Dynamic obstacle avoidance and target tracking for a swarm of robots using distributed Kalman filter

Abstract

In this this paper we have proposed a novel way for avoiding dynamic stochastic obstacles through estimating the position and velocity of the obstacles using Kalman consensus filter which is also known as distributed Kalman filter while seeking to track a stochastic target and keeping the connectivity of the swarm. Estimation algorithms enables the agents to propagate their measurements of the target or obstacles to the entire swarm. Simulation results shows the efficiency of the proposed method.