用不确定性感知规划，让机器人更安全避让行人。
Prediction uncertainty-aware planning using deep ensembles and trajectory optimisation
- 基于深度集成模型预测行人轨迹并量化不确定性
- 将预测不确定性作为约束条件提升导航安全性
- 在真实数据与罕见场景中验证了方法鲁棒性
人类运动具有随机性，在人流密集环境中保障机器人安全导航需主动决策。以往研究依赖确定性预测行人未来状态，易导致过度自信从而引发不安全行为。本文提出一种预测不确定性感知的规划方法，将基于神经网络的概率轨迹预测结果与不确定性信息融入规划过程。采用深度集成模型进行行人轨迹的不确定性建模，并将其作为约束输入规划器。比较了多种约束满足策略，评估了该方法在真实世界行人数据集上的性能。此外，还在狭窄走廊内对分布外行人轨迹进行了离线机器人导航实验。
原文摘要 · Abstract (English)
Human motion is stochastic and ensuring safe robot navigation in a pedestrian-rich environment requires proactive decision-making. Past research relied on incorporating deterministic future states of surrounding pedestrians which can be overconfident leading to unsafe robot behaviour. The current paper proposes a predictive uncertainty-aware planner that integrates neural network based probabilistic trajectory prediction into planning. Our method uses a deep ensemble based network for probabilistic forecasting of surrounding humans and integrates the predictive uncertainty as constraints into the planner. We compare numerous constraint satisfaction methods on the planner and evaluated its performance on real world pedestrian datasets. Further, offline robot navigation was carried out on out-of-distribution pedestrian trajectories inside a narrow corridor
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