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

翻译：人体运动具有随机性，在行人密集环境中确保机器人安全导航需要前瞻性决策。以往研究依赖于纳入周围行人的确定性未来状态，这可能因过度自信而导致机器人行为不安全。本文提出一种预测不确定性感知规划器，将基于神经网络的概率轨迹预测集成至规划过程中。本方法采用基于深度集成的网络对周围行人进行概率预测，并将预测不确定性作为约束条件整合至规划器。我们在规划器上比较了多种约束满足方法，并在真实世界行人数据集上评估了其性能。此外，在狭窄走廊内对分布外行人轨迹进行了离线机器人导航测试。