Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration
🤖 AI Summary
To address the challenges of information maximization and real-time collision avoidance for robotic manipulator visual observation and active exploration in dynamic environments, this paper proposes a hierarchical trajectory planning method based on Next-Best-Trajectory (NBT). The method employs a two-tier framework: a global layer generates reference trajectories guided by traversal optimality, while a local layer refines real-time trajectories via information-gain optimization. We introduce, for the first time, GPU-accelerated ray casting for voxel-based information gain estimation, significantly improving computational efficiency. The system integrates voxel mapping, collision-constrained kinematic solving, and real-time trajectory generation. Benchmark evaluations demonstrate an 8.3× speedup in information evaluation enabled by GPU acceleration. Real-robot experiments in unknown environments show a 37% improvement in 3D reconstruction completeness and a 29% increase in exploration coverage, achieving a favorable trade-off between real-time performance and perceptual quality.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Develops Next-Best-Trajectory planning for robot manipulators in dynamic environments
Maximizes observation information gain while avoiding collisions during exploration
Improves computational efficiency via GPU-parallelized raycasting for information estimation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Next-Best-trajectory planning for dynamic environments
Voxel mapping and GPU-accelerated raycasting
Global ergodic planner avoids local minima
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