A bioinspired jumping robot has a strong ability to overcome obstacles. It can be applied to the occasion with complex and changeable environment, such as detection of planet surface, postdisaster relief, and military reconnaissance. So the bioinspired jumping robot has broad application prospect. The jumping process of the robot can be divided into three stages: takeoff, air posture adjustment, and landing buffer. The motivation of this review is to investigate the research results of the most published bioinspired jumping robots for these three stages. Then, the movement performance of the bioinspired jumping robots is analyzed and compared quantitatively. Then, the limitation of the research on bioinspired jumping robots is discussed, such as the research on the mechanism of biological motion is not thorough enough, the research method about structural design, material applications, and control are still traditional, and energy utilization is low, which make the robots far from practical applications. Finally, the development trend is summarized. This review provides a reference for further research of bioinspired jumping robots.

Aiming to solve the problem of role dynamic assignment in human–robot collaborative motion with unknown and changeable targets, a role dynamic assignment method based on target prediction and fuzzy inference is proposed. First, a human–robot collaborative motion framework based on role dynamic assignment is constructed. Then, based on the analysis of the human–robot collaborative motion with fixed role (HMFR), a human–robot collaborative motion target prediction module, a role dynamic assignment module, and a robot motion planning module are designed to realize the role dynamic assignment and motion planning of the robot. Finally, human–robot collaborative motion experiments based on role dynamic assignment are performed. Experimental results show that the proposed role dynamic assignment algorithm can effectively implement role dynamic assignments. Compared with the human–robot collaborative motion with fixed roles, the human–robot collaborative motion based on role dynamic assignment has better overall performance in terms of actual path and desired path similarity (Fréchet distance) and participant workload.

Admittance control of the robot is an important method to improve human–robot collaborative performance. However, it displays poor matching between admittance parameters and human–robot collaborative motion. This results in poor motion performance when the robot interacts with the changeable environment (human). Therefore, to improve the performance of human–robot collaboration, the human-like variable admittance parameter regulator (HVAPR) based on the change rate of interaction force is proposed by studying the human arm’s static and dynamic admittance parameters in human–human collaborative motion. HVAPR can generate admittance parameters matching with human collaborative motion. To test the performance of the proposed HVAPR, the human–robot collaborative motion experiment based on HVAPR is designed and compared with the variable admittance parameter regulator (VAPR). The satisfaction, recognition ratio, and recognition confidence of the two admittance parameter regulators are statistically analyzed via questionnaire. Simultaneously, the trajectory and interaction force of the robot are analyzed, and the performance of the human–robot collaborative motion is assessed and compared using the trajectory smoothness index and average energy index. The results show that HVAPR is superior to VAPR in human–robot collaborative satisfaction, robot trajectory smoothness, and average energy consumption.