PRIEST: Projection Guided Sampling-Based Optimization for Autonomous Navigation
IEEE ROBOTICS AND AUTOMATION LETTERS(2024)
摘要
Efficient navigation in unknown and dynamic environments is crucial for expanding the application domain of mobile robots. The core challenge stems from the non-availability 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 letter, 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 method can recover from the frequently encountered pathological cases wherein all the sampled trajectories lie in the high-cost region. We push the state-of-the-art (SOTA) 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 model predictive path integral (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.
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关键词
Autonomous vehicle navigation,collision avoidance,optimization and optimal control
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