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

翻译：在未知和动态环境中高效导航对于拓展移动机器人的应用领域至关重要。核心挑战在于缺乏可行的全局路径来指导基于优化的局部规划器，导致现有局部规划常陷入不良局部极小值。本文提出一种新型优化器，能够在探索多种同伦类型的同时，规划长时域内的高质量轨迹，并保持足够快的实时应用速度。我们通过投影优化增强轨迹采样策略，将无梯度范式与可行区域引导相结合。该方法能从所有采样轨迹均位于高代价区域的常见病态案例中恢复。此外，我们的投影优化具有高度可并行化结构，易于在GPU上加速。我们在以下方面推动最新技术：在机器人操作系统（ROS）的导航框架中，成功率提升7-13%，总行程时间指标提升达两倍；在相同基准与指标下，相较MPPI及其近期变体改进达44%；在简单点对点导航任务中，本优化器的可靠性是现有最优梯度求解器、Cross-Entropy Method（CEM）及VPSTO等采样方法的2倍。代码：https://github.com/fatemeh-rastgar/PRIEST