CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots

-- This study presents the conflict-aware multi-agent estimated time of arrival (CAMET A) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined road infrastructure. The CAMET A framework consists of three components: a path planning layer generating potential path suggestions, a multi-agent ET A prediction layer predicting the arrival times for all agents based on the paths, and lastly, a path selection layer that calculates the accumulated cost and selects the best path. The novelty of the CAMET A framework lies in the heterogeneous map representation and the heterogeneous graph neural network architecture. As a result of the proposed novel structure, CAMET A improves the generalization capability compared to the state-of-the-art methods that rely on structured road infrastructure and historical data. The simulation results demonstrate the efficiency and efficacy of the multi-agent ET A prediction layer, with a mean average percentage error improvement of 29.5% and 44% when compared to a traditional path planning method ( A The performance of the CAMET A framework shows significant improvements in terms of robustness to noise and conflicts as well as determining proficient routes compared to state-of-the-art multi-agent path planners. Multi-agent path finding (MAPF) is the problem of generating valid paths for multiple agents while avoiding conflicts. This problem is highly relevant in many real-world applications, such as logistics, transportation, and robotics, where multiple agents must operate in a shared environment. MAPF is a challenging problem due to the need to find paths that avoid conflicts while minimizing the overall travel time for all agents. Many state-of-the-art MAPF solvers [1, 2, 3] employ various techniques to find a set of conflict-free paths on graphs representing the environment and the agents. However, a common limitation of these solvers is that they tend to generate tightly planned and coordinated paths. Therefore, the agents are expected to follow the exact path prescribed by the solver, which can lead to problems when applied to real-world systems with imperfect plan execution and uncertainties in the environment. This work introduces a conflict-aware multi-agent estimated time of arrival (CAMET A) for indoor autonomous mobile robot (AMR) applications that operate in time-constrained scenarios.