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It is necessary for robot-trailer to tow robot to the charging station for recharging when the robot fails, or the battery cannot support the robot to the charging station. However, the delay of towing robot is affecting the working efficiency of mobile robot. Based on the task priority of the mobile robot and impact degree on the room after the power failure, the paper proposes a distributed scheduling of robot being towed to recharge for reducing the delay expectation. This work designs a Distributed Three Nodes Service (DTNS) scheduling based on the communication between charging stations. The two-step path-planner based on decision factor and travel path is used in the scheme. Simulations show that the distributed scheduling of this work can well ensure the success communication in the case of low power, and DTNS can well reduce the delay expectation of towing robot to recharge. Compared with First Come First Service (FCFS) scheduling, DTNS reduces the towing delay by 48.71%, 48.83% and 40.45% when there are some robots sending the towing request, and by 58.77%, 39.97% and 41.90% when no robot sends request in the case of 1, 2 and 3 robot trailers in the service space respectively.

Data acquisition in large areas has issues of cost and data loss. When sensors are sparse in the physical field, it is critical to study the deployment methods to improve the accuracy of reconstructed data set and the precision of the recovery of lost data. It is desirable to place sensors at optimal locations to achieve higher precision of recovery. In this paper, we present a sparse sensor placement scheme for data interpolation reconstruction based on iterative four subregions using fractal theory. The results of our experiments demonstrate that the precision of our algorithm is higher than that with random placement in dispersion degree, coverage rate, and reconstruction accuracy.

Autonomous mobile service robots are used to complete many tasks, such as cleaning, transporting goods and monitoring. Such tasks usually require uninterrupted and continuous service. However, the battery of the robot is limited and must be charged frequently. For a large number of robots, it is essential to select a suitable charging pile. For this issue, we propose a path planning model for robots to intelligently access a limited number of charging piles distributed on the map. The traditional path planning model mainly considers the shortest path criterion to generate the path. Different from this, the path planning model in this paper not only considers the shortest path, but also the service position of the robot after charging, the remaining power of the robot, the state of the charging pile and the position of the robot in the map. Our path planning assigns the most suitable charging pile to the robot that needs charging. To solve the problem of high memory consumption and slow search speed when traditional A* algorithm is used for path planning, we propose local memorial path planning (LMPP) algorithm to quickly generate effective paths. The simulation results show that the proposed robot charging path planner can improve the robot service satisfaction and plan the effective path to the available charging piles.INDEX TERMS Autonomous mobile robot, service robot, path planning, robot charging