Abstract
This work proposes a control architecture for a group of nonholonomic robotic vehicles. We present a decentralized control strategy that permits each vehicle to autonomously compute an optimal trajectory by using only locally generated information. We propose a method to incorporate reactive terms in the path planning process which adapt the trajectory of each robot, thus avoiding obstacles and maintaining communication links while it reaches the desired positions in the robot formation. We provide the proof of the reachability of the trajectory generation between the current and desired position of each follower. Simulation results validate and highlight the efficiency and relevance of this method. An integration of the wireless network signal strength data with the vehicle sensors information by means of a Kalman filter is proposed to estimate the relative position of each vehicle in a robot formation set. Vehicle sensors consist of wheel speed and steering angle, the WiFi data consist of reception signal strength (RSS) and the angle of the maximal RSS with respect to the robot orientation. A nonholonomic nonlinear model vehicle is considered; due to these nonlinearities an Extended Kalman Filter EKF is used. Simulation and experimental results of the proposed estimation strategy are presented.