Robotic navigation in unknown, cluttered environments with limited sensing capabilities poses significant challenges in robotics. Local trajectory optimization methods, such as Model Predictive Path Intergal (MPPI), are a promising solution to this challenge. However, global guidance is required to ensure effective navigation, especially when encountering challenging environmental conditions or navigating beyond the planning horizon. This study presents the GP-MPPI, an online learning-based control strategy that integrates MPPI with a local perception model based on Sparse Gaussian Process (SGP). The key idea is to leverage the learning capability of SGP to construct a variance (uncertainty) surface, which enables the robot to learn about the navigable space surrounding it, identify a set of suggested subgoals, and ultimately recommend the optimal subgoal that minimizes a predefined cost function to the local MPPI planner. Afterward, MPPI computes the optimal control sequence that satisfies the robot and collision avoidance constraints. Such an approach eliminates the necessity of a global map of the environment or an offline training process. We validate the efficiency and robustness of our proposed control strategy through both simulated and real-world experiments of 2D autonomous navigation tasks in complex unknown environments, demonstrating its superiority in guiding the robot safely towards its desired goal while avoiding obstacles and escaping entrapment in local minima. The GPU implementation of GP-MPPI, including the supplementary video, is available at https://github.com/IhabMohamed/GP-MPPI.

翻译：在感知能力受限的未知杂乱环境中进行机器人导航，是机器人领域面临的重要挑战。模型预测路径积分（MPPI）等局部轨迹优化方法为解决该问题提供了有前景的解决方案。然而，当遇到具有挑战性的环境条件或需要超出规划视界进行导航时，全局引导对于确保有效导航至关重要。本研究提出GP-MPPI，一种基于在线学习的控制策略，将MPPI与基于稀疏高斯过程（SGP）的局部感知模型相结合。其核心思想是利用SGP的学习能力构建方差（不确定性）曲面，使机器人能够学习其周围的可通行空间，识别一组建议子目标，最终向局部MPPI规划器推荐能使预定义代价函数最小化的最优子目标。随后，MPPI计算满足机器人运动约束和避碰约束的最优控制序列。该方法无需环境全局地图或离线训练过程。我们通过在复杂未知环境中进行的2D自主导航任务的仿真和真实实验，验证了所提控制策略的效率和鲁棒性，证明了其在引导机器人安全抵达目标、规避障碍物以及避免陷入局部极小值方面的优越性。GP-MPPI的GPU实现（包括补充视频）可从https://github.com/IhabMohamed/GP-MPPI获取。