Rapidly-exploring Random Belief Trees for motion planning under uncertainty
Rapidly-exploring Random Belief Trees for motion planning under uncertainty
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DOI：
10.1109/icra.2011.5980508
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
A. Bry;N. Roy
中科院分区：
文献类型：
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作者：
A. Bry;N. Roy
在本文中，我们解决的问题，在存在状态不确定性的运动规划，也被称为规划的信念空间。这项工作的动机是规划领域，涉及非平凡的动态，空间变化的测量特性，和障碍物的限制。为了使问题易于处理，我们限制的运动计划的名义轨迹稳定的线性估计器和控制器。这使我们能够预测给定候选标称轨迹的未来状态的分布。使用这些分布来确保有界的碰撞概率，该算法通过状态空间增量地构建轨迹图，同时在每次迭代时通过该图有效地搜索候选路径。这个过程中的搜索树的信念空间，证明收敛到最优path.We理论分析的算法，也提供了模拟结果表明其效用的平衡信息收集，以减少不确定性，并找到低成本的路径。
In this paper we address the problem of motion planning in the presence of state uncertainty, also known as planning in belief space. The work is motivated by planning domains involving nontrivial dynamics, spatially varying measurement properties, and obstacle constraints. To make the problem tractable, we restrict the motion plan to a nominal trajectory stabilized with a linear estimator and controller. This allows us to predict distributions over future states given a candidate nominal trajectory. Using these distributions to ensure a bounded probability of collision, the algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration. This process results in a search tree in belief space that provably converges to the optimal path. We analyze the algorithm theoretically and also provide simulation results demonstrating its utility for balancing information gathering to reduce uncertainty and finding low cost paths.