Graph Neural Networks for Decentralized Multi-Robot Path Planning (1912.06095v2)

Abstract: Effective communication is key to successful, decentralized, multi-robot path planning. Yet, it is far from obvious what information is crucial to the task at hand, and how and when it must be shared among robots. To side-step these issues and move beyond hand-crafted heuristics, we propose a combined model that automatically synthesizes local communication and decision-making policies for robots navigating in constrained workspaces. Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among robots. We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations. We evaluate our method in simulations {by navigating teams of robots to their destinations in 2D} cluttered workspaces. We measure the success rates and sum of costs over the planned paths. The results show a performance close to that of our expert algorithm, demonstrating the validity of our approach. In particular, we show our model's capability to generalize to previously unseen cases (involving larger environments and larger robot teams).

• The paper demonstrates a novel framework leveraging GNNs with CNNs for decentralized multi-robot path planning that approximates expert algorithm performance.
• It details an architecture that processes local observations and uses multi-hop communication to enable effective inter-robot decision-making under limited connectivity.
• Simulated experiments reveal robust scalability and efficiency, with high success rates and competitive flowtime metrics across diverse environments and team sizes.

An Expert Examination of "Graph Neural Networks for Decentralized Multi-Robot Path Planning"

This paper contributes to the field of multi-robot path planning, addressing the challenges posed by decentralized operations in constrained environments. Traditional approaches to multi-agent pathfinding (MAPF) often rely on centralized strategies that fail to scale efficiently with the number of robots involved. These centralized approaches, while optimal in terms of complete data utilization, suffer from high computational complexity and are unsuitable for operations where robots lack a global reference frame and have limited communication capabilities. In response to these limitations, the authors propose a novel framework that leverages deep learning, specifically Graph Neural Networks (GNNs), to achieve decentralized multi-robot path planning.

Framework Overview

The proposed architecture integrates Convolutional Neural Networks (CNNs) to process local observations and GNNs to facilitate inter-robot communication. This combination allows the effective capture and dissemination of local information within robot communication ranges, enabling each robot to make informed decisions based on both its observations and those of its nearby peers. The GNN-based system is trained through imitation learning to emulate an expert algorithm. This offline training with global information is key to optimizing decentralized decisions during online operation.

The model's architecture ensures that the implementation is decentralized, with operations local to each robot. It applies a CNN to extract relevant features from each robot's field of vision, followed by communication of these features via a multi-hop GNN to share information across the team. A multi-layer perceptron (MLP) ultimately predicts the action policy based on the integrated features.

Results and Performance

Empirical results from simulated 2D environments demonstrate that the framework achieves performance near to that of the expert algorithm in terms of success rates and path efficiency. Specifically, the GNN-augmented framework generalizes well across different environment sizes and team configurations, showing robustness even with robot teams larger than those used for training.

The paper reports strong performance metrics, notably in success rates and flowtime metrics across various scenarios. Success rates indicate the proportion of cases where all robots reach their assigned destinations without collision within a set timeframe. The models also reported a low increase in flowtime compared to the expert planner, verifying that solutions remain competitive in terms of efficiency. Generalization tests further highlight the method's capability to handle previously unseen configurations, an essential feature for real-world deployments where environment variability cannot always be controlled.

Implications and Future Directions

This work-advances the use of graph neural networks in multi-agent systems, particularly in applications where decentralized operation is a necessity, not a choice. The leveraging of GNNs promises scalable path planning strategies that mitigate the scalability limitations inherent in traditional approaches. By allowing decentralized agents to communicate effectively and make cooperative decisions, the proposed model adapts well to larger teams, suggesting practical utility in complex, dynamic environments like warehouse robotics, swarming drones, and search-and-rescue missions.

Future work could explore enhancing decision accuracy in environments with varying communication delays and further optimizations to prevent deadlock situations. Furthermore, the integration of reinforcement learning techniques may complement the imitation-based training, providing adaptive policies when faced with novel or evolving task specifications. Given the growing scale and complexity of robotic operations, the framework's promise in achieving scalable autonomy offers significant potential for innovation within various AI-driven sectors.