Path planning and robust fuzzy output-feedback control for unmanned ground vehicles with obstacle avoidance

Abstract

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.

Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

“…Because UGV path tracking control is a strongly nonlinear process, the control methods for previous linear systems seem to be inadequate [25]. At present, the common control methods include adaptive control, sliding mode control, fuzzy control, neural network control, and predictive control [26][27][28][29][30][31][32][33][34][35]. For instance, for formation control of multiple vehicles, the leader-follower error model was built and an adaptive controller was designed to address the uncertain relative distance using the dynamic estimation of the leader-follower distance [26].…”

“…For the accuracy and stability of driverless buses, a path-tracking controller based on fuzzy pure pursuit control with a front axle reference (FPPC-FAR) was proposed [30]. An obstacle avoidance strategy consisting of path planning and robust fuzzy output feedback control was proposed for unmanned ground vehicles [31]. A novel adaptive neural network robust lateral motion control method was presented that can maintain the yaw stability of an autonomous vehicle while minimizing the lateral path tracking error at the limits of driving conditions [32].…”

Solution of the linear quadratic regulator problem of black box linear systems using reinforcement learning

“…Black box systems are commonly used to model systems which can be viewed in terms of measurements of its inputs and outputs [1]. For these kind of systems, both the internal states and the parameters of the system are unknown [2,3], e.g., robots [4,5], unmanned vehicles [6], chemical and biological processes [7], among others.…”

Vehicle steering control method based on machine learning

“…However, the integration of visual information and general installation subcontracts (GIS) information increased complexity of the system, and it took at 50-100 ms to detect one frame. In 2021, Chen et al 8 propose an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control and that method is validated in CarSim. But they don't test on road dataset or real road.…”