In view of the shortcomings of the current path planning strategies for multiple robots, such as high path coupling, long total path, long waiting time for collision avoidance, and the resulting problems of low system robustness and low robot utilization, a multi-robot path planning algorithm based on 3D spatiotemporal maps and motion decomposition was proposed. Firstly, the dynamic temporary obstacles in time dimension were generated according to the existing path set and the current robots' positions, and were expanded into 3D search space together with the static obstacles. Secondly, in the 3D search space, the total time of path motion was divided into three parameters:motion time, turning time, and in-situ dwell time, and the conditional depth first search strategy was used to calculate the set of all paths from the starting node to the target node that met the parameter requirements. Finally, all paths in the path set were traversed. For each path, the actual total time consumption was calculated. If the difference between the actual total time consumption and the theoretical total time consumption of a path was less than the specified maximum error, the path was considered as the shortest path. Otherwise, the remaining paths were continued to traverse. And if the differences between the actual total time and the theoretical total time of all paths in the set were greater than the maximum error, the parameters needed to be adjusted dynamically, and then the initial steps of algorithm were continued to execute. Experimental results show that, the path planned by the proposed algorithm has the advantages of short total length, less running time, no collision and high robustness, and the proposed algorithm can solve the problem of completing continuous random tasks by multi-robot system.

Aiming at the problems of limited monitoring area and difficult target locating in video security surveillance system, a radar-guided video linkage monitoring model was established with the characteristics of wide radar monitoring range and freedom from optical conditions. On this basis, a target location algorithm and a multi-target selection algorithmm were proposed. Firstly, according to the target information detected by radar, the corresponding camera azimuth and pitch angle of a moving target in the system linkage model were automatically calculated so that the target could be accurately locked, monitored and tracked by camera in real-time. Then, with multiple targets appearing in the surveillance scene, the multi-target selecting algorithm was used for data weighted fusion of discrete degree of target, radial velocity of target and the distance between target and camera to select the target with the highest priority for intensive monitoring. The experimental results show that, the locating accuracy of the proposed target location algorithm for pedestrians and vehicles can reach 0.94 and 0.84 respectively, which can achieve accurate target location. Moreover, the proposed multi-target selection algorithm can effectively select the best monitoring target in complex environment, and has good robustness and real-time performance.