Multi-agent path planning with multiple tasks and distance constraints

The DPC algorithm developed in our previous work is an efficient way of computing optimal trajectories for multiple robots in a distributed fashion with time-parameterized constraints on the distances between pairs of robots. In the present work we extend DPC to the problem of multiple task execution. While this extended problem inherits all the objectives, complexities and constraints of the basic DPC algorithm, each robot is also given an unordered set of tasks that it has to execute before it reaches its goal. There is no specific order imposed on the tasks assigned to a particular robot. The algorithm decides the order of execution of the tasks such that an optimal solution is attained while the time-parametrized distance constraints are satisfied along with successful execution of the tasks. We solve this problem by designing a “State-task Graph” that represents a product of the state-space graph and the task graph. We then develop an efficient heuristic function for performing searches in this graph.