Owing to uncertainties in both kinematics and dynamics, the current trajectory tracking framework for mobile robots like spherical robots cannot function effectively on multiple terrains, especially uneven and unknown ones. Since this is a prerequisite for robots to execute tasks in the wild, we enhance our previous hierarchical trajectory tracking framework to handle this issue. First, a modified adaptive RBF neural network (RBFNN) is proposed to represent all uncertainties in kinodynamics. Then the Lyapunov function is utilized to design its adaptive law, and a variable step-size algorithm is employed in the weights update procedure to accelerate convergence and improve stability. Hence, a new adaptive model prediction controlbased instruction planner (VAN-MPC) is proposed. Without modifying the bottom controllers, we finally develop the multi-terrain trajectory tracking framework by employing the new instruction planner VAN-MPC. The practical experiments demonstrate its effectiveness and robustness.
This article proposes a modification of hybrid A* method used for navigation of spherical mobile robots with the ability of limited partial lateral movement driven by pendulum. For pendulum-driven spherical robots with nonzero minimal turning radius, our modification helps to find a feasible and achievable path, which can be followed in line with the low time cost. Because of spherical shell shape, the robot is point contact with the ground, showing different kinematic model compared with common ground mobile robots such as differential robot and wheeled car-like robot. Therefore, this article analyzes the kinematic model of spherical robot and proposes a novel method to generate feasible and achievable paths conforming to kinematic constraints, which can be the initial value of future trajectory tracking control and further optimization. A concept of optimal robot’s minimum area for rotation is also proposed to improve search efficiency and ensure the ability of turning to any orientation by moving forward and backward in a finite number of times within limited areas.
There are few studies on the lateral motion of spherical robots. In this article, a new algorithm is proposed to solve the problem of low control accuracy of the lateral motion. A single lateral motion model is established, and the optimal solution of the linear quadratic regulator in infinite time domain is obtained. Aiming at the problems of longitudinal velocity and lateral angle coupling and low control precision, state compensation and velocity feedforward are carried out, and an improved robust servo linear quadratic regulator control algorithm is proposed. Experiments show that the proposed lateral control algorithm has strong adaptability and robustness to changing speeds and lateral angles, and the control effect is stable and reliable.
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