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摘要:
针对未知环境下的多机器人协同搜索和源定位问题，提出一种基于Voronoi图的分布式协同区域搜索和覆盖方法。该方法考虑机器人的实际尺寸和定位误差引起的碰撞问题，根据每个机器人的定位不确定性半径构造Voronoi缓冲区域以保障安全性。利用稀疏高斯过程回归和引入不确定正则项的质心Voronoi划分(CVT)算法重建未知浓度场的分布，并进行协同覆盖；提出一种自适应环境探索策略，实现无先验信息下的环境探索。仿真实验表明：所提方法能够快速完成对未知环境的探索，并准确定位到污染源的位置。
Abstract:For the problem of multi-robot collaborative search and source localization in unknown environments, a distributed collaborative search and coverage method based on Voronoi diagrams is proposed. In order to assure safety, this method initially addresses collision problems caused by the physical dimensions and positioning faults of the robots by building Voronoi buffer zones based on each robot’s placement uncertainty radius. Utilizing sparse Gaussian process regression and the centroidal Voronoi tessellation (CVT) algorithm with an uncertainty regularization term, the distribution of the unknown concentration field is reconstructed to achieve collaborative coverage. An adaptive environmental exploration strategy is proposed to enable environment exploration without prior information. Simulation experiments demonstrate that this method can rapidly complete exploration of unknown environments and accurately locate the position of the pollution source.
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图 5 多污染源仿真初始环境
Figure 5. Initial environment for simulating multiple pollution sources
图 6 多源密度函数下的UCB-CVT算法仿真
Figure 6. Simulation of UCB-CVT algorithm under multiple source density function
图 7 UCB-CVT与G-CVT的评价指标对比
Figure 7. Comparison of evaluation metrics between UCB-CVT and G-CVT
图 10 加入探索策略与未加入探索策略的评价指标对比
Figure 10. Comparison of evaluation metrics with and without exploration strategy
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