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This paper describes a switching formation strategy for multiple robots, in order to avoid an obstacle and cross obstacles. In the strategy, a leader robot plans a safe path using geometric obstacle avoidance control method (GOACM). By calculating a new desired distance and desired bearing angle with the leader robot, the follower robots follow the leader… (More)
This paper presents a novel, decentralized, control-theoretic approach to address collision avoidance for multi-robot systems. We create a virtual obstacle at the mean position of the robots. A control is be designed such that each robot will avoid the closest obstacle when a collision is possible. The closest obstacle can be the virtual obstacle or the… (More)
This paper presents a formation-control method based on virtual-space configuration for multi-robot, collective navigation. To maintain the configuration of a multiple-robot formation, each robot creates a virtual space composed of virtual robots around it, so that it can avoid collisions with, and keep a constant distance from, the other robots. In… (More)
This paper proposes a composite local path planning method for multi-robot formation navigation with path deviation prevention using a repulsive function, A-star algorithm, and unscented Kalman filter (UKF). The repulsive function in the potential field method is used to avoid collisions among robots and obstacles. The A-star algorithm helps the robots to… (More)
A novel, decentralized switched-system approach is proposed to address the problem of controlling multiple nonholonomic mobile robots to achieve a desired formation as well as heading consensus. The formation is induced by each robot following an attractive vector derived using a virtual, isomorphic graph. Then, a novel switching control law is designed… (More)
In this paper, we propose a multi-robot collaborative localization strategy for a group of mobile robots to enhance their formation-control and navigation performance even when the robots share only limited navigation information during indoor, multi-robot service applications.