COMPASS通过主动感知策略，提升狭窄空间内机械臂的抓取成功率。
COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy
- 基于多目标效用函数选择兼具信息量与操作便利性的视角。
- 在仿真中相比传统方法提升24.25%的抓取成功率。
- 适合需要主动感知的复杂狭窄环境机器人任务。
在受限且杂乱环境中进行操作仍面临显著挑战，主要源于观测不全和复杂的配置空间。实现有效操作需智能探索策略以安全地理解场景并定位目标。本文提出COMPASS，一种多阶段探索与操作框架，包含面向操作的采样规划器。首先，通过近场感知扫描降低碰撞风险，构建局部碰撞地图；其次，采用多目标效用函数寻找既具信息性又利于后续操作的观察视角；此外，实施约束操作优化策略生成满足障碍物约束的操作姿态。为系统评估该方法在上述困难下的表现，我们构建了一个包含四个难度等级的受限空间探索与操作基准测试集。仿真结果表明，相较于仅考虑信息增益的其他机器人探索方法，本框架在模拟中将操作成功率提升24.25%。真实世界实验验证了该方法在受限环境中实现主动感知与操作的能力。
原文摘要 · Abstract (English)
Manipulation in confined and cluttered environments remains a significant challenge due to partial observability and complex configuration spaces. Effective manipulation in such environments requires an intelligent exploration strategy to safely understand the scene and search the target. In this paper, we propose COMPASS, a multi-stage exploration and manipulation framework featuring a manipulation-aware sampling-based planner. First, we reduce collision risks with a near-field awareness scan to build a local collision map. Additionally, we employ a multi-objective utility function to find viewpoints that are both informative and conducive to subsequent manipulation. Moreover, we perform a constrained manipulation optimization strategy to generate manipulation poses that respect obstacle constraints. To systematically evaluate method's performance under these difficulties, we propose a benchmark of confined-space exploration and manipulation containing four level challenging scenarios. Compared to exploration methods designed for other robots and only considering information gain, our framework increases manipulation success rate by 24.25% in simulations. Real-world experiments demonstrate our method's capability for active sensing and manipulation in confined environments.
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