This paper proposes a two-layer path-planning method, where an optimized artificial potential field (APF) method and an improved dynamic window approach (DWA) are used at the global and local layer, respectively. This method enables the robot to plan a better path under a multi-obstacle environment while avoiding the moving obstacles effectively. For the part of global path planning, a new repulsive field is proposed based on the APF method. The length and smoothness of the global path are taken as fitness functions of particle swarm optimization (PSO) to obtain the obstacle influence range, the coefficients of gravitation and repulsion in APF. At the level of local path planning, on the basis of DWA, a fuzzy control scheme is adopted to evaluate the danger level of moving obstacles via collision risk index and relative distance. In brief, compared with existing methods, the robot can reasonably plan a shorter and smoother path with the aid of PSO-based APF, meanwhile quickly react to the moving obstacles and avoid them by fuzzy-based DWA. Finally, a static multi-obstacle environment and two dynamic scenarios with moving obstacles are simulated to verify the effectiveness of the proposed path-planning method.

This paper reports an effective quintic polynomial-based trajectory planning approach and a robust tube-based model predictive control (RTMPC) method for an underactuated tractor-trailer vehicle system during lane change maneuver. To begin with, a time-based quintic polynomial function is introduced for implementation of trajectory planning, which takes vehicle system safety, comfort, and traffic efficiency into account. After that, an RTMPC scheme, consisting of a nominal system-oriented model predictive controller and an ancillary feedback control law, is proposed to construct posture controller, such that favorable transient performances can be achieved for the trajectory tracking process. Lastly, the simulation results confirm that the proposed lane change trajectory planning and tracking control methods can effectively perform the lane change behaviors for tractor-trailer vehicles.

This paper is concerned with a model predictive control (MPC) technique together with an adaptive robust scheme for the trajectory tracking of a wheeled inverted pendulum vehicle in the absence of platform velocity information, in addition to taking dynamic uncertainty and external disturbance into consideration. Specifically, to deal with velocity information loss and dynamic uncertainty, an extended state observer is introduced to evaluate the velocity vectors and model dynamics, where the uniformly ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, MPC is employed for the underactuated longitudinal subsystem to achieve longitudinal velocity tracking, as well as holding the pendulum-like vehicle body stability, and in particular taking the state and input saturation into account; at the same time, an adaptive robust controller is constructed for the rotational subsystem to realize the rotational velocity tracking, in which the adaptive laws can enhance the vehicle adaptability in diverse environment. In addition, a saturated trajectory generator with closed-loop characteristic is introduced so as to properly handle the velocity limitation and nonholonomic constraint simultaneously. The simulation results validate that the control system is robust against the external disturbance and model uncertainty, thereby demonstrating the effectiveness and robustness of the proposed control strategy.