Simulation of Centralized Algorithms for Multi-Agent Path Finding on Real Robots

Simulace řešení multi-agentího hledání cest je nezbytná pro výzkum, ale také pro demonstrace v akademickém prostředí. Většinou se simulace pouze zobrazuje na obrazovce bez použití robotických agentů. Používají-li se roboty, obdrží posloupnost příkazů, které potřebují provést, nebo příkazy obdrží postupně, aby správně sledovaly své naplánované cesty. Tato práce navrhuje nový přístup k simulaci centralizovaných multi-agentných algoritmů pro hledání cest na fyzických agentech s názvem ESO-Nav. V tomhle přístupu agenti nejsou součástí plánovacího procesu, ani nemají o svých cestách žádné informace. Agenti mají jednoduché předdefinované chování v prostředí, v kterém navigují na základě jeho podnetů. Pro skupinu robotů Ozobot Evo byl implementován funkční prototyp simulátoru, který využívá tento nový přístup.The simulation of multi-agent pathfinding solutions is essential for research but also in educational demonstrations. Most of the time, the simulation is only displayed on a screen without the use of robotic agents. If robots are used, they get a sequence of commands they need to execute, or they receive the commands gradually, to follow their planned paths correctly. This work proposes a novel approach to simulation of centralized multi-agent pathfinding algorithms on physical agents called ESO-Nav. In this approach, the agents are not part of the planning process, nor do they have any information about their paths. The agents have a simple predetermined behavior in an environment and navigate in it based on the environment outputs. A working prototype of a simulator that utilizes this novel approach was implemented for a group of Ozobot Evo robots

Prioritized Multi-agent Path Finding for Differential Drive Robots

Methods for centralized planning of the collision-free trajectories for a fleet of mobile robots typically solve the discretized version of the problem and rely on numerous simplifying assumptions, e.g. moves of uniform duration, cardinal only translations, equal speed and size of the robots etc., thus the resultant plans can not always be directly executed by the real robotic systems. To mitigate this issue we suggest a set of modifications to the prominent prioritized planner -- AA-SIPP(m) -- aimed at lifting the most restrictive assumptions (syncronized translation only moves, equal size and speed of the robots) and at providing robustness to the solutions. We evaluate the suggested algorithm in simulation and on differential drive robots in typical lab environment (indoor polygon with external video-based navigation system). The results of the evaluation provide a clear evidence that the algorithm scales well to large number of robots (up to hundreds in simulation) and is able to produce solutions that are safely executed by the robots prone to imperfect trajectory following. The video of the experiments can be found at https://youtu.be/Fer_irn4BG0.Comment: This is a pre-print version of the paper accepted to ECMR 2019 (https://ieeexplore.ieee.org/document/8870957

La Course 12--4--90

National audienceAu commencement était la ligne, théorique et infinie, comme le temps. Cette ligne peut être vue comme une frise temporelle ou spatio-temporelle, comme une bande permettant à une « tête de lecture » de calculer ce qui est calculable. Elle peut être tracée physiquement au fur et à mesure afin de remplir l'espace de motifs improvisés ou calculés. Les performances proposées ici trouvent leurs origines à la fois dans les sciences et les arts afin de tisser des liens, des lignes de contact, entre les perceptions scientifiques et artistiques, trop souvent perçues comme disjointes. Nous verrons notamment comment lier le travail de Alan Turing à celui de Keith Haring, deux icônes scientifiques et artistiques majeures du XX ème siècle, lors d'interactions entre un petit robot Ozobot et un humain qui dessinent des trajectoires et « code-instructions » pour ce robot

A systematic literature review of multi-agent pathfinding for maze research

Multi-agent Pathfinding, also known as MAPF, is an Artificial Intelligence problem-solving. The aim is to direct each agent to find its path to reach its target, both individually and in groups. Of course, this path allows agents to move without colliding with each other. This MAPF application is implemented in many areas that require the movement of various agents, such as warehouse robots, autonomous cars, video games, traffic control, Unmanned Aerial Vehicles (UAV), Search and Rescue (SAR), many others. The use of multi-agent in exploring often assumes all areas to be explored are free of obstructions. However, the use of MAPF to achieve their goals often faces static barriers, and even other agents can also be considered dynamic barriers. Because it requires some constraints in the program, such as agents cannot collide with each other. The use of single-agent can find the shortest path through exploration. Still, multi-agent cooperation should shorten the time to find a target location, especially if there is more than one target. This paper explains the Systematic Literature Review (SLR) method to review research on various multi-agent pathfinding. The contribution of this paper is the analysis of multi-agent pathfinding and its potential application in solving maze problems based on an SLR