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This paper presents a new approach to formation forming of multiple mobile robots with decentralized motion planning. When the robots enter the required formation, there exists the formation-structure constraint, which causes disorder or even deadlock of the formation. A dynamic priority strategy is developed to solve the problem of the formation-structure constraint, and coordinate the robots to...
This paper presents a decentralized motion planning methodology for multiple mobile robots. A novel three-step local-constraint-path planner (LCPP) is proposed to perform path planning for each robot while considering kinematic constraint and online implement efficiency. A priority-based strategy is employed in the planning to avoid mutual collisions amongst the robots. Simulations are performed on...
In this paper, a novel computation method named MMK (Multi-sensory Markov-Kalman) localization, is proposed for self-localization of mobile robots in polygonal environments. The method combines multimodal robust Markov localization and unimodal efficient KF method, and is much improved by utilizing multi-sensory information. The localization process requires less memory and lower resolution discretization...
As a probabilistic localization algorithm, Monte Carlo localization (MCL) method has been widely used for mobile robot localization over the past decade. In this paper, an extended MCL method (EMCL) is developed by incorporating two different resampling processes, namely importance resampling and sensor-based resampling, to conventional MCL for improvement of localization performance. Different resampling...
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