To enhance the navigation and control efficiency of mobile robots in the field of health monitoring, a novel path planning and control strategy for mobile robots with uncertain dynamics based on improved artificial potential fields is proposed in this paper. Specifically, we propose an attractive potential field rotation method to overcome the limitation that traditional artificial potential fields tend to fall into local minima. Then, we define a new class of attractive potential fields to address the goals non-reachable with obstacles nearby (GNRON) and collisions caused by excessive attractive force at long distances from the target point. Furthermore, a control law is proposed for the mobile robot with uncertain dynamics, and the stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, the feasibility and effectiveness of the proposed method are verified by simulations and experiments.

This paper develops a new vision-based robot customized for table tennis juggling tasks. Specifically, the robot is equipped with two industrial cameras operating as a sensing system. An image-processing algorithm is proposed that allows the robot to balance a table tennis ball while controlling its bounce height. The robot adopts a parallel structure design, and the end effector employs three ball joints to increase the degree of freedom (DOF) of the parallel mechanism. In addition, we design a control scheme explicitly customized for this robotic system. Extensive real-time experiments are performed to show the effectiveness of the juggling robot at different jumping heights. Furthermore, the ability to consistently maintain a fixed preset bounce height is demonstrated. These experimental results confirm the efficacy of the developed robotic system.