24,731 research outputs found
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
Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning
Multi-robot path finding in dynamic environments is a highly challenging
classic problem. In the movement process, robots need to avoid collisions with
other moving robots while minimizing their travel distance. Previous methods
for this problem either continuously replan paths using heuristic search
methods to avoid conflicts or choose appropriate collision avoidance strategies
based on learning approaches. The former may result in long travel distances
due to frequent replanning, while the latter may have low learning efficiency
due to low sample exploration and utilization, and causing high training costs
for the model. To address these issues, we propose a path planning method,
MAPPOHR, which combines heuristic search, empirical rules, and multi-agent
reinforcement learning. The method consists of two layers: a real-time planner
based on the multi-agent reinforcement learning algorithm, MAPPO, which embeds
empirical rules in the action output layer and reward functions, and a
heuristic search planner used to create a global guiding path. During movement,
the heuristic search planner replans new paths based on the instructions of the
real-time planner. We tested our method in 10 different conflict scenarios. The
experiments show that the planning performance of MAPPOHR is better than that
of existing learning and heuristic methods. Due to the utilization of empirical
knowledge and heuristic search, the learning efficiency of MAPPOHR is higher
than that of existing learning methods
Multi-agent Collective Construction using 3D Decomposition
This paper addresses a Multi-Agent Collective Construction (MACC) problem
that aims to build a three-dimensional structure comprised of cubic blocks. We
use cube-shaped robots that can carry one cubic block at a time, and move
forward, reverse, left, and right to an adjacent cell of the same height or
climb up and down one cube height. To construct structures taller than one
cube, the robots must build supporting stairs made of blocks and remove the
stairs once the structure is built. Conventional techniques solve for the
entire structure at once and quickly become intractable for larger workspaces
and complex structures, especially in a multi-agent setting. To this end, we
present a decomposition algorithm that computes valid substructures based on
intrinsic structural dependencies. We use Mixed Integer Linear Programming
(MILP) to solve for each of these substructures and then aggregate the
solutions to construct the entire structure. Extensive testing on 200 randomly
generated structures shows an order of magnitude improvement in the solution
computation time compared to an MILP approach without decomposition.
Additionally, compared to Reinforcement Learning (RL) based and
heuristics-based approaches drawn from the literature, our solution indicates
orders of magnitude improvement in the number of pick-up and drop-off actions
required to construct a structure. Furthermore, we leverage the independence
between substructures to detect which sub-structures can be built in parallel.
With this parallelization technique, we illustrate a further improvement in the
number of time steps required to complete building the structure. This work is
a step towards applying multi-agent collective construction for real-world
structures by significantly reducing solution computation time with a bounded
increase in the number of time steps required to build the structure.Comment: Presented at the Multi-agent Path Finding Workshop at AAAI 202
Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming
The multi-agent path finding (MAPF) problem is a combinatorial search problem
that aims at finding paths for multiple agents (e.g., robots) in an environment
(e.g., an autonomous warehouse) such that no two agents collide with each
other, and subject to some constraints on the lengths of paths. We consider a
general version of MAPF, called mMAPF, that involves multi-modal transportation
modes (e.g., due to velocity constraints) and consumption of different types of
resources (e.g., batteries). The real-world applications of mMAPF require
flexibility (e.g., solving variations of mMAPF) as well as explainability. Our
earlier studies on mMAPF have focused on the former challenge of flexibility.
In this study, we focus on the latter challenge of explainability, and
introduce a method for generating explanations for queries regarding the
feasibility and optimality of solutions, the nonexistence of solutions, and the
observations about solutions. Our method is based on answer set programming.
This paper is under consideration for acceptance in TPLP.Comment: Paper presented at the 36th International Conference on Logic
Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September
2020, 16 pages, 6 figure
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