Prediction uncertainty-aware planning using deep ensembles and trajectory optimisation

TL;DR

This paper introduces a probabilistic planning approach that uses deep ensembles to predict pedestrian trajectories with uncertainty, enhancing safety in robot navigation amidst humans.

Contribution

It presents a novel uncertainty-aware planner integrating deep ensemble-based probabilistic predictions into trajectory optimization for safer robot navigation.

Findings

Deep ensembles improve pedestrian trajectory prediction accuracy.

Uncertainty constraints enhance navigation safety.

Method performs well on real-world pedestrian datasets.

Abstract

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