Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders
for More Efficient Multi-Agent Path Finding Plan Execution
- URL: http://arxiv.org/abs/2401.00315v2
- Date: Sun, 7 Jan 2024 01:23:49 GMT
- Title: Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders
for More Efficient Multi-Agent Path Finding Plan Execution
- Authors: Yifan Su, Rishi Veerapaneni, Jiaoyang Li
- Abstract summary: We introduce a new graphical representation called a Bidirectional Temporal Plan Graph (BTPG) which allows switching orders during execution to avoid unnecessary waiting time.
Experimental results show that following BTPGs consistently outperforms following TPGs, reducing unnecessary waits by 8-20%.
- Score: 7.2988406091449605
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Multi-Agent Path Finding (MAPF) problem involves planning collision-free
paths for multiple agents in a shared environment. The majority of MAPF solvers
rely on the assumption that an agent can arrive at a specific location at a
specific timestep. However, real-world execution uncertainties can cause agents
to deviate from this assumption, leading to collisions and deadlocks. Prior
research solves this problem by having agents follow a Temporal Plan Graph
(TPG), enforcing a consistent passing order at every location as defined in the
MAPF plan. However, we show that TPGs are overly strict because, in some
circumstances, satisfying the passing order requires agents to wait
unnecessarily, leading to longer execution time. 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. We design two anytime algorithms for constructing a
BTPG: BTPG-na\"ive and BTPG-optimized. Experimental results show that following
BTPGs consistently outperforms following TPGs, reducing unnecessary waits by
8-20%.
Related papers
- Plan-over-Graph: Towards Parallelable LLM Agent Schedule [53.834646147919436]
Large Language Models (LLMs) have demonstrated exceptional abilities in reasoning for task planning.
This paper introduces a novel paradigm, plan-over-graph, in which the model first decomposes a real-life textual task into executable subtasks and constructs an abstract task graph.
The model then understands this task graph as input and generates a plan for parallel execution.
arXiv Detail & Related papers (2025-02-20T13:47:51Z) - Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following [62.10809033451526]
This work focuses on building a task planner for Embodied Instruction Following (EIF) using Large Language Models (LLMs)
We frame the task as a Partially Observable Markov Decision Process (POMDP) and aim to develop a robust planner under a few-shot assumption.
Our experiments on the ALFRED dataset indicate that our planner achieves competitive performance under a few-shot assumption.
arXiv Detail & Related papers (2024-12-27T10:05:45Z) - Speedup Techniques for Switchable Temporal Plan Graph Optimization [7.478072166004144]
Multi-Agent Path Finding (MAPF) focuses on planning collision-free paths for multiple agents.
During the execution of a MAPF plan, agents may encounter unexpected delays, which can lead to inefficiencies, deadlocks, or even collisions.
This paper introduces Improved GSES, which significantly accelerates Graph-Based Switchable Edge Search (GSES) through four speedup techniques.
arXiv Detail & Related papers (2024-12-20T13:59:15Z) - Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions [5.5233853454863615]
Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective starting locations to their respective goal locations.
Although many MAPF algorithms can handle up to thousands of agents, they usually rely on the assumption that each action of the agent takes a time unit.
This paper develops new planners that lie on the other end of the spectrum, trading off solution quality for scalability.
arXiv Detail & Related papers (2024-12-16T11:36:24Z) - Decentralized Monte Carlo Tree Search for Partially Observable
Multi-agent Pathfinding [49.730902939565986]
Multi-Agent Pathfinding problem involves finding a set of conflict-free paths for a group of agents confined to a graph.
In this study, we focus on the decentralized MAPF setting, where the agents may observe the other agents only locally.
We propose a decentralized multi-agent Monte Carlo Tree Search (MCTS) method for MAPF tasks.
arXiv Detail & Related papers (2023-12-26T06:57:22Z) - Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results [60.4817465598352]
We introduce an original variant of Monte-Carlo Tree Search (MCTS) tailored to multi-agent pathfinding.
Specifically, we use individual paths to assist the agents with the the goal-reaching behavior.
We also use a dedicated decomposition technique to reduce the branching factor of the tree search procedure.
arXiv Detail & Related papers (2023-07-25T12:33:53Z) - Robust Multi-Agent Pickup and Delivery with Delays [5.287544737925232]
Multi-Agent Pickup and Delivery (MAPD) is the problem of computing collision-free paths for a group of agents.
Current algorithms for MAPD do not consider many of the practical issues encountered in real applications.
We present two solution approaches that provide robustness guarantees by planning paths that limit the effects of imperfect execution.
arXiv Detail & Related papers (2023-03-30T14:42:41Z) - Symmetry Breaking for k-Robust Multi-Agent Path Finding [30.645303869311366]
k-Robust Conflict-BasedSearch (k-CBS) is an algorithm that produces coordinated and collision-free plan that is robust for up to k delays.
We introduce a variety of pairwise symmetry breaking constraints, specific to k-robust planning, that can efficiently find compatible and optimal paths for pairs of conflicting agents.
arXiv Detail & Related papers (2021-02-17T11:09:33Z) - A Feedback Scheme to Reorder a Multi-Agent Execution Schedule by
Persistently Optimizing a Switchable Action Dependency Graph [65.70656676650391]
We consider multiple Automated Guided Vehicles (AGVs) navigating a common workspace to fulfill various intralogistics tasks.
One approach is to construct an Action Dependency Graph (ADG) which encodes the ordering of AGVs as they proceed along their routes.
If the workspace is shared by dynamic obstacles such as humans or third party robots, AGVs can experience large delays.
We present an online method to repeatedly modify acyclic ADG to minimize route completion times of each AGV.
arXiv Detail & Related papers (2020-10-11T14:39:50Z) - Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal
Constraints [52.58352707495122]
We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination.
We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
arXiv Detail & Related papers (2020-05-27T01:10:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.