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Path Planning for a Network of Robots with Distributed Multi-Objective Linear Programming

In this paper, the problem of target pursuit and obstacle avoidance in a network of robots is considered. No single robot in the network is aware of all the obstacles in the arena, and therefore, knowledge of obstacle avoidance constraints is distributed across the network. A distributed multi-objective linear programming form of the problem is derived and its efficient solutions identified. This is broken down into a set of distributed linear programs that are solved at each robot, which is substantially computationally cheaper than nonlinear optimization. Numerical examples are used to demonstrate the efficacy of the proposed method in a network of robots navigating through a dynamic environment.