Search Results for author:
Found 27 papers, 8 papers with code
Large-Scale Multi-Robot Coverage Path Planning on Grids with Path Deconfliction
We study Multi-Robot Coverage Path Planning (MCPP) on a 4-neighbor 2D grid G, which aims to compute paths for multiple robots to cover all cells of G. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid H and then employ the Spanning Tree Coverage (STC) paradigm to generate paths on G, making them inapplicable to grids with partially obstructed 2x2 blocks.
MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation
We study a decentralized version of Moving Agents in Formation (MAiF), a variant of Multi-Agent Path Finding aiming to plan collision-free paths for multiple agents with the dual objectives of reaching their goals quickly while maintaining a desired formation.
MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping
The paper further makes benchmark contributions by 1) Improving processing code for existing datasets to produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks.
Multi-Robot Connected Fermat Spiral Coverage
We introduce the Multi-Robot Connected Fermat Spiral (MCFS), a novel algorithmic framework for Multi-Robot Coverage Path Planning (MCPP) that adapts Connected Fermat Spiral (CFS) from the computer graphics community to multi-robot coordination for the first time.
Large-Scale Multi-Robot Coverage Path Planning via Local Search
Existing graph-based MCPP algorithms first compute a tree cover on $G$ -- a forest of multiple trees that cover all vertices -- and then employ the Spanning Tree Coverage (STC) paradigm to generate coverage paths on the decomposed graph $D$ of the terrain graph $G$ by circumnavigating the edges of the computed trees, aiming to optimize the makespan (i. e., the maximum coverage path cost among all robots).
SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding
To tackle this challenge, we propose a multi-agent actor-critic method called Soft Actor-Critic with Heuristic-Based Attention (SACHA), which employs novel heuristic-based attention mechanisms for both the actors and critics to encourage cooperation among agents.
Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics
We investigate time-optimal Multi-Robot Coverage Path Planning (MCPP) for both unweighted and weighted terrains, which aims to minimize the coverage time, defined as the maximum travel time of all robots.
Double-Deck Multi-Agent Pickup and Delivery: Multi-Robot Rearrangement in Large-Scale Warehouses
We introduce a new problem formulation, Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD), which models the multi-robot shelf rearrangement problem in automated warehouses.
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.
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.
Graph-Based Multi-Robot Path Finding and Planning
Many variants of MAPF have been formalized to adapt MAPF techniques to different real-world requirements, such as considerations of robot kinematics, online optimization for real-time systems, and the integration of task assignment and path planning.
A Competitive Analysis of Online Multi-Agent Path Finding
We then show a counter-intuitive result that, if rerouting of previously-revealed agents is not allowed, any rational online MAPF algorithms, including ones that plan optimal paths for all newly-revealed agents, have the same asymptotic competitive ratio as the naive algorithm, even on 2D 4-neighbor grids.
Distributed Heuristic Multi-Agent Path Finding with Communication
The final trained policy is applied to each agent for decentralized execution.
Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers
In this paper, we study the one-shot and lifelong versions of the Target Assignment and Path Finding problem in automated sortation centers, where each agent needs to constantly assign itself a sorting station, move to its assigned station without colliding with obstacles or other agents, wait in the queue of that station to obtain a parcel for delivery, and then deliver the parcel to a sorting bin.
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.
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities.
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).
Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems
In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account.
AI Buzzwords Explained: Multi-Agent Path Finding (MAPF)
Explanation of the hot topic "multi-agent path finding".
Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments
Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations.
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.
Path Planning with Kinematic Constraints for Robot Groups
Path planning for multiple robots is well studied in the AI and robotics communities.
Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research.
Optimal Target Assignment and Path Finding for Teams of Agents
On the low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths.
Multi-Agent Path Finding with Delay Probabilities
Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search.
