Search Results for author:
Found 30 papers, 11 papers with code
ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem
The Combined Target-Assignment and Path-Finding (TAPF) problem, a variant of MAPF, requires one to simultaneously assign targets to agents and plan collision-free paths for agents.
Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
An exciting frontier in robotic manipulation is the use of multiple arms at once.
Improving Learnt Local MAPF Policies with Heuristic Search
Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations.
A Real-Time Rescheduling Algorithm for Multi-robot Plan Execution
One area of research in multi-agent path finding is to determine how replanning can be efficiently achieved in the case of agents being delayed during execution.
Caching-Augmented Lifelong Multi-Agent Path Finding
In this paper, we present a novel mechanism named Caching-Augmented Lifelong MAPF (CAL-MAPF), designed to improve the performance of Lifelong MAPF.
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints
To incorporate task assignment, path planning, and a user-defined objective into a coherent framework, this paper examines the Task Assignment and Path Finding with Precedence and Temporal Constraints (TAPF-PTC) problem.
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in four benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3000 agents.
Scalable Mechanism Design for Multi-Agent Path Finding
In this work, we introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms.
Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution
To overcome this issue, we introduce a new graphical representation called a Bidirectional Temporal Plan Graph (BTPG), which allows switching passing orders during execution to avoid unnecessary waiting time.
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2, 350 robots.
Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding
To tackle this issue, we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths.
Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree
As a leading approach to address TAPF, Conflict-Based Search with Target Assignment (CBS-TA) leverages both K-best target assignments to create multiple search trees and Conflict-Based Search (CBS) to resolve collisions in each search tree.
Multi-Robot Coordination and Layout Design for Automated Warehousing
We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability.
Cost Splitting for Multi-Objective Conflict-Based Search
Our theoretical results show that, when combined with either of these two new splitting strategies, MO-CBS maintains its completeness and optimality guarantees.
Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding
We present algorithms that build upon algorithmic techniques for the multi-agent path finding problem and solve the MG-TAPF problem optimally and bounded-suboptimally.
Multi-Goal Multi-Agent Pickup and Delivery
In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem, where agents constantly engage with new tasks and need to plan collision-free paths to execute them.
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner.
Multi-Robot Routing with Time Windows: A Column Generation Approach
We formulate the problem as a weighted set packing problem where the elements in consideration are items on the warehouse floor that can be picked up and delivered within specified time windows.
Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search
Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents.
Symmetry Breaking for k-Robust Multi-Agent Path Finding
During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by unexpected events.
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances.
Motion Planning
Robotics
Multiagent Systems
EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding
ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal.
Integer Programming for Multi-Robot Planning: A Column Generation Approach
We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit while respecting problem and robot specific constraints.
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions.
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks
The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other.
Searching with Consistent Prioritization for Multi-Agent Path Finding
We study prioritized planning for Multi-Agent Path Finding (MAPF).
Multi-Agent Path Finding with Deadlines
We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL).
Multi-Agent Path Finding with Deadlines: Preliminary Results
We formalize the problem of multi-agent path finding with deadlines (MAPF-DL).
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting.
