Jorge Cortés


Cooperative dynamic domain reduction
A. Ma, M. Ouimet, J. Cortés
Distributed Autonomous Robotic Systems: The 14th International Symposium, ed. N. Correll, M. Schwager and M. Otte, Springer Proceedings in Advanced Robotics, vol. 9, Springer, New York, 2019, pp. 499-512


Unmanned vehicles (UxVs) are increasingly deployed in a wide range of challenging scenarios, including disaster response, surveillance, and search and rescue. Consider a scenario where a heterogeneous swarm of UxVs are tasked with completing a wide variety of types of objectives that possibly require cooperation from vehicles of differing capabilities. Our goal is to find a framework that enables vehicles to distributively and autonomously aid each other in the services of these objectives. We approach this problem by extension of Dynamic domain reduction for multi-agent planning (DDRP), in which we created a framework that utilizes model-based hierarchical reinforcement learning and spatial state abstractions crafted for robotic planning. We extend DDRP with a design for optimizing the joint trajectories to allow multiple agents to coordinate for the completion of cooperative objectives. We modify of our previous framework and introduce an algorithm that uses simulated annealing to find joint trajectories. We call the result of the modifications and extensions, Cooperative dynamic domain reduction for multi-agent planning (CDDRP). Our analysis characterizes long term convergence in probability to the optimal set of trajectories. We provide simulations to estimate the performance of CDDRP in the context of swarm deployment.


Mechanical and Aerospace Engineering, University of California, San Diego
9500 Gilman Dr, La Jolla, California, 92093-0411

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