithm, is a graph search algorithm that solves the
single-source shortest path problem for a graph with
nonnegative edge path costs, producing a shortest path tree. This
algorithm is often used in routing and as a subroutine in other graph
algorithms.
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Dijkstras Algorithm
12
0+2
F
A
3
2
7
2
0
0+5
5
S
4
B
5
T
D
1
1
3
4
7
4
C
E
0+4
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Dijkstras Algorithm
12+2
12
2
F
A
3
2
2
7
0
2+2
5
0+5
S
2+7
4
B
5
T
D
1
1
3
4
7
4
C
E
0+4
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Dijkstras Algorithm
12+2
12
2
F
A
3
2
2
7
0
4
5
0+5
S
2+7
4
B
5
T
D
4+4
1
1
3
4
4+1
0+4
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C
4+3
7
4
E
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Dijkstras Algorithm
12+2
12
2
F
A
3
2
2
7
0
4
5
S
2+7
4
B
5
T
D
4+4
1
1
3
4
4+3
7
4
4
C
E
4+4
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Dijkstras Algorithm
12+2
12
2
F
A
3
2
2
7
0
4
5
S
2+7
4
B
D
5
T
7+1
4+4
7+7
1
1
3
4
7
7
4
4
C
E
4+4
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Dijkstras Algorithm
12+2
12
2
F
A
3
2
2
7
0
4
5
S
4
B
8+5
2+7
D
5
T
8
8
8+7
1
1
3
4
7
7
4
4
C
E
4+4
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Dijkstras Algorithm
Unsolved
Node
12
2
F
A
3
2
2
7
0
4
13
5
S
4
B
5
D
T
8
8
Shortest Routes:
1
1
3
4
Incomplete
Route
4
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7
4
C
1)S-A-B-D-T
2)S-A-B-E-D-T
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A* Variables
• G
– The movement cost to move from the starting point to a given
square cell on the grid, following the path generated to get there.
• H
– The estimated movement cost (heuristic) to move from that given
square on the grid to the final destination. The H scores are
calculated by estimating the Manhattan distance to the target square,
moving only horizontally and vertically and ignoring the obstacles in
between.
• F=G+H
– The path is generated by repeatedly going through the open square cell
list and selecting the cell with the lowest F score.
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A* Determining Adjacent Cell
Costs
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
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80
10
70
S
80
10
70
74
14
O
60
60
10
O
50
74
14
60
T
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A* Determining Adjacent Cell
Costs
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
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80
10
70
S
80
10
70
74
14
O
60
60
10
O
50
74
14
60
T
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A* Determining Adjacent Cell
Costs
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
3/18/2016
80
10
70
S
80
10
70
74
20
O
60
60
10
O
50
74
20
60
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NASA Grant URC NCC NNX08BA44A
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A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
94
10
80
10
70
94
14
80
94
10
50
60
10
60
10
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20 60
74
24
60
10
50
60
20
40
70
80
20
60
74
24
50
50
60
10
50
60
10
70
S
O
50
O
T
O
50
60
10
50
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A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
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80
10
70
S
80
10
70
94
14
O
60
74
24 50
60
10
O
50
60
20
40
94
14
60
74
24
50
T
O
NASA Grant URC NCC NNX08BA44A
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A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
3/18/2016
80
10
70
60
74
24
50
60
20
40
74
20 60
74
24
50
108
38 70
94
34 60
88
38
114
34
114
44
108
48 60
60
10
S
80
10
74
14
70
80
O
50
O
T
O
70
50
NASA Grant URC NCC NNX08BA44A
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A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
80
10
70
S
80
10
70
74
14
60
74
24
60
10
50
60
20
40
74
14
60
74
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70
94
34
80
114
44
94
24
114
34
3/18/2016
O
50
O
T
O
60
88
38
70
108
48 60
50
NASA Grant URC NCC NNX08BA44A
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A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
80
10
60
74
24
60
10
50
60
20
40
70
74
14
60
74
24
50
80
94
24
70
88
34
80
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52 70
70
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80
10
100
20
74
14
114
34
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O
50
O
T
O
60
88
38
108
48
82
52
30
50
88
48
40
60
102
52 50
NASA Grant URC NCC NNX08BA44A
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A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
80
10
60
74
24
60
10
50
60
20
40
70
74
14
60
74
24
50
80
94
24
70
88
34
80
122
52 70
70
S
80
10
100
20
74
14
114
34
3/18/2016
O
50
O
T
O
60
88
38
108
48
82
52
30
50
88
48
40
60
102
52 50
NASA Grant URC NCC NNX08BA44A
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A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
80
10
60
74
24
60
10
50
60
20
40
70
74
14
60
74
24
50
80
94
24
70
88
34
80
122
52 70
70
S
80
10
100
20
74
14
114
34
3/18/2016
O
50
O
O
60
88
38
108
48
82
62
82
52
20
76
66
10
30
82
62
20
40
94
64
30
50
88
48
60
102
52 50
NASA Grant URC NCC NNX08BA44A
T
41
A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
80
10
60
74
24
60
10
50
60
20
40
70
74
14
60
74
24
50
80
94
24
70
88
34
80
122
52 70
70
S
80
10
100
20
74
14
114
34
3/18/2016
O
50
O
O
60
88
38
108
48
82
62
82
52
20
76
66
10
30
82
62
20
40
94
64
30
50
88
48
60
102
52 50
NASA Grant URC NCC NNX08BA44A
T
42
A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
94
14
80
80
10
60
74
24
60
10
50
60
20
40
70
74
14
60
74
24
50
80
94
24
70
88
34
80
122
52 70
70
S
80
10
100
20
74
14
114
34
3/18/2016
O
50
O
O
60
88
38
108
48
82
62
82
52
20
76
66
10
30
82
62
20
40
94
64
30
50
88
48
60
102
52 50
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A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
80
10
70
S
74
14
60
10
60
74
24
O
50
50
60
20
40
O
110
80 30
96
76
82
62
76
66
20
20
90
80
10
10
76
76
0
T
94
14
3/18/2016
80
80
10
100
20
70
74
14
60
74
24
80
O
50
94
24
70
88
34
60
114
34
80
122
52 70
82
52
30
82
62
20
40
94
64
30
88
38
50
88
48
108
48
60
102
52 50
NASA Grant URC NCC NNX08BA44A
90
80
10
44
A* Calculating and Selecting
Lowest Cost Cell
Red = G, Blue = H, White = F
94
14
80
80
10
70
80
10
70
S
74
14
60
10
60
74
24
O
50
50
60
20
40
O
110
80 30
96
76
82
62
76
66
20
20
90
80
10
10
76
76
0
T
94
14
3/18/2016
80
80
10
100
20
70
74
14
60
74
24
80
O
50
94
24
70
88
34
60
114
34
80
122
52 70
82
52
30
82
62
20
40
94
64
30
88
38
50
88
48
108
48
60
102
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80
10
45
A* Tracing Best Path(s)
Red = G, Blue = H, White = F
O
O
O
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A* Summary
1) Add the starting square (or node) to the open list.
2) Repeat the following:
a) Look for the lowest F cost square on the open list (i.e. Current Cell)
b) Switch it (Current Cell) to the closed list.
c) For each of the 8 squares adjacent to this current square;
If the cell is not reachable, or if the cell is on the closed list, ignore it.
Otherwise do the following.
i) If the cell is not on the open list, add it to the open list.
ii) Make the current square the parent of this square.
iii) Record the F, G, and H costs of the square.
iv)If it is on the open list already, check to see if this path to that square is better, using G
or F score
d) Stop when:
•
Target square is added to the closed list, or
•
Open list is empty. In this case, there is no path.
3) Save the path. Trace path backwards from the target square, go from each square to its parent
square until the starting square is reached. That is the optimal path.
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Tangent Bug Algorithm
Algorithm developed by Ishay Kamon, Elon Remon, and Ehud Rivlin
in 1995 to determine path through previously unknown terrains. Its
approach relies on the incremental creation of a “roadmap” within
the visible region.
Conditions:
– The path must not intersect any obstacles (correctness).
– Robot must reach the goal in a finite amount of time (completeness) or
determine that the goal is unreachable.
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Tangent Bug Algorithm
•
•
Uses Local Tangent Graph (LTG), which
consists of nodes including Robot’s Location
(xR), endpoints of sensed obstacles, and
edges between the robot and the sensed
obstacle endpoints.
An optional Tg node is added on the line
segment xRxG if and only if the line does not
intersect any obstacles and d(xR, xg) > R.
Modes of Operations:
1) Motion to Goal Mode: Decreases the
distance to the goal
2) Boundary Following Mode: Escapes Local
Minima in the distance to goal function
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Tangent Bug Algorithm
Modes of Operations:
1) Motion to Goal (MtG) Mode:
Robot selects locally optimal path to
goal until
A. Goal is Reached.
B. Local Minimum of the distance to goal
function is detected. Switch to Boundary
Following Mode.
2) Boundary Following (BF) Mode:
Robot moves around the obstacle in
the direction it last recently traveled using
MtG, while updating minimum distances
from new nodes to the goal.
A. The goal is reached
B. Goal is unreachable.
C. Escapes BF Mode if the local minimum
condition is met
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A* and Tangent Bug
Conclusion
• The A* algorithm and Tangent Bug are useful in unbounded
environments.
• The A* and Tangent Bug produce “locally optimal” solutions, that is,
the resultant paths are the shortest length possible given the use of
solely local information.
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Simulation Results using A*:
L2 Map
•
•
•
•
45 degrees Turn = 0.68 s
Sensor Sweep Time = 1.36 s
Velocity (no load) = 0.325 m/s
Velocity (with load) = 0.293 m/s
Estimated Travel Time =
(Turn Time x # of turns)
+
(Sensor Sweep Time x # of sweeps)
+
(Distance Traveled / Velocity with
Load)
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Simulated Distance
Performance
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Simulated Time
Performance
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Simulation Performance Metrics
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Ongoing Research
•
•
•
•
•
•
Localization Problem
Complimentary Filter
Kalman Filter
Virtual-Reality Head-Tracking Vision Control
Multitasking Problem
RTOS
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Mobile Robot
Localization Problem
Localization – Determination of robot’s true location and orientation
calculated based on the information from its sensor data fusion.
Dead Reckoning – Technique using odometry for sensor
measurements and trigonometry with kinematic equations for
calculating robot’s new position.
Requirements:
1) Knowledge of robot’s initial position
2) Accuracy decreases over time as error accumulates
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Complimentary Filter
Approach based on DCM
• Objective: Fusing gyroscope, accelerometer, and magnetometer
(compass) data in order to create an estimation of the device
orientation in space.
• Alternatively referred to as “Simplified Kalman Filter”
• Gyroscopes will determine more precise orientation, then the one
estimated directly from the accelerometer and magnetometer direct
readings which are subject to a lot of noise in form of external (nongravitational) inertial forces (i.e. acceleration) or magnetically forces
that are not caused by the earth’s magnetic field.
• Complimentary filter is an algorithm that combines the readings
from all three devices in order to create best estimate regarding the
device orientation in space
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DCM (Direction Cosine Matrix)
Algorithm
• To calculate the device orientation in the space using gyroscope and
accelerometer data:
– Roll: The angle between the Yb axis and the horizontal plane
– Pitch: The angle between the Xb axis and the horizontal plane
– Yaw (Heading): The angle with respect to the magnetic north
pole
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DCM Procedure
1.
2.
3.
4.
5.
6.
7.
Data Acquisition via I2C from gyroscope and accelerometer
Data Acquisition via I2C from Magnetometer
Determining Magnetic Heading
Update DCM Matrix
a. Update DCM values
b. Add Proportional and Integrator terms
Normalization and Renormalization to enforce orthogonality
conditions
Drift Correction
a.
Calculate magnitude of the vectors
b.
Perform Dynamic Weighting
Determine Euler Angles (roll, pitch, yaw) from DCM Matrix
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Kalman Filter Approach
• In navigation systems, Kalman filters are based on the
complementary filtering principle.
• It is an optimal estimator – infers parameters from indirect,
inaccurate, and uncertain observations.
• It is recursive – new measurements are processed as they are
available
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Kalman Filter Recursive Processing
1) State Estimation
A)
B)
C)
D)
E)
Known are x(k|k ), u(k ), P(k|k ) and the new measurement z(k+1).
State Prediction
(Time Update)
Measurement Prediction:
(Time Update)
Measurement Residual:
(Measurement Update)
Updated State Estimate:
(Measurement Update with Kalman Gain)
2) State Covariance Estimation
A)
B)
C)
D)
State prediction covariance
Measurement prediction covariance
Filter Gain
Updated State Covariance
3) Matrix Riccati Equation
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Localization Sensor Fusion
Filtered and Combined Outputs & States
Robot Localization Fusion Center
Error State Covariance Update
State Estimate Update
GPS
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Compass
Gyroscope
NASA Grant URC NCC NNX08BA44A
Accelerometer
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ITV Goggles
Display Specs:
•
•
•
•
Equivalent Virtual Screen Size(In 2M): 80'' inch
Resolution: 640 x 480, 920k pixels
Color Depth: 24bit
Angel of View: 35 degrees
User Adjustable Features:
•
•
•
•
Volume
Brightness
Contrast
Switch between 2D/3D
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VR Head-Tracking Vision Control
Specifications
Three servo motors
ITV Goggles
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Accelerometer Arduino Board Servo Controller Wireless Camera
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VR Head-Tracking Vision Control
• Human Controlled Real-Time
Remote Observation
• On-demand Multimedia Retrieval
• Automatic Remote Surveillance
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Head-tracking Standalone
Demonstration
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Multitasking Problem
• A single threaded design prevents real-time sensor feedback since
executing tasks cannot be interrupted.
• Communications may be dropped if the processor is handling a
previous command.
• Task switching between interrupt handlers is not prioritized.
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RTOS Micrium
Implementation Solution
• A PSoC-5 compatible build of μC/OS-III exists.
• Feedback control loops must be implemented to integrate sensor
outputs.
• Multithreading will allow commands to be issued at any time.
• Interrupts can be prioritized to respond to sensor triggered events.
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Conclusion and Future Work
•
•
•
•
•
•
•
•
Continue with current Hybrid Routing Algorithm Model implementation
Survey and implement a library of navigational algorithms for various path-finding scenarios.
Incorporate natural environment sensors into the system to retrieve scientific data.
Information tagging to the corresponding coordinate location.
Implementation of Control Algorithms for stability and localization.
Wireless Head-tracking Controlled 3D Stereoscopic Observation
Reconfiguration of embedded systems.
Investigate the concepts of Robotic Network for large area sensor coverage.
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ACC Team Timeline
Summer 2012 – Spring 2013
2013
Summer 2012
Fall 2012
Winter 2013
Spring 2013
Evan Tsai
Low-level PSoC 5 &
Sensor Implementation
PSoC 5 Hardware &
Software Implementation
PSoC 5 Hardware &
Software Implementation &
Robot Construction
PSoC 5 Hardware &
Software Implementation
Aleks
Milshteyn
Kinect Research and
Host PC Software
Development
Robot Interface / Host PC
Software Development
Robot Navigational
Algorithms and
Kinect Integration
Robot Navigational
Algorithms and Host PC
Interface Development
Airs Lin
PSoC-5 and Host PC
Interface Development
Data Structure and
Communication Protocols
PSoC 5 & Host PC
Interface Development
Embedded Host PC
Interface Development
Gabriel
Nunez
PSoC-5 and Host PC
Interface Development
Robot Navigational
Algorithms and Host PC
Interface Development
Gregory
Carter
PSoC and FPGA
Research
Sergio
Mendoza
PSoC and FPGA
Research
PSoC 5 Data Acquisition
and Formatting
PSoC 5 Data Processing,
Host PC Interface,
Kinect Testing
80
Garth
Herman
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ACC Team Timeline
Summer 2013 – Spring 2014
2013
Summer 2013
Fall 2013
Winter 2014
Spring 2014
Evan Tsai
PSoC 5 Hardware &
Sensor Implementation
PSoC 5 Hardware &
Software Implementation
PSoC 5 Hardware &
Software Implementation
PSoC 5 Hardware &
Software Implementation
Aleks
Milshteyn
Kinect and Host PC
Software Development
Robot Interface / Host PC
Software Development
Robot Navigational
Algorithms and Host PC
Interface Development
Robot Navigational
Algorithms
Airs Lin
PSoC-5 and Host PC
Interface Development
Data Structure and
Communication Protocols
PSoC 5 & Host PC
Interface Development
Embedde Host PC
Interface Development
Garth
Herman
PSoC-5 Data Acquisition
and Filtering,
RTOS Implementation
PSoC-5 Data Filtering
and Host PC Integration,
RTOS Implementation
PSoC-5 and Host PC
Integration,
RTOS Implementation
PSoC-5 and Host PC
Integration,
RTOS Implementation
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References
•
•
•
[1] "International Lunar Network" NASA URL: <http://iln.arc.nasa.gov/ >
[2] “Mars Science Laboratory Curiosity Rover" NASA URL: <http://mars.jpl.nasa.gov/msl/>
[3] “PSoC5: CY8C55 Family Datasheet: Programmable System-on-Chip (PSOC)”, Cypress URL:
<http://www.cypress.com/?docID=34815>
•
[4] XL- MaxSonar® - EZ0™ (MB1200) Sonar Range Finder with High Power
•
Output, Noise Rejection, Auto Calibration & Long-Range Wide Detection Zone URL:
<http://www.maxbotix.com/documents/MB1200-MB1300_Datasheet.pdf>
•
[5] Sharp GP2Y0A21YK0F Distance Measuring Sensor Unit Measuring distance: 10 to 80 cm Analog output type
URL: < http://www.pololu.com/file/0J85/gp2y0a21yk0f.pdf>
•
[6] LSM303DLM Sensor module: 3-axis accelerometer and 3-axis magnetometer URL:
<http://www.pololu.com/file/0J514/LSM303DLM.pdf>
•
[7] L3G4200D MEMS motion sensor: ultra-stable three-axis digital output gyroscope URL:
<http://www.pololu.com/file/0J491/L3G4200D.pdf>
•
[8] Rover 5 URL http://www.pololu.com/file/0J467/Rover%205.pdf
•
[9] A. Stentz, “Optimal and Efficient Path Planning for Partially-Known Environments,” in Proc. IEEE
•
Conf. Robotics Automat., 1994.
•
[10] 5.0 A Throttle Control H-Bridge URL: <http://www.pololu.com/file/0J233/MC33926.pdf>
•
[11] Pololu Wixel User’s Guide URL: http://www.pololu.com/docs/pdf/0J46/wixel.pdf
•
[12] nRF24L01 Single Chip 2.4GHz Transceiver URL:
www.nordicsemi.com/eng/nordic/download_resource/8041/1/87271851
•
[13] Lindsay Kleeman. Understanding and Applying Kalman Filtering. URL:
http://www.cs.cmu.edu/~motionplanning/papers/sbp_papers/integrated3/kleeman_kalman_basics.pdf
•
[14] Student Dave’s Tutorials: Kalman Filter. URL: http://studentdavestutorials.weebly.com/kalman-filter-withmatlab-code.html
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[15] William Premerlani and Paul Bizard.
Cosine
IMU: Theory. URL:
NASADirection
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NCCMatrix
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gentlenav.googlecode.com/files/DCMDraft2.pdf
Any Questions?
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Simulated Platform
Application
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