DCLP++: Learning to Navigate with Footprint Clearance and Relative Motion

2026-09-08

AI Summary

研究提出DCLP++框架，利用足迹清除和相对运动特性解决动态环境中的局部导航问题，通过LiDAR数据映射及策略学习提高导航成功率。

Abstract

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.

Problem

• local navigation
• footprint clearance
• relative motion
• dynamic environments

Innovation

• footprint clearance
• relative motion features
• local navigation framework
• dynamic environments