GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning
This paper presents a novel approach to multi-robot collision avoidance that integrates global path planning with local navigation strategies, utilizing attentive graph neural networks to manage dynamic interactions among agents. We introduce a local navigation model that leverages pre-planned global paths, allowing robots to adhere to optimal routes while dynamically adjusting to environmental changes. The models robustness is enhanced through the introduction of noise during training, resulting in superior performance in complex, dynamic environments. Our approach is evaluated against established baselines, including NH-ORCA, DRL-NAV, and GA3C-CADRL, across various structurally diverse ...
| Authors | Jonas le Fevre Sejersen, Toyotaro Suzumura, Erdal Kayacan |
|---|---|
| Primary category | cs.RO |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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