1 research outputs found
Integrated Task and Motion Planning for Multiple Robots under Path and Communication Uncertainties
We consider a problem called task ordering with path uncertainty (TOP-U)
where multiple robots are provided with a set of task locations to visit in a
bounded environment, but the length of the path between a pair of task
locations is initially known only coarsely by the robots. The objective of the
robots is to find the order of tasks that reduces the path length (or, energy
expended) to visit the task locations in such a scenario. To solve this
problem, we propose an abstraction called a task reachability graph (TRG) that
integrates the task ordering with the path planning by the robots. The TRG is
updated dynamically based on inter-task path costs calculated using a
sampling-based motion planner, and, a Hidden Markov Model (HMM)-based technique
that calculates the belief in the current path costs based on the environment
perceived by the robot's sensors and task completion information received from
other robots. We then describe a Markov Decision Process (MDP)-based algorithm
that can select the paths that reduce the overall path length to visit the task
locations and a coordination algorithm that resolves path conflicts between
robots. We have shown analytically that our task selection algorithm finds the
lowest cost path returned by the motion planner, and, that our proposed
coordination algorithm is deadlock free. We have also evaluated our algorithm
on simulated Corobot robots within different environments while varying the
number of task locations, obstacle geometries and number of robots, as well as
on physical Corobot robots. Our results show that the TRG-based approach can
perform considerably better in planning and locomotion times, and number of
re-plans, while traveling almost-similar distances as compared to a closest
first, no uncertainty (CFNU) task selection algorithm