Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration
This addresses the costly data collection bottleneck for machine learning in robotics, though it appears incremental as it builds on existing trajectory planning concepts.
The paper tackles the problem of efficient data collection for robotic applications by developing a Next-Best-Trajectory planning strategy for robot manipulators in dynamic environments, which achieves improved computational efficiency through GPU parallelization and demonstrates effectiveness in real-world experiments.
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.