PRIEST: Projection Guided Sampling-Based Optimization For Autonomous Navigation

TL;DR

PRIEST introduces a projection-guided sampling optimization method for autonomous robot navigation, enabling exploration of multiple paths and recovery from poor samples, significantly improving success rates and efficiency in complex environments.

Contribution

The paper presents a novel gradient-free optimizer that guides sampling with projection, allowing real-time, long-horizon trajectory planning and better recovery from local minima.

Findings

Improves success rate by 7-13% in ROS navigation stack.

Achieves up to 44% better performance over MPPI and variants.

More reliable than SOTA gradient-based and sampling-based methods.

Abstract

Efficient navigation in unknown and dynamic environments is crucial for expanding the application domain of mobile robots. The core challenge stems from the nonavailability of a feasible global path for guiding optimization-based local planners. As a result, existing local planners often get trapped in poor local minima. In this paper, we present a novel optimizer that can explore multiple homotopies to plan high-quality trajectories over long horizons while still being fast enough for real-time applications. We build on the gradient-free paradigm by augmenting the trajectory sampling strategy with a projection optimization that guides the samples toward a feasible region. As a result, our approach can recover from the frequently encountered pathological cases wherein all the sampled trajectories lie in the high-cost region. Furthermore, we also show that our projection optimization has a highly parallelizable structure that can be easily accelerated over GPUs. We push the state-of-the-art in the following respects. Over the navigation stack of the Robot Operating System (ROS), we show an improvement of 7-13% in success rate and up to two times in total travel time metric. On the same benchmarks and metrics, our approach achieves up to 44% improvement over MPPI and its recent variants. On simple point-to-point navigation tasks, our optimizer is up to two times more reliable than SOTA gradient-based solvers, as well as sampling-based approaches such as the Cross-Entropy Method (CEM) and VPSTO. Codes: https://github.com/fatemeh-rastgar/PRIEST