Self-Adaptive Path Tracking Control for Mobile Robots under Slippage Conditions Based on an RBF Neural Network

Abstract

Wheeled mobile robots are widely implemented in the field environment where slipping and skidding may often occur. This paper presents a self-adaptive path tracking control framework based on a radial basis function (RBF) neural network to overcome slippage disturbances. Both kinematic and dynamic models of a wheeled robot with skid-steer characteristics are established with position, orientation, and equivalent tracking error definitions. A dual-loop control framework is proposed, and kinematic and dynamic models are integrated in the inner and outer loops, respectively. An RBF neutral network is employed for yaw rate control to realize adaptability to longitudinal slippage. Simulations employing the proposed control framework are performed to track snaking and a DLC reference path with slip ratio variations. The results suggest that the proposed control framework yields much lower position and orientation errors compared with those of a PID and a single neuron network (SNN) controller. It also exhibits prior anti-disturbance performance and adaptability to longitudinal slippage. The proposed control framework could thus be employed for autonomous mobile robots working on complex terrain.

Torque control of a wheeled humanoid robot with dual redundant arms

“…where w u c is the wrench due to driving forces, and w u D is the wrench due to external forces. Premultiply equation (19) with DeNOC matrix G u for obtaining a set of independent equations for motion by eliminating the constraint wrench elements. The unconstrained set of equations for motion is given by equation ( 21)…”

“…The proposed controller approach was validated using TALOS humanoid robot. A self-adaptive type mobile robot tracking control algorithm was defined by Kang et al 19 The tracking control was based on radial basis function (RBF) to overcome a potential mobile robot slippage. A kinematic model and a dynamic model of the mobile robot were derived by incorporating skid and steer parameters.…”

A Survey of Machine Learning for Information Processing and Networking

“…The BP NN (Neural Network) determines the approximate bias between the UKF throughput and the current condition when the input data are modified. Algorithms linked to mobile robots are included in [36]. For the purpose of following a target via wireless sensor networks, it uses an improved form of SLAM Simultaneous Localization and Mapping (SLAM).…”