机器人用新轨迹规划方法高效观察环境，避免碰撞并提升数据收集效率。
Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration
- 基于视角信息增益生成局部最优观测轨迹，兼顾安全与信息量。
- 利用体素地图与并行射线追踪实现高效环境建模与信息评估。
- 适合需要自主探索与高效感知的动态环境机器人任务。
视觉观察对物体重建、操作、导航和场景理解等机器人应用至关重要。尽管机器学习算法性能先进，但其训练依赖大量数据，采集成本高且耗时。自动化观测与探索策略能显著提升数据收集效率。为此，本文提出一种基于下一最佳轨迹（Next-Best-Trajectory）原则的机器人机械臂规划方法，适用于动态环境。通过体素地图建模环境，采用围绕兴趣点的射线投射估算信息增益，生成局部轨迹以最大化观测信息并避免碰撞。全局遍历轨迹规划器提供参考路径，帮助局部规划器规避局部极小值。为提升计算效率，信息增益的射线投射在图形处理器上并行执行。基准测试验证了并行化效果，真实实验展示了该策略的有效性。
原文摘要 · Abstract (English)
Visual observation of objects is essential for many robotic applications, such as object reconstruction and manipulation, navigation, and scene understanding. Machine learning algorithms constitute the state-of-the-art in many fields but require vast data sets, which are costly and time-intensive to collect. Automated strategies for observation and exploration are crucial to enhance the efficiency of data gathering. Therefore, a novel strategy utilizing the Next-Best-Trajectory principle is developed for a robot manipulator operating in dynamic environments. Local trajectories are generated to maximize the information gained from observations along the path while avoiding collisions. We employ a voxel map for environment modeling and utilize raycasting from perspectives around a point of interest to estimate the information gain. A global ergodic trajectory planner provides an optional reference trajectory to the local planner, improving exploration and helping to avoid local minima. To enhance computational efficiency, raycasting for estimating the information gain in the environment is executed in parallel on the graphics processing unit. Benchmark results confirm the efficiency of the parallelization, while real-world experiments demonstrate the strategy's effectiveness.
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