BOMP让机器人更快更稳地从深箱中取放货物。
BOMP: Bin-Optimized Motion Planning
- 用深度网络预热优化，结合机器人参数和箱体尺寸规划路径。
- 比基线方法快58%，比行业算法快36%且成功率更高。
- 适合工业物流场景，尤其对快速、平滑运动有要求的机器人系统。
在物流领域，快速计算并执行从深箱中取放物品的机械臂动作对提升效率至关重要。本文提出Bin-Optimized Motion Planning（BOMP），一种针对六轴工业机器人配备长鼻吸盘工具的运动规划框架，用于从深箱中取出箱子。BOMP综合考虑机械臂运动学、驱动极限、抓取物尺寸及箱体环境的动态高度图，快速生成时间最优、加加速度受限且无碰撞的轨迹。优化过程通过离线训练的深度神经网络进行热启动，该网络基于25,000个模拟场景及其对应轨迹学习。在96个模拟和15个物理环境中测试表明，BOMP生成的轨迹比基于采样的基线方法快58%，比行业标准的Up-Over-Down算法快36%，且后者在此场景下成功率达仅15%。此外，BOMP生成的轨迹具备加加速度限制，而基线方法不具备此特性。
原文摘要 · Abstract (English)
In logistics, the ability to quickly compute and execute pick-and-place motions from bins is critical to increasing productivity. We present Bin-Optimized Motion Planning (BOMP), a motion planning framework that plans arm motions for a six-axis industrial robot with a long-nosed suction tool to remove boxes from deep bins. BOMP considers robot arm kinematics, actuation limits, the dimensions of a grasped box, and a varying height map of a bin environment to rapidly generate time-optimized, jerk-limited, and collision-free trajectories. The optimization is warm-started using a deep neural network trained offline in simulation with 25,000 scenes and corresponding trajectories. Experiments with 96 simulated and 15 physical environments suggest that BOMP generates collision-free trajectories that are up to 58 % faster than baseline sampling-based planners and up to 36 % faster than an industry-standard Up-Over-Down algorithm, which has an extremely low 15 % success rate in this context. BOMP also generates jerk-limited trajectories while baselines do not. Website: https://sites.google.com/berkeley.edu/bomp.
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