An evolutionary approach to swarm adaptation in dense environments
An evolutionary approach to swarm adaptation in dense environments
复制标题
密集环境中群体适应的进化方法
DOI：
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发表时间：
2010
期刊：
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通讯作者：
S. Hettiarachchi
中科院分区：
文献类型：
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作者：
S. Hettiarachchi
一群移动的智能体在未知环境中快速适应并达到目标的能力，同时避免障碍物和保持队形，在时间关键任务中是非常重要的。我们利用一个基于物理的自主代理框架结合我们的DAEDALUS范式，它允许代理从邻近的代理学习。在传统的方法中，一群智能体在模拟（离线）中结合进化/遗传算法学习任务，全局观测器优化群性能。在真实的世界（在线）中，智能体群体可能必须迅速适应不熟悉的环境。当没有全局观测器并且在线（真实的世界）环境与离线环境相比障碍物密集时，性能反馈可能被噪声延迟或扰动，并且在仿真（离线）中学习的规则可能不足以克服导航困难，使得群快速适应新环境。DAEDALUS是一个旨在解决这些问题的范例，通过更紧密地模仿在任务环境中移动和交互的代理群体的实际动态。本文提出了一种分析群体适应DAEDALUS在高障碍密度的环境中，代理的相互作用可能会受到阻碍的障碍。
The ability for a swarm of mobile agents to quickly adapt in unknown environments and reach a goal while avoiding obstacles and maintaining a formation is extremely important in time critical tasks. We utilize a physics-based autonomous agent framework combined with our DAEDALUS paradigm which allows the agents to learn from the neighboring agents. In traditional approaches, a swarm of agents learn the task in simulation(offline) combined with an evolutionary/genetic algorithm, and a global observer optimizes the swarm performance. In real world(online), the swarm of agents may have to rapidly adapt in unfamiliar environments. When there is no global observer and the online(real world) environment is dense with obstacles compared to offline environment, the performance feedback may be delayed or perturbed by noise, and the rules learned in simulation(offline) may not be sufficient to overcome the navigational difficulties, leaving the swarm to rapidly adapt in new environment. DAEDALUS is a paradigm designed to address these issues, by mimicking more closely the actual dynamics of populations of agents moving and interacting in a task environment. This paper presents an analysis of swarm adaptation using DAEDALUS in high obstacle density environments where agent interactions could be obstructed by obstacles.