Abstract
In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method.
Highlights
The topic of unmanned ground vehicle (UGV)[1] has become increasingly popular in the field of intelligent transportation system
Motivated by the safety and rapidity of obstacle avoidance of the UGV, a steering control strategy based on model predictive control (MPC), consisting of path planning and path tracking, is proposed in this paper
This paper focuses on the obstacle avoidance steering control strategy, so the states of obstacles are assumed to be known for the UGV
Summary
The topic of unmanned ground vehicle (UGV)[1] has become increasingly popular in the field of intelligent transportation system. Obstacle avoidance steering problem is formulated as steering tracking control.[4] Naranjo et al.[5] suggest that there are two ways to design the steering controllers: imitating human drivers and using the dynamic model of the car. Motivated by the safety and rapidity of obstacle avoidance of the UGV, a steering control strategy based on MPC, consisting of path planning and path tracking, is proposed in this paper. In order to decrease the computation burden, the actual control input (front-wheel-steering angle) constraint is considered in path planning, so that the tracking design becomes an unconstrained optimization problem. If the decision-making system of the UGV determines that obstacles are static, the path planning controller will utilize the optimal path reconfiguration based on DC to obtain a local optimal path online. The local path will be calculated periodically for the movement of obstacles
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