Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived, with many of them explicitly requiring objects or persons to be within view, i.e., for monitoring or safety. However, solving motion and perception tasks simultaneously is challenging, as these objectives often impose conflicting requirements. Furthermore, while robots must react quickly to changes in the environment, directly evaluating the quality of perception (e.g., object detection confidence) is often expensive or infeasible at runtime. This problem is especially important in human-centered environments, such as homes and hospitals, where effective perception is essential for safe and reliable operation. In this work, we address the challenge of solving motion planning problems for high-degree-of-freedom (DoF) robots from a start to a goal configuration with continuous perception constraints under both static and dynamic environments. We propose a GPU-parallelized perception-score-guided probabilistic roadmap planner with a neural surrogate model (PS-PRM). Unlike existing active perception-, visibility-aware or learning-based planners, our work integrates perception tasks and constraints directly into the motion planning formulation. Our method uses a neural surrogate model to approximate perception scores, incorporates them into the roadmap, and leverages GPU parallelism to enable efficient online replanning in dynamic settings. We demonstrate that our planner, evaluated on high-DoF robots, outperforms baseline methods in both static and dynamic environments in both simulation and real-robot experiments.

翻译：动态空间中机器人的大多数常见任务都需要对环境进行定期主动感知，其中许多任务明确要求物体或人员处于视野范围内，例如用于监控或安全目的。然而，同时解决运动与感知任务具有挑战性，因为这些目标通常存在相互冲突的要求。此外，尽管机器人必须对环境变化做出快速反应，但直接评估感知质量（例如物体检测置信度）在运行时往往计算成本高昂或难以实现。这一问题在以人为中心的环境（如家庭和医院）中尤为重要，因为有效的感知对于安全可靠的操作至关重要。在本研究中，我们解决了高自由度机器人从起始构型到目标构型的运动规划问题，该问题需在静态和动态环境下满足连续感知约束。我们提出了一种基于神经代理模型的GPU并行化感知分数引导概率路线图规划器。与现有的主动感知规划器、可见性感知规划器或基于学习的规划器不同，我们的工作将感知任务与约束直接集成到运动规划框架中。该方法使用神经代理模型近似感知分数，将其纳入路线图构建，并利用GPU并行性实现动态环境下高效的在线重规划。我们通过仿真和真实机器人实验证明，在高自由度机器人上评估的规划器在静态和动态环境中均优于基线方法。