Map-Predictive Motion Planning in Unknown Environments

TL;DR

This paper introduces a unified, data-driven approach that combines map prediction with motion planning to enable safe, efficient navigation in unknown environments, outperforming traditional frontier-based methods in speed and computational efficiency.

Contribution

The paper presents a novel map-predictive motion planning method that integrates environment prediction with trajectory planning, eliminating the need for frontier selection.

Findings

Significant reduction in trajectory planning time compared to naive methods.

Comparable performance to advanced frontier heuristics with less computation.

Improved navigation efficiency in unknown environments.

Abstract

Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient autonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot's observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot's position to the goal without requiring frontier selection. We demonstrate that our map-predictive motion planning strategy yields a substantial improvement in trajectory time over a naive frontier pursuit method and demonstrates similar performance to methods using more sophisticated frontier selection heuristics with significantly shorter computation time.