1 research outputs found
A Novel Warehouse Multi-Robot Automation System with Semi-Complete and Computationally Efficient Path Planning and Adaptive Genetic Task Allocation Algorithms
We consider the problem of warehouse multi-robot automation system in
discrete-time and discrete-space configuration with focus on the task
allocation and conflict-free path planning. We present a system design where a
centralized server handles the task allocation and each robot performs local
path planning distributively. A genetic-based task allocation algorithm is
firstly presented, with modification to enable heuristic learning. A
semi-complete potential field based local path planning algorithm is then
proposed, named the recursive excitation/relaxation artificial potential field
(RERAPF). A mathematical proof is also presented to show the semi-completeness
of the RERAPF algorithm. The main contribution of this paper is the
modification of conventional artificial potential field (APF) to be
semi-complete while computationally efficient, resolving the traditional issue
of incompleteness. Simulation results are also presented for performance
evaluation of the proposed path planning algorithm and the overall system.Comment: Accepted by the 15th International Conference on Control, Automation,
Robotics and Vision, ICARCV 201