DynGMP: Graph Neural Network-based Motion Planning in Unpredictable Dynamic Environments

Neural networks have already demonstrated attractive performance for solving motion planning problems, especially in static and predictable environments. However, efficient neural planners that can adapt to unpredictable dynamic environments, a highly demanded scenario in many practical applications, are still under-explored. To fill this research gap and enrich the existing motion planning approaches, in this paper, we propose DynGMP, a graph neural network (GNN)-based planner that provides high-performance planning solutions in unpredictable dynamic environments. By fully leveraging the prior exploration experience and minimizing the replanning cost incurred by environmental change, DynGMP achieves high planning performance and efficiency simultaneously. Empirical evaluations across different environments show that DynGMP can achieve close to 100% success rate with fast planning speed and short path cost. Compared with existing non-learning and learning-based counterparts, DynGMP shows very significant planning performance improvement, e.g., at least 2.7x, 2.2x, 2.4x and 2x faster planning speed with low path distance in four environments, respectively.