Fuzzy inferencing-based path planning with a cyber-physical framework and adaptive second-order SMC for routing and mobility control in a robotic network

In this study, the authors address the problem of optimal routing and relative motion control in a network of robots. The path planning scheme has been designed using a fuzzy-based potential function employing optimal routing parameters. The optimal routing variables, such as routing probability and the transmission rate are obtained using a discrete optimisation problem. To deal with the disturbances and uncertainties in the physical system, an adaptive second-order sliding mode control(SMC) scheme has been proposed for the relative motion control of the networks of robots, where the disturbances are estimated using a novel disturbance observer and the controller parameters are updated online using an adaptive tuning algorithm derived based on Lyapunov theory. The robustness of the proposed path planner and the control scheme are validated through simulation as well as through real-time experimentation based on Pioneer P3-DX robots. The comparison results based on conventional SMC and adaptive SMC are also drawn.