Distributed motion planning and sensor fusion for cooperative behavior of mobile robots

The paper analyzes two important issues in the design of multi-robot systems: (i) motion planning with the use of distributed algorithms, (ii) sensor fusion with the use of Extended Kalman or Particle Filtering. First, distributed gradient for motion planning of a multi-robot system is examined. The dynamic model of the multi-robot system is derived and its convergence to the desirable position is proved using Lyapunov analysis of the distributed gradient algorithm. Next, the problem of localization of a mobile robot through the fusion of measurements that come from distributed sensors is examined. Two approaches are studied: sensor fusion using Extended Kalman Filtering and sensor Fusion using Particle Filtering. Simulation tests are provided to evaluate the performance of the distributed motion planning algorithm for the multi-robot system, as well as the performance of Extended Kalman and Particle Filtering in mobile robotics sensor fusion problems.