2 research outputs found
Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems
In this work, we address traffic management of multiple payload transport
systems comprising of non-holonomic robots. We consider loosely coupled rigid
robot formations carrying a payload from one place to another. Each payload
transport system (PTS) moves in various kinds of environments with obstacles.
We ensure each PTS completes its given task by avoiding collisions with other
payload systems and obstacles as well. Each PTS has one leader and multiple
followers and the followers maintain a desired distance and angle with respect
to the leader using a decentralized leader-follower control architecture while
moving in the traffic. We showcase, through simulations the time taken by each
PTS to traverse its respective trajectory with and without other PTS and
obstacles. We show that our strategies help manage the traffic for a large
number of PTS moving from one place to another.Comment: 7 Pages, Accepted to IEEE International Symposium on Multi-Robot and
Multi-Agent Systems, Jun 201
Multi-Robot Formation Control Using Reinforcement Learning
In this paper, we present a machine learning approach to move a group of
robots in a formation. We model the problem as a multi-agent reinforcement
learning problem. Our aim is to design a control policy for maintaining a
desired formation among a number of agents (robots) while moving towards a
desired goal. This is achieved by training our agents to track two agents of
the group and maintain the formation with respect to those agents. We consider
all agents to be homogeneous and model them as unicycle [1]. In contrast to the
leader-follower approach, where each agent has an independent goal, our
approach aims to train the agents to be cooperative and work towards the common
goal. Our motivation to use this method is to make a fully decentralized
multi-agent formation system and scalable for a number of agents