基于行为的多机器人探索优化策略

G. Fang, G. Dissanayake, H. Lau
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引用次数: 16

摘要

为了让机器人团队有效地探索未知环境,需要一个协调的策略来最大化探索区域。这是一个困难的优化问题,因为可能存在许多次优解。为了将搜索空间缩小到接近最优解的区域,使用基于行为的探索策略来定义可以找到最优解的区域。然后使用数值优化技术来找到该区域的解。特别地,提出的策略使用势场技术来获得每个机器人的粗运动方向。然后采用非线性优化方法计算速度和与粗方向的角度偏差,以实现每次移动的最大勘探。仿真结果表明,该方法提供了一种有效的勘探策略。
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A behaviour-based optimisation strategy for multi-robot exploration
To efficiently explore an unknown environment with a team of robots, a coordinated strategy that maximises the exploration area is required. This is a difficult optimisation problem, as there may exist many suboptimal solutions. In order to reduce the search space to a region that is near the optimal, a behaviour-based exploration strategy is used to define the region in which an optimal solution can be found. A numerical optimisation technique is then used to find the solution in this region. In particular, the proposed strategy uses a potential-fields technique to obtain a coarse movement direction for each robot. A nonlinear optimisation method is then used to calculate the velocity and angle deviation from the coarse direction to achieve the maximum exploration for each move. Simulation results have shown that the proposed method provides an efficient exploration strategy.
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