LiDAR based robot navigation improves safety and smoothness near workers
Learning Interaction-Aware Path Corrections from Unlabeled Trajectories: A LiDAR-Based Sim-to-Real GNN Framework for Construction Robots
Robotics
Summary
Construction robots need to move safely around people on busy and changing job sites. Current methods often rely on preset rules or require labeled examples, which limits how well they work in new situations. The authors created a way for robots to use only LiDAR sensors to learn how to adjust their paths by watching unlabeled movements of people and robots. Their method uses a graph neural network to understand interactions and improves the robot’s path smoothness and safety in tests.
What this means in practice
- •For construction robot operators: Enable robots to navigate safely and smoothly around workers using only LiDAR without needing labeled data or customized control rules.
- •For mobile robot developers: Incorporate interaction-aware path correction into robots operating in environments with unpredictable human movement using a planner-agnostic, real-time LiDAR system.
Authors
Yilong Chen, Yong K. Cho
Abstract
Construction robots operating near workers require navigation that can anticipate human motion while remaining robust to dynamic and unstructured site conditions. However, existing social navigation methods depend on hand-crafted interaction rules, labeled demonstrations, or robot-specific control policies, limiting their scalability and transferability in construction environments. We propose a real-time, LiDAR-only framework that learns interaction-aware path corrections from unlabeled demonstrations. Constant-velocity extrapolation generates residual learning targets, while a GNN models relationships among the robot, pedestrians, and goal context. During deployment, an extended Kalman filter tracks LiDAR detections to construct dynamic graphs, and the GNN offsets planner-generated waypoints through a planner-agnostic interface. Within the bricklaying trial, the GNN reduced path irregularity by 39.2\% and increased minimum TTC. Across Dynablox sequences, it increased minimum TTC by 38.3\% while producing shorter, smoother paths than the baselines. Ablation studies supported a 1.0-s prediction interval and confirmed the benefits of target context, reflection augmentation, and feature normalization.