Optimized Directed Roadmap Graph for Multi-Agent Path Finding Using Stochastic Gradient Descent

03/29/2020
by   Christian Henkel, et al.
2

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's first step is generating samples from C-space. In a second step, ODRM optimizes vertex positions and edge directions by Stochastic Gradient Descent (SGD). This leads to emergent properties like edges parallel to walls and patterns similar to two-lane streets or roundabouts. Agents can then navigate on this graph by searching their path independently and solving occurring agent-agent collisions at run-time. Using the graphs generated by ODRM compared to a non-optimized graph significantly fewer agent-agent collisions happen. We evaluate our roadmap with both, centralized and decentralized planners. Our experiments show that with ODRM even a simple centralized planner can solve problems with high numbers of agents that other multi-agent planners can not solve. Additionally, we use simulated robots with decentralized planners and online collision avoidance to show how agents are a lot faster on our roadmap than on standard grid maps.

READ FULL TEXT

page 2

page 5

page 6

page 7

research
10/24/2019

Reciprocal Collision Avoidance for General Nonlinear Agents using Reinforcement Learning

Finding feasible and collision-free paths for multiple nonlinear agents ...
research
07/01/2021

Distributed Multi-agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-agent Path Finding

Avoiding collisions is the core problem in multi-agent navigation. In de...
research
09/18/2019

SPARCAS: A Decentralized, Truthful Multi-Agent Collision-free Path Finding Mechanism

We propose a decentralized collision-avoidance mechanism for a group of ...
research
03/18/2021

Human-Inspired Multi-Agent Navigation using Knowledge Distillation

Despite significant advancements in the field of multi-agent navigation,...
research
04/19/2022

Multi-UAV Collision Avoidance using Multi-Agent Reinforcement Learning with Counterfactual Credit Assignment

Multi-UAV collision avoidance is a challenging task for UAV swarm applic...
research
01/16/2022

Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks

The multi-agent pickup and delivery (MAPD) problem, in which multiple ag...
research
10/23/2019

Decentralized Runtime Synthesis of Shields for Multi-Agent Systems

A shield is attached to a system to guarantee safety by correcting the s...

Please sign up or login with your details

Forgot password? Click here to reset