Dynamic Task Allocation Multi-Heterogenous System for Classroom
Environment
Sree Teja Dravidam, Venkata Sai Sanjay Mullapudi, Vamsy Krishna Nanduri, Pavan Srinivas Narayana
Project Objective: Centralized Heterogeneous Task allocation (auction-based methodology)
followed by Partially Decentralized Collective Transport (Leader-Follower) in classroom
environments with multiple layouts. This type of control behavior can be implemented easily in a
classroom environment where the same room has to be in different layouts with respect to different
classes and timings. Centralized Task Allocation can be designed to handle robust scenarios where
Robots might fail in the process of doing a task [1]. Decentralized Path planning caters the Robot’s
individual path based on it immediate neighbors and obstacles [2].
Methodology: An arena M ∈ 𝑹𝒏𝒙𝒏 (in this case a classroom) is a static environment where walls
and obstacles are already known to all the agents. Example Arenas can be found in this link. We
consider there are two types of furniture based on the weight i.e., a table with two units of load,
and a chair with one unit of load. There are two storage units for this furniture (Tables and Chairs)
and one storage for the robots to be placed initially. Let there be k number of agents in a random
configuration inside the Robot’s storage initially. We consider two types of Robots based on its
ability to move the load. Robot A: Agents with the load bearing capacity of 2, i.e. they can move
one table or two chairs by themselves and Agent B: Agents with a load bearing capacity of 1, i.e.
they can only move one chair. For the agents with capacity of 1 (Robot B) to move a table, they
need to collaborate with other agents of similar kind. We assume that all the agents know the map
and static objects in the arena beforehand.
Task Allocation: An Auction based Task Allocation Module will be used which takes in the
required layout of the room and creates a queue of tasks to achieve the final goal layout. The Task
allocation module auctions the task from its queue in top-down approach to all the agents for
bidding. Each agent ki will bid to the task that is being auctioned in the form of a value which is
calculated based on the time that agent ki will take to complete the task by moving to the all the
goal states Tki (including any tasks which are already queued in their own to-do-list), and the
capacity of the robot. Then the task allocation module collects all these bids and will try to assign
the task to the agent which has lowest value.
All the agents with capacity 2 have the states 𝑆𝐴 ∈ 𝑅 4 which contains the robot_id, two
components of position vector (Px, Py) and orientation with respect to global directions ϴ. Agents
with capacity 1 have an extra component which defines whether it has reached the goal state
required to hold the furniture. This extra component is used in collaborative transport furniture.
We assume that the robots can sense its neighboring agents when they enter the sensing range of
the agent.
Control methods: We plan to use Graph based methodologies with Artificial Potential Functions
for path planning of the individual robots which can assure collision avoidance and optimal path
to goal state.
We plan to show the convergence of the agents to a reference trajectory when in collaboration and
stability of Potential Functions used as some properties of the controller.
Simulations and Hardware Implementations:
We plan to create simulations in MATLAB environments because it is easy to create arenas and
many tools like Simulink will help us to solve the dynamics of the system.
We plan to show some visualizations to ensure that the system work without any contingencies.
We plan to implement this model in Robotarium Simulator and make changes such that this system
can be implemented in Hardware [3].
Division of Labor: We all will design the environment needed for agents to work.
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Sree Teja Dravidam: Create MATLAB environment scripts based on the arena and
furniture, develops the model for Task Allocation module.
Venkata Sai Sanjay Mullapudi: Design the Path planning model and analyze the properties
like rate of convergence of agents (in case of collaborative transport), develop MATLAB
scripts to simulate Path Planning.
Vamsy Krishna Nanduri: Design the Potential Function and analyze properties like stability
of the dynamic system. Develops the MATLAB scripts to visualize the motion of robots.
Pavan Srinivas Narayana: Deploy the system in Robotarium Simulator and verify its
hardware implementation.
We will divide the work as mentioned above but we will still contribute and come to consensus in
all the modules required to achieve the task like agents in our project.
References:
[1] Badreldin, Mohamed, Ahmed Hussein, and Alaa Khamis. "A Comparative Study between
Optimization and Market-Based Approaches to Multi-Robot Task Allocation." Advances in
Artificial Intelligence (16877470) (2013).
[2] Hamed Farivarnejad and Spring Berman. Multi-Robot Control Strategies for Collective
Transport. Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, no. 1, pp. 205219, May 2022.
[3] Sean Wilson, et al., "The Robotarium: Globally Impactful Opportunities, Challenges, and
Lessons Learned in Remote-Access, Distributed Control of Multirobot Systems," in IEEE Control
Systems Magazine, vol. 40, no. 1, pp. 26-44, Feb. 2020.
[4] Korsah, G. Ayorkor, Anthony Stentz, and M. Bernardine Dias. "A comprehensive taxonomy
for multi-robot task allocation." The International Journal of Robotics Research 32.12 (2013):
1495-1512.