Cooperative task assignment and path planning for multiple uavs

Abstract

In this chapter, a hierarchical framework for path planning and task assignment for multiple unmanned aerial vehicles in a dynamic environment is presented. For multi-agent scenarios in dynamic environments, a candidate algorithm should be able to replan for a new path to perform the given tasks without any collision with obstacles or other agents. The path-planning algorithm proposed here is based on the visibility and shortest-path principles in Euclidean space. Instead of typical visibility graph-based methods that scan through all nodes, A* algorithm is adopted to find an admissible path in a “best-first” approach during the search process. Since the direct outcome from such algorithms may not produce admissible paths in complex environments due to the problems including cul-de-sac, additional procedures are conceived to find a solution with a lower cost by avoiding local minima and eliminating any redundant nodes. The path planner is augmented with a potential field-based trajectory planner, which solves for a detouring trajectory around other agents or pop-up obstacles. Task assignment is achieved by a negotiation-based algorithm, which assigns a task with the lowest cost to each agent after comparing all task costs of all participating agents. These algorithms are implemented on MATLAB/Simulink, which can run with simulated vehicle models or actual UAVs through a communication network. In the simulations, the algorithms were validated to perform task assignment and path planning flawlessly. In actual flight tests, the proposed algorithms were tested with a number of fixed-wing UAVs in a fully realistic situation under various reality factors such as communication loss or tracking errors. The flight test shows, even in the presence of such uncertainties and logistic factors, the algorithms were able to perform all of the given tasks without any collision with other agents or obstacles.