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Multimodal trajectory optimization for motion planning
The International Journal of Robotics Research ( IF 9.2 ) Pub Date : 2020-06-04 , DOI: 10.1177/0278364920918296
Takayuki Osa 1, 2
Affiliation  

Existing motion planning methods often have two drawbacks: (1) goal configurations need to be specified by a user, and (2) only a single solution is generated under a given condition. In practice, multiple possible goal configurations exist to achieve a task. Although the choice of the goal configuration significantly affects the quality of the resulting trajectory, it is not trivial for a user to specify the optimal goal configuration. In addition, the objective function used in the trajectory optimization is often non-convex, and it can have multiple solutions that achieve comparable costs. In this study, we propose a framework that determines multiple trajectories that correspond to the different modes of the cost function. We reduce the problem of identifying the modes of the cost function to that of estimating the density induced by a distribution based on the cost function. The proposed framework enables users to select a preferable solution from multiple candidate trajectories, thereby making it easier to tune the cost function and obtain a satisfactory solution. We evaluated our proposed method with motion planning tasks in 2D and 3D space. Our experiments show that the proposed algorithm is capable of determining multiple solutions for those tasks.

中文翻译:

用于运动规划的多模态轨迹优化

现有的运动规划方法通常有两个缺点:(1)需要由用户指定目标配置,以及(2)在给定条件下只能生成一个解决方案。在实践中,存在多种可能的目标配置来完成一项任务。尽管目标配置的选择会显着影响最终轨迹的质量,但用户指定最佳目标配置并非易事。此外,轨迹优化中使用的目标函数往往是非凸的,它可以有多种解决方案,实现可比成本。在这项研究中,我们提出了一个框架,该框架确定与成本函数的不同模式相对应的多个轨迹。我们将识别成本函数模式的问题简化为基于成本函数估计由分布引起的密度的问题。所提出的框架使用户能够从多个候选轨迹中选择一个优选的解决方案,从而更容易调整成本函数并获得满意的解决方案。我们通过 2D 和 3D 空间中的运动规划任务评估了我们提出的方法。我们的实验表明,所提出的算法能够为这些任务确定多个解决方案。
更新日期:2020-06-04
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