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Physics-based robot motion planning in dynamic multi-body environments.

机译：动态多体环境中基于物理的机器人运动计划。

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摘要

Traditional motion planning focuses on the problem of safely navigating a robot through an obstacle-ridden environment. In this thesis, we address the question of how to perform robot motion planning in complex domains, with goals that go beyond collision-free navigation. Specifically, we are interested in problems that impose challenging constraints on the intermediate states of a plan, and problems that require the purposeful manipulation of non-actuated bodies, in environments that contain multiple, physically interacting bodies with varying degrees of controllability and predictability. Examples of such domains include physical games, such as robot soccer, where the controlled robot has to deliver the ball into the opponent's goal. For these domains, navigation only constitutes a small part of the overall planning problem. Additional planning challenges include accurately modeling and exploiting the dynamic interactions with other non-actuated bodies (e.g., dribbling a ball), and the problem of predicting and avoiding foreign-controlled bodies (e.g., opponent robots).;To plan in such domains, this thesis introduces physics-based planning methods, relying on rich models that aim to reflect the detailed dynamics of the real physical world. We introduce non-deterministic Skills and Tactics as an intelligent action sampling model for effectively reducing the size of the searchable action space. We contribute two efficient Tactics-driven planning algorithms, BK-RRT and BK-BGT, and we evaluate their performance across several challenging domains. We contribute a physics model parameter optimization method for increasing the planner's physical prediction accuracy, resulting in significantly improved real-world execution success rates. Additionally, we contribute Variable Level-Of-Detail (VLOD) planning, a method for reducing overall planning time in uncertain multi-body execution environments.;Besides relying on an extensive simulated testbed, we apply and evaluate our planning approaches in two challenging real-world robot domains. We contribute the robot minigolf domain, where a robot uses physics-based planning methods to solve freely configurable minigolf-like courses, e.g., by purposefully bouncing a ball off from obstacles. We furthermore contribute a robot soccer attacker behavior that uses physics-based planning to out-dribble opponents, which has been successfully tested as part of the "CMDragons" robot soccer Small Size League team at the BoboCup world cup in 2009.
机译：传统的运动计划着重于安全地使机器人在充满障碍的环境中导航的问题。在本文中，我们解决了如何在复杂领域中执行机器人运动计划的问题，其目标超出了无碰撞导航的范围。具体而言，我们对在计划的中间状态施加挑战性约束的问题以及在包含多个具有不同程度可控性和可预测性的物理交互体的环境中需要对非驱动体进行有目的操纵的问题感兴趣。这样的领域的例子包括物理游戏，例如机器人足球，其中受控机器人必须将球传送到对手的球门中。对于这些领域，导航仅占总体规划问题的一小部分。其他计划挑战包括：准确建模和利用与其他未操纵物体（例如运球）的动态交互，以及预测和避免外来控制物体（例如对手机器人）的问题。本文介绍了基于物理的计划方法，并依靠丰富的模型来反映真实物理世界的详细动态。我们引入非确定性技能和战术作为智能动作采样模型，以有效减小可搜索动作空间的大小。我们提供了两种有效的Tactics驱动的计划算法BK-RRT和BK-BGT，并且我们评估了它们在多个挑战性领域中的表现。我们提供了一种物理模型参数优化方法来提高计划者的物理预测准确性，从而显着提高了实际执行成功率。此外，我们还提供了可变详细程度（VLOD）计划，这是一种在不确定的多体执行环境中减少总体计划时间的方法。除了依靠广泛的模拟测试台之外，我们还在两个具有挑战性的真实环境中应用和评估了我们的计划方法-world机器人领域。我们为机器人迷你高尔夫领域做出了贡献，其中机器人使用基于物理学的规划方法来解决可自由配置的迷你高尔夫式路线，例如通过有目的地将球从障碍物弹起。我们还贡献了一种机器人足球攻击者的行为，该行为使用基于物理的计划来运球对手，该行为已成功作为2009年BoboCup世界杯“ CMDragons”机器人足球小型联赛团队的一部分进行了测试。

著录项

  • 作者

    Zickler, Stefan.;

  • 作者单位

    Carnegie Mellon University.;

  • 授予单位 Carnegie Mellon University.;
  • 学科 Engineering Robotics.;Computer Science.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 188 p.
  • 总页数 188
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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