Target detecting and dynamic coverage are fundamental tasks in mobile robotics and represent two important features of mobile robots: mobility and perceptivity. This paper establishes the constrained motion model and sensor model of a mobile robot to represent these two features and defines the k -step reachable region to describe the states that the robot may reach. We show that the calculation of the k-step reachable region can be reduced from that of 2(k) reachable regions with the fixed motion styles to k + 1 such regions and provide an algorithm for its calculation. Based on the constrained motion model and the k -step reachable region, the problems associated with target detecting and dynamic coverage are formulated and solved. For target detecting, the k-step detectable region is used to describe the area that the robot may detect, and an algorithm for detecting a target and planning the optimal path is proposed. For dynamic coverage, the k-step detected region is used to represent the area that the robot has detected during its motion, and the dynamic-coverage strategy and algorithm are proposed. Simulation results demonstrate the efficiency of the coverage algorithm in both convex and concave environments.

The objective of self-healing in mobile robot networks is to maintain not only logical topology but also physical one of a network when robots fail. An interaction dynamics model is first established to describe both logical and physical topologies of the network. Considering the mobility of mobile robot networks, we propose a recursive, distributed topology control for self-healing when mobile robots fail, and give a metric of the topology structure for evaluating the performance of recovered network topologies. Then, we prove the stability of motion synchronization with the topology control based on Lyapunov exponent. Finally, the results of simulations have demonstrated the validity of the proposed modeling and control methods.

Motion synchronization in mobile robot networks is a fundamental task in distributed multi-robot collaboration. In this paper, we investigate the robustness of synchronous speeds against robot and communication failures, an important feature of distributed systems. A performance metric is proposed to represent the robustness of the robot teams based on complex networks theories. Moreover, a distributed topology control algorithm to improve the robustness metric of groups of robots by increasing the communication range temporarily is presented. When the new topology is constructed and is stable, the range for communicating is reduced to the original value. Finally, the results of simulation experiments have demonstrated the efficiency of the proposed control algorithm while robots and connections fail in a robot group

Using multi-robot has more advantages than using single robot for unknown environment exploration. But it brings a new problem of task allocation. Market-based method is an economic approach to allocating targets for robots through auction. However, it only considers costs in the local map of each robot. We update the local maps through fusion of both the local sensor data and the bid information, and thus the extended parts in maps enable robots to calculate costs of other robots’ targets. No extra communication is needed. The results of real robot experiments and simulations demonstrate that the improved method is more efficient than the original market-based approach and provide a proper improved method for environment exploration.