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.

中文翻译：

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用于实时防撞的置信感知运动预测1

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（例如人类）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。尽管最近在构建预测模型方面有很多工作，但没有任何模型是完美的：代理总是可能以无法预测或未分配足够概率的方式意外移动。在这种情况下，机器人可能会规划出看似安全但实际上会导致碰撞的轨迹。我们建议机器人不应盲目相信模型的预测，而是应使用模型当前的预测准确性来告知其未来预测的置信度。这种模型置信推断使我们能够生成概率运动预测，当结构成功解释人体运动时利用建模结构，并在人体意外移动时优雅地降级。我们通过对控制人类运动模型方差的单个参数保持贝叶斯信念来实现这一点。我们将此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，这些轨迹非常近似，无碰撞，具有用户指定的高概率。我们通过建立与可达性分析的联系，对组合方法及其整体安全特性进行广泛分析，