In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots.

Heavy-duty legged robots have played an important role in material transportation, planet exploration, and other fields due to their unique advantages in complex and harsh terrain environments. The instability phenomenon of the heavy-duty legged robots often arises during the dynamic interactions between the supporting feet and the intricate terrains, which significantly impact the ability of the heavy-duty legged robots to move rapidly and accomplish tasks. Therefore, it is necessary to assess the mechanical behavior of foot–terrain interactions for the heavy-duty legged robots. In order to achieve the above goal, a systematic literature review methodology is employed to examine recent technical scientific publications, aiming to identify both current and prospective research fields. The characteristics of supporting feet for different heavy-duty legged robots are compared and analyzed. The foot–terrain mechanical models of the heavy-duty legged robots are discussed. The problems that need further research are summarized and presented, which is conducive to further deepening and expanding the research on the mechanical behavior of foot–terrain interactions for heavy-duty legged robots.

The combination of gesture recognition and aerospace exploration robots can realize the efficient non-contact control of the robots. In the harsh aerospace environment, the captured gesture images are usually blurred and damaged inevitably. The motion blurred images not only cause part of the transmitted information to be lost, but also affect the effect of neural network training in the later stage. To improve the speed and accuracy of motion blurred gestures recognition, the algorithm of YOLOv4 (You Only Look Once, vision 4) is studied from the two aspects of motion blurred image processing and model optimization. The DeblurGanv2 is employed to remove the motion blur of the gestures in YOLOv4 network input pictures. In terms of model structure, the K-means++ algorithm is used to cluster the priori boxes for obtaining the more appropriate size parameters of the priori boxes. The CBAM attention mechanism and SPP (spatial pyramid pooling layer) structure are added to YOLOv4 model to improve the efficiency of network learning. The dataset for network training is designed for the human–computer interaction in the aerospace space. To reduce the redundant features of the captured images and enhance the effect of model training, the Wiener filter and bilateral filter are superimposed on the blurred images in the dataset to simply remove the motion blur. The augmentation of the model is executed by imitating different environments. A YOLOv4-gesture model is built, which collaborates with K-means++ algorithm, the CBAM and SPP mechanism. A DeblurGanv2 model is built to process the input images of the YOLOv4 target recognition. The YOLOv4-motion-blur-gesture model is composed of the YOLOv4-gesture and the DeblurGanv2. The augmented and enhanced gesture data set is used to simulate the model training. The experimental results demonstrate that the YOLOv4-motion-blur-gesture model has relatively better performance. The proposed model has the high inclusiveness and accuracy recognition effect in the real-time interaction of motion blur gestures, it improves the network training speed by 30%, the target detection accuracy by 10%, and the value of mAP by about 10%. The constructed YOLOv4-motion-blur-gesture model has a stable performance. It can not only meet the real-time human–computer interaction in aerospace space under real-time complex conditions, but also can be applied to other application environments under complex backgrounds requiring real-time detection.

Aiming at the formation and maintenance of the multiple formations of nonholonomic constrained multi-robots, a leader-follower formation control method under the grouping consistency is proposed on the trajectory tracking of a nonholonomic constrained mobile robots with the low convergence time. The distributed control structure in the leader-follower formation is adopted. The multi-robot cooperative formation is realized by using the consistency algorithm of graph theory. According to the graph theory, the communication topology matrixes are designed by the consistency algorithm. The mathematical model of nonholonomic constrained robot is established with the wheeled structure as the mobile structure under the nonholonomic constraints. Then the navigation following model is transformed into the error model of a local coordinate system through the global coordinate transformation. The formation control law of multi-robot cooperative motion is put forward based on the leader-follower model. Its convergence is proved by the Lyapunov function. By setting the reasonable communication protocol parameters, the MATLAB software (Natick, MA, USA, R2016b) is employed on the simulation verification and result comparison. Through the comparison of the two leader formation control methods, the convergence time of the algorithm in this article can be 25% less than that of PFC. The effectiveness and feasibility of the formation control law are verified under the leader-follower method. The proposed control method lays a foundation for reducing the convergence time to improve the multi-robot cooperative motion under nonholonomic constraints.

The articulated rotating speed is one of the important parameters to determine the drive devices and actuating devices of joints for legged robots. Compared with the small-scale multi-legged robots, the range of the output speed of the joint should be as accurate as possible for the large-load-ratio multi-legged robots. To reasonably select the devices of joints, the maximum walking speed method is proposed to quickly and accurately obtain the range of articulated rotating speed by taking an electrically driven large-load-ratio six-legged robot as an example. To prove the rapidity, accuracy, and conciseness of the maximum walking speed method, the analyses of the forward kinematics and inverse kinematics of the robot are implemented based on the Denavit-Hartenberg method. However, only one range of articulated rotating speed is effectively confirmed in a single leg. Through rotating one of the joints to achieve the maximum speed index of the robot, the maximum walking speed method is employed to establish the mathematical relationships between the articulated rotating angles and the maximum walking speed index of the robot. The ranges of the output speeds of all joints are accurately obtained. The simulation verification and walking experiments of the prototype are, respectively, carried out. The results of the simulation and walking experiments show that the maximum walking speed method is reasonable and effective in calculating the range of articulated rotating speed. The proposed method in this paper can be reliably applied to the development of large-load-ratio multi-legged robots.

Compared to the traditional non-load-bearing multi-legged robots, the heavy-duty multi-legged robots typically not only have larger body weight, larger volume, and larger load ratio but also require greater energy dissipation. Traditional path planning often focuses on the problem of finding the shortest path. However, the substantial load capacity and multi-jointed structure of heavy-duty multi-legged robots impose stringent requirements on path smoothness. Consequently, the smoothness requirement makes the traditional A* algorithm unsuitable for applications where low-energy operation is critical. An improved low-energy path planning method based on the A* algorithm is presented for an electrically driven heavy-duty six-legged robot. Then, the environment is discretized by using the grid method to facilitate path searching. To address the path zigzagging problem caused by the traditional A* algorithm, the Bézier curve smoothing technique is adopted. The continuous curvature transitions are employed to significantly improve the smoothness of path. The heuristic function in the A* algorithm is enhanced through a dynamic weight adjustment mechanism. The nonlinear suppression strategy is introduced to prevent data changes and improve the robustness of the algorithm. The effectiveness of the proposed method is verified through the MATLAB simulation platform system. The simulation experiments show that, in various environments with different obstacle densities (0.17–0.37%), compared with the traditional A* algorithm, the method proposed in this paper reduces the average path length by 7.2%, the number of turning points by 25.9%, and the energy consumption by 5.75%. The proposed improved A* algorithm can significantly overcome the problem of insufficient smoothness in traditional A* algorithms and reduce the number of nodes generated by the control data stack, which improves the optimization efficiency during path planning. As a result, the heavy-duty six-legged robots can walk farther and operate for longer periods of time while carrying the limited energy sources.