The hitting position and velocity control for table tennis robots have been investigated widely in the literature. However, most of the studies conducted do not consider the opponent’s hitting behaviors, which may reduce hitting accuracy. This paper proposes a new table tennis robot framework that returns the ball based on the opponent’s hitting behaviors. Specifically, we classify the opponent’s hitting behaviors into four categories: forehand attacking, forehand rubbing, backhand attacking, and backhand rubbing. A tailor-made mechanical structure that consists of a robot arm and a two-dimensional slide rail is developed such that the robot can reach large workspaces. Additionally, a visual module is incorporated to enable the robot to capture opponent motion sequences. Based on the opponent’s hitting behaviors and the predicted ball trajectory, smooth and stable control of the robot’s hitting motion can be obtained by applying quintic polynomial trajectory planning. Moreover, a motion control strategy is devised for the robot to return the ball to the desired location. Extensive experimental results are presented to demonstrate the effectiveness of the proposed strategy.

In industrial applications, many systems present serious productivity problems due to limited resources. Generally, the dynamics of resource allocation are inherently discrete-event driven, such as the buffer allocation in production line systems. In this paper, we develop a discrete-event mathematical model for resource allocation optimization. In this work, we consider two crucial optimization objectives, e.g., deadlock-free and efficiency, that originate from the customer’s actual requirements. The main aim is to develop a resource allocation scheme for fulfilling the production process (without deadlock) while ensuring that the cost of the process is minimized. As a case study, we consider the vehicle scheduling problem in a signal-free intersection. The intersection is divided into several disjoint spatial traffic resources, and vehicles need to occupy different traffic resources for passing through the intersection. Thus, the traffic control problem at the signal-free intersection is transformed into a scheduling problem with limited resource constraints. An online control approach is developed to schedule vehicles to go through the intersection safely and efficiently by optimizing the resource allocation order. Simulation results demonstrate the efficiency and robustness of the proposed model and optimization approach.

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.