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A Centralized Strategy for Multi-Agent Exploration
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DOI：10.1109/ACCESS.2022.3218653.png)
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摘要
En 中文
This paper introduces recently developed Aquila Optimization Algorithm specifically configured for Multi-Robot space exploration. The proposed hybrid framework Coordinated Multi-Robot Exploration Aquila Optimizer (CME-AO) is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). A novel parallel communication protocol is also embedded to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed algorithm. Algorithm consistency aspects in achieving the expected results (area explored rate and time) is demonstrated through statistical means. A perspective analysis is then performed by comparing the performance of the CME-AO algorithm with latest state of art contemporary algorithms namely conventional CME and CME-WO (CME Whale Optimizer). The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. Results indicate that the proposed algorithm presents two distinct advantages over the other conventional state of the art CME based techniques a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time, with almost no fail runs. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other techniques either fails (stuck locally) or takes longer exploration time.
Keyword:
Multi-agent systems
Robot kinematics
Planning
Optimization
Path planning
Collision avoidance
Robot sensing systems
Artificial intelligence
Autonomous systems
Robotics
multiple agent system
space exploration
artificial intelligence
optimization algorithms
autonomous systems
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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