Confidence-aware motion prediction for real-time collision avoidance1
Confidence-aware motion prediction for real-time collision avoidance1
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DOI：
10.1177/0278364919859436
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发表时间：
2020-03-01
影响因子：
9.2
通讯作者：
Tomlin, Claire J.
中科院分区：
文献类型：
--
作者：
Fridovich-Keil, David;Bajcsy, Andrea;Tomlin, Claire J.
机器人运动规划中最困难的挑战之一是考虑其他移动代理（如人类）的行为。通常，从业者使用预测模型来推断其他代理将移动到哪里。尽管最近在建立预测模型方面做了很多工作，但没有一个模型是完美的：智能体总是会以一种无法预测或没有分配足够概率的方式出人意料地移动。在这种情况下，机器人可能会规划看似安全但实际上会导致碰撞的轨迹。与其盲目地相信模型的预测，我们建议机器人应该使用模型当前的预测精度来告知其对未来预测的信心程度。这种模型置信度推理使我们能够生成概率运动预测，当结构成功地解释人类运动时，利用建模结构，并在人类意外移动时优雅地降级。我们通过在单个参数上保持贝叶斯信念来实现这一点，该参数控制着我们的人体运动模型的方差。我们将这种预测算法与最近提出的鲁棒运动规划器和控制器相结合，以指导机器人轨迹的构建，以良好的近似，具有高的用户指定概率的无碰撞。我们通过建立与可达性分析的连接，对组合方法及其整体安全特性进行了广泛的分析，并以硬件演示结束，其中小型四轴飞行器在与人类行人相同的空间中安全运行。
One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about where other agents are going to move. Though there has been much recent work in building predictive models, no model is ever perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability. In such cases, the robot may plan trajectories that appear safe but, in fact, lead to collision. Rather than trust a model's predictions blindly, we propose that the robot should use the model's current predictive accuracy to inform the degree of confidence in its future predictions. This model confidence inference allows us to generate probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly. We accomplish this by maintaining a Bayesian belief over a single parameter that governs the variance of our human motion model. We couple this prediction algorithm with a recently proposed robust motion planner and controller to guide the construction of robot trajectories that are, to a good approximation, collision-free with a high, user-specified probability. We provide extensive analysis of the combined approach and its overall safety properties by establishing a connection to reachability analysis, and conclude with a hardware demonstration in which a small quadcopter operates safely in the same space as a human pedestrian.