Feedback Stabilization of Nonholonomic Mobile Robots with Obstacle Avoidance

2006 ◽  
Author(s):  
Xie Xiaoli ◽  
Yang Tiantian ◽  
Liu Zhiyuan
Keyword(s):  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Feedback Stabilization ◽  
Nonholonomic Mobile Robots

scholarly journals Adaptive State-Feedback Stabilization for Stochastic Nonholonomic Mobile Robots with Unknown Parameters

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wenli Feng ◽  
Qingli Sun ◽  
Zhijun Cao ◽  
Dongkai Zhang ◽  
Hua Chen
Keyword(s):  
Mobile Robots ◽  
State Feedback ◽  
Original System ◽  
Feedback Stabilization ◽  
Unknown Parameters ◽  
Nonholonomic Mobile Robots ◽  
Stabilizing Controllers ◽  
Stochastic Case ◽  
Switching Control Strategy ◽  
Zero Equilibrium

The stabilizing problem of stochastic nonholonomic mobile robots with uncertain parameters is addressed in this paper. The nonholonomic mobile robots with kinematic unknown parameters are extended to the stochastic case. Based on backstepping technique, adaptive state-feedback stabilizing controllers are designed for nonholonomic mobile robots with kinematic unknown parameters whose linear velocity and angular velocity are subject to some stochastic disturbances simultaneously. A switching control strategy for the original system is presented. The proposed controllers that guarantee the states of closed-loop system are asymptotically stabilized at the zero equilibrium point in probability.

Neural Control System for Autonomous Vehicles

2011 ◽  
pp. 1197-1204
Author(s):  
Francisco García-Córdova ◽  
Antonio Guerrero-González ◽  
Fulgencio Marín-García
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Neural Control ◽  
Learning Rule ◽  
Nonholonomic Mobile Robots ◽  
Neural Architecture ◽  
Neuro Controller

Neural networks have been used in a number of robotic applications (Das & Kar, 2006; Fierro & Lewis, 1998), including both manipulators and mobile robots. A typical approach is to use neural networks for nonlinear system modelling, including for instance the learning of forward and inverse models of a plant, noise cancellation, and other forms of nonlinear control (Fierro & Lewis, 1998). An alternative approach is to solve a particular problem by designing a specialized neural network architecture and/or learning rule (Sutton & Barto, 1981). It is clear that biological brains, though exhibiting a certain degree of homogeneity, rely on many specialized circuits designed to solve particular problems. We are interested in understanding how animals are able to solve complex problems such as learning to navigate in an unknown environment, with the aim of applying what is learned of biology to the control of robots (Chang & Gaudiano, 1998; Martínez-Marín, 2007; Montes-González, Santos-Reyes & Ríos- Figueroa, 2006). In particular, this article presents a neural architecture that makes possible the integration of a kinematical adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller for nonholonomic mobile robots. The kinematical adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates (García-Córdova, Guerrero-González & García-Marín, 2007). The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The obstacle avoidance adaptive neurocontroller is a neural network that learns to control avoidance behaviours in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around a cluttered environment with obstacles. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot’s sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.

scholarly journals Research on Trajectory Tracking and Obstacle Avoidance of Nonholonomic Mobile Robots in a Dynamic Environment

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 74
Author(s):  
Kai Zhang ◽  
Ruizhen Gao ◽  
Jingjun Zhang
Keyword(s):  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Trajectory Tracking ◽  
Dynamic Environment ◽  
Inequality Constraints ◽  
Nonlinear Constraint ◽  
Nonholonomic Mobile Robots ◽  
Trajectory Tracking Control ◽  
Avoidance Problem ◽  
Position State

This paper presents an obstacle-avoidance trajectory tracking method based on a nonlinear model prediction, with a dynamic environment considered in the trajectory tracking of nonholonomic mobile robots for obstacle avoidance. In this method, collision avoidance is embedded into the trajectory tracking control problem as a nonlinear constraint of the position state, which changes with time to solve the obstacle-avoidance problem in dynamic environments. The CasADi toolkit was used in MATLAB to generate a real-time, efficient C++ code with inequality constraints to avoid collisions. Trajectory tracking and obstacle avoidance in dynamic and static environments are trialed using MATLAB and CasADi simulations, and the effectiveness of the proposed control algorithm is verified.

A Switched-System Approach to Shared Robust Control and Obstacle Avoidance for Mobile Robots

2014 ◽  
Author(s):  
Jingfu Jin ◽  
Nicholas Gans ◽  
Yoon-Gu Kim ◽  
Sung-Gil Wee
Keyword(s):  
Robust Control ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
System Approach ◽  
Variable Structure ◽  
Switched System ◽  
Shared Control ◽  
Nonholonomic Mobile Robots ◽  
Control Approach ◽  
Free Region

We propose a shared control structure for nonholonomic mobile robots, in which a human operator can command motions that override autonomous operation, and the robot overrides either the teleoperation or autonomous controller if it encounters an obstacle. We divide the whole configuration, including orientation, space into an obstacle avoidance and an obstacle-free region. This enables a switched-system approach to switch between autonomous and teleoperation mode, or the obstacle avoidance and the obstacle-free region. To reject disturbances or noise present in the error dynamics, two different robust control laws are proposed using a high gain and a variable structure approach. Lyapunov-based stability analysis is provided. To rigorously test the approach under different circumstances, experiments have been conducted by two different research groups. The results from two groups show that the shared control approach works effectively both in the teleoperation mode and autonomous mode with different system settings and environments.

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