用足迹清除度替代距离，让机器人更安全地避障导航
DCLP++: Learning to Navigate with Footprint Clearance and Relative Motion
- 以足迹清除距离为几何基础，构建动态环境下的相对运动特征
- 在20个移动障碍物场景中，足迹清除方法成功率提升至70%
- 适合研究机器人避障与运动规划的开发者参考
我们提出DCLP++，一种基于足迹清除度的局部导航框架。每个有效的激光雷达回波在互逆编码前，被映射到其与机器人实体占据区域的最短欧氏距离，取代传感器距离。通过径向测量或模拟平面相对速度，无需静态-动态标签即可提供短时程特征。初步研究采用矩形机器人，最大速度1米/秒，在20个移动障碍物环境中进行100个固定验证任务。训练20万次环境步后，传感器距离方法平均成功率42%，而足迹清除方法达70%。不同运动变体表现不一，增益混合。结果支持该清除度观测的有效性；运动收益的可靠性及跨机器人迁移仍需进一步评估。
原文摘要 · Abstract (English)
We present DCLP++, a local navigation frameworkthat uses footprint clearance as the geometric basis for studying relative motion features in dynamic environments. Each valid LiDAR return is mapped to its shortest Euclidean distance from the filled robot footprint before reciprocal encoding, replacing distance from the sensor with distance to the occupied body. Radial measurementsor simulated planar relative velocities provide short-horizon features without static-dynamic labels in the policy input. A preliminary study uses a rectangular robot with a speed limit of 1 m/s among 20 moving obstacles. On 100 fixed validation tasks, two selected training seeds yield mean success rates of 42% with sensor rangeand 70% with footprint clearance after 200,000 environment steps.Motion variants show mixed additional gains. These results supportthe clearance-based observation in the evaluated setting; reliable motion benefits and transfer across robots require further evaluation.
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