One of the most frequent issues in multiple robot implementation is task allocation with the lowest path cost. Our study addresses the multi-robot task allocation challenge with path costs, the lowest computing time, ...
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One of the most frequent issues in multiple robot implementation is task allocation with the lowest path cost. Our study addresses the multi-robot task allocation challenge with path costs, the lowest computing time, and task distribution. Furthermore, it is usual for a robot's processing capabilities to be restricted to operate in various target environments. As a consequence, adequate processing power consumption would demonstrate the system's efficiency. Task allocation and path planning issues must be addressed regularly to ensure multi-robot system operation. Task allocation and path planning issues must be addressed regularly to ensure multi-robot system operation. The above-mentioned serious challenge gets more complicated when system factors such as robots and tasks multiply. As previously stated, this article solves the issue using a fuzzy-based optimum path and reverse auction-based methods. The detailed simulation results indicate that the suggested methods can solve task allocation with the lowest path cost. A comparative study is conducted between the suggested algorithm and two existing commonly used techniques, the auction-based and the Hungarian algorithms. Finally, the suggested method was run in real-time on a TurtleBot2 robot. The findings show the suggested algorithm's efficiency and simplicity of implementation.
This work develops a novel two-phase control framework that enables a swarm of compact spacecraft (agents), such as CubeSats and Nanosats, to autonomously capture tumbling and uncooperative targets. By leveraging dece...
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This work develops a novel two-phase control framework that enables a swarm of compact spacecraft (agents), such as CubeSats and Nanosats, to autonomously capture tumbling and uncooperative targets. By leveraging decentralized, bio-inspired swarm behavior control and distributed coordination strategies, the proposed system enables fully interchangeable agents to achieve robust, leaderless self-organization. During the capture, flocking behavior guides agents towards the target, while anti-flocking behavior enforces uniform dispersion of agents around it to provide full surface coverage and effective encapsulation prior to capture. A consensus-based protocol synchronizes the capture action among agents by allowing all agents to agree on a common action time. In this process, each agent autonomously identifies available capture points and participates in an auction-based allocation algorithm to collectively allocate optimal capture positions among agents. Simulation results validate the effectiveness of the proposed framework in autonomously capturing targets of various shapes, sizes and motion patterns, and demonstrate scalability across different swarm sizes. Overall, the proposed approach shows significant potential for coordinated, efficient, and robust swarm-based capture of uncooperative targets in space, offering benefits in scalability, adaptability, robustness, and cost-effectiveness.
Hybrid sensor networks consisting of both inexpensive static wireless sensors and highly capable mobile robots have the potential to monitor large environments at a low cost. To do so, an algorithm is needed to assign...
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ISBN:
(纸本)9781424466757
Hybrid sensor networks consisting of both inexpensive static wireless sensors and highly capable mobile robots have the potential to monitor large environments at a low cost. To do so, an algorithm is needed to assign tasks to mobile robots which minimizes communication among the static sensors in order to extend the lifetime of the network. We present three algorithms to solve this task allocation problem: a centralized algorithm, an auction-based algorithm, and a novel distributed algorithm utilizing a spanning tree over the static sensors to assign tasks. We compare the assignment quality and communication costs of these algorithms experimentally. Our experiments show that at a small cost in assignment quality, the distributed tree-basedalgorithm significantly extends the lifetime of the static sensor network.
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