On the performance of selective adaptation in state lattices for mobile robot motion planning in cluttered environments
On the performance of selective adaptation in state lattices for mobile robot motion planning in cluttered environments
复制标题
杂乱环境中移动机器人运动规划的状态格选择性适应性能
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
T. Howard
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文献类型：
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作者：
Michael E. Napoli;Harel Biggie;T. Howard
自主移动的机器人需要运动规划算法，以匹配机载计算资源的限制，以安全地导航复杂的环境。在近似最优性优先于运行时性能的情况下，优化其局部连接以提高生成的解决方案的全局最优性的搜索空间是可取的。然而，并非搜索空间中的所有节点都同样受益于优化，这可能导致计算资源的低效使用和运行时间的不必要增加。为了解决这个问题，我们提出了一种方法，称为选择性自适应状态格，它使用一个启发式的本地环境的基础上选择性地执行优化，并获得运行时性能和相对最优性之间的平衡。我们提出了一个统计评估的局部连通性优化和全局搜索的状态格，自适应状态格，选择性自适应状态格算法在随机生成的障碍领域。我们在定性物理实验中进一步强调了每种方法在Clearpath Robotics TurtleBot 2上的性能。
Autonomous mobile robots require motion planning algorithms that match limitations of on-board computing resources to safely navigate complex environments. In situations where near-optimality is preferential to runtime performance, search spaces that optimize their local connectivity to improve the global optimality of generated solutions are desirable. However, not all nodes in the search space benefit equally from optimization which can result in an inefficient use of computational resources and unnecessary increase in runtime. To address this limitation, we propose an approach called the Selectively Adaptive State Lattice which uses a heuristic based on the local environment to selectively perform optimization and obtain a balance between runtime performance and relative optimality. We present a statistical evaluation of local connectivity optimization and global search with the State Lattice, Adaptive State Lattice, and Selectively Adaptive State Lattice algorithms in randomly generated obstacle fields. We further highlight the performance of each method on a Clearpath Robotics TurtleBot2 in a qualitative physical experiment.