# Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent

@article{Henkel2020OptimizedDR, title={Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent}, author={Christian Henkel and Marc Toussaint}, journal={Proceedings of the 35th Annual ACM Symposium on Applied Computing}, year={2020} }

We present a novel approach called Optimized Directed Roadmap Graph (ODRM). It is a method to build a directed roadmap graph that allows for collision avoidance in multi-robot navigation. This is a highly relevant problem, for example for industrial autonomous guided vehicles. The core idea of ODRM is, that a directed roadmap can encode inherent properties of the environment which are useful when agents have to avoid each other in that same environment. Like Probabilistic Roadmaps (PRMs), ODRM…

## 7 Citations

### CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces

- Computer ScienceAAMAS
- 2022

CTRMs enable each agent to focus on its important locations around potential solution paths in a way that considers the behavior of other agents to avoid inter-agent collisions, while being augmented in the time direction to make it easy to derive a “timed” solution path.

### Avoidance Critical Probabilistic Roadmaps for Motion Planning in Dynamic Environments

- Computer Science2021 IEEE International Conference on Robotics and Automation (ICRA)
- 2021

This work proposes a self-supervised methodology for learning to identify regions frequently used for obstacle avoidance from local environment features and leverages a neural network to generate hierarchical probabilistic roadmaps termed Avoidance Critical Probabilistic Roadmaps (ACPRM).

### Automated Roadmap Graph Creation and MAPF Benchmarking for Large AGV Fleets

- Computer Science2022 8th International Conference on Automation, Robotics and Applications (ICARA)
- 2022

A concept and first evaluation of an automated topology creation and two different Multi-Agent Path Finding approaches for large robot fleets with the focus on logistic applications are presented.

### Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search

- Computer ScienceArtif. Intell.
- 2021

### Reinforcement-Learning-Based Route Generation for Heavy-Traffic Autonomous Mobile Robot Systems

- BusinessSensors
- 2021

The paper proposes a reinforcement learning approach where an agent builds the routes on a given layout while being rewarded according to different criteria based on the desired characteristics of the system to improve the operation and cooperation of multiple robots in their shared environment.

### Multilevel Motion Planning: A Fiber Bundle Formulation

- Computer ScienceArXiv
- 2020

The terminology of fiber bundles is introduced, in particular the notion of restrictions and sections, which is used to develop novel multilevel motion planning algorithms, which are called QRRT and QMP and shown to be probabilistically complete and almost-surely asymptotically optimal.

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