This paper proposes an optimized trajectory planner and motion planner framework, which aim to deal with obstacle avoidance along a reference road for autonomous driving in unstructured environments. The trajectory planning problem is decomposed into lateral and longitudinal planning sub-tasks along the reference road. First, a vehicle kinematic model with road coordinates is established to describe the lateral movement of the vehicle. Then, nonlinear optimization based on a vehicle kinematic model in the space domain is employed to smooth the reference road. Second, a multilayered search algorithm is applied in the lateral-space domain to deal with obstacles and find a suitable path boundary. Then, the optimized path planner calculates the optimal path by considering the distance to the reference road and the curvature constraints. Furthermore, the optimized speed planner takes into account the speed boundary in the space domain and the constraints on vehicle acceleration. The optimal speed profile is obtained by using a numerical optimization method. Furthermore, a motion controller based on a kinematic error model is proposed to follow the desired trajectory. Finally, the experimental results show the effectiveness of the proposed trajectory planner and motion controller framework in handling typical scenarios and avoiding obstacles safely and smoothly on the reference road and in unstructured environments.
Slip rate control is important in improving vehicle stability and driving efficiency. In this paper, a robust slip rate control system is designed for distributed drive electric vehicles that consists of two slip rate estimators for multi-driving conditions, a vehicle speed estimator, and an anti-windup robust variable structure slip rate tracking controller. Because there is no driven wheel in a four-in-wheel-motor distributed drive electric vehicle, the estimators for small and large slip rates are designed based on dynamic and kinematic methods, respectively, which can switch according to the slip conditions. The convergence of the estimation error is discussed with the Lyapunov stability law and is less than 2% under the condition of acceleration on a low-friction road. The slip rate tracking controller is designed based on the sliding mode control law and the proportional-integral (PI) control method to handle model nonlinearity, modelling and estimation errors, and disturbances and to control the input chattering and saturation. The asymptotic stability of the tracking error is proven by Lyapunov theory. A joint control variable composed of the wheel angular acceleration and slip rate is designed to improve the robustness of the controller against the slip rate estimation error. Simulations and experiments under various conditions are performed to verify the proposed anti-slip control method. The results show that compared with a distributed drive vehicle without a slip rate controller, the controlled vehicle can prevent serious wheel skid on low-adhesion roads and improve the driving performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.