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Sensor-based multi-robot coverage path planning problem is one of the challenging problems in managing flexible, computer-integrated, intelligent manufacturing systems. A novel pattern-based genetic algorithm is proposed for this problem. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a sequence of the disks for each robot to minimize the coverage completion time determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Robot turns cause the robot to slow down, turn and accelerate inevitably. Therefore, the actual travel time of a mobile robot is calculated based on the traveled distance and the number of turns. The algorithm is designed to handle routing and partitioning concurrently. Experiments are conducted using P3-DX mobile robots in the laboratory and simulation environment to validate the results.

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This paper presents a motion-planning and control scheme for a cooperative transportation system comprising a single rigid object and multiple autonomous non-holonomic mobile robots. A leader-follower formation control strategy is used for the transportation system in which the object is assumed to be the virtual leader; the robots carrying the object are considered to be followers. A smooth trajectory between the current and desired locations of the object is generated considering the constraints of the virtual leader. In the leader-follower approach, the origin of the coordinate system attached to the centre of gravity of the object, which is known as the virtual leader, moves along the generated trajectory while the real robots, which are known as followers, maintain a desired distance and orientation in relation to the leader. An asymptotically stable tracking controller is used for trajectory tracking. The proposed approach is verified by simulations and real applications using Pioneer P3-DX mobile robots.

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Sensor-based mobile robot coverage path planning (MRCPP) problem is a challenging problem in robotic management. We here develop a genetic algorithm (GA) for MRCPP problems. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a path which runs through the center of each disk at least once with minimal cost of full coverage. The proposed GA utilizes prioritized neighborhood-disk information to generate practical and high-quality paths for the mobile robot. Prioritized movement patterns are designed to generate efficient rectilinear coverage paths with no narrow-angle turn; they enable GA to find optimal or near-optimal solutions. The results of GA are compared with a well-known approach called backtracking spiral algorithm (BSA). Experiments are also carried out using P3-DX mobile robots in the laboratory environment.

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