Reactive plan execution in multi-agent environments

Author(s):  
César Augusto Gúzman Álvarez
2019 ◽  
Vol 9 (23) ◽  
pp. 5180 ◽  
Author(s):  
Jaume Jordán ◽  
Javier Bajo ◽  
Vicent Botti ◽  
Vicente Julian

In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents’ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.


2008 ◽  
Vol 18 (2) ◽  
pp. 267-294 ◽  
Author(s):  
Femke de Jonge ◽  
Nico Roos ◽  
Cees Witteveen

Author(s):  
Cesar Guzman ◽  
Pablo Castejon ◽  
Eva Onaindia ◽  
Jeremy Frank
Keyword(s):  

Author(s):  
Femke de Jonge ◽  
Nico Roos ◽  
Cees Witteveen
Keyword(s):  

Author(s):  
Wolfgang Hönig ◽  
T. K. Satish Kumar ◽  
Liron Cohen ◽  
Hang Ma ◽  
Hong Xu ◽  
...  

Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, considers kinematic constraints, provides a guaranteed safety distance between robots, and exploits slack to avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.


Sign in / Sign up

Export Citation Format

Share Document