Speedup Techniques for Switchable Temporal Plan Graph Optimization

• URL: http://arxiv.org/abs/2412.15908v2
• Date: Sun, 12 Jan 2025 01:03:35 GMT
• Title: Speedup Techniques for Switchable Temporal Plan Graph Optimization
• Authors: He Jiang, Muhan Lin, Jiaoyang Li,
• Abstract summary: 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.
• Score: 7.478072166004144
• License:
• Abstract: Multi-Agent Path Finding (MAPF) focuses on planning collision-free paths for multiple agents. However, during the execution of a MAPF plan, agents may encounter unexpected delays, which can lead to inefficiencies, deadlocks, or even collisions. To address these issues, the Switchable Temporal Plan Graph provides a framework for finding an acyclic Temporal Plan Graph with the minimum execution cost under delays, ensuring deadlock- and collision-free execution. Unfortunately, existing optimal algorithms, such as Mixed Integer Linear Programming and Graph-Based Switchable Edge Search (GSES), are often too slow for practical use. This paper introduces Improved GSES, which significantly accelerates GSES through four speedup techniques: stronger admissible heuristics, edge grouping, prioritized branching, and incremental implementation. Experiments conducted on four different map types with varying numbers of agents demonstrate that Improved GSES consistently achieves over twice the success rate of GSES and delivers up to a 30-fold speedup on instances where both methods successfully find solutions.

Related papers

• A Real-Time Rescheduling Algorithm for Multi-robot Plan Execution [9.839983977902671]
  Switchable-Edge Search (SES) is an A*-style algorithm designed to find optimal passing orders. We prove the optimality of SES and evaluate its efficiency via simulations.
  arXiv  Detail & Related papers  (2024-03-26T23:10:41Z)
• Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution [7.2988406091449605]
  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%.
  arXiv  Detail & Related papers  (2023-12-30T20:23:27Z)
• Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching [53.91395791840179]
  We present Unified Spectral Bundling with Sketching (USBS), a provably correct, fast and scalable algorithm for solving massive SDPs. USBS provides a 500x speed-up over the state-of-the-art scalable SDP solver on an instance with over 2 billion decision variables.
  arXiv  Detail & Related papers  (2023-12-19T02:27:22Z)
• GRASP: Accelerating Shortest Path Attacks via Graph Attention [1.3270612461223892]
  Recent advances in machine learning (ML) have shown promise in aiding and accelerating classical optimization algorithms. We propose the GRASP algorithm: Graph Accelerated Shortest Path Attack, an ML aided optimization algorithm that achieves run times up to 10x faster.
  arXiv  Detail & Related papers  (2023-10-12T02:03:10Z)
• Non-stationary Online Convex Optimization with Arbitrary Delays [50.46856739179311]
  This paper investigates the delayed online convex optimization (OCO) in non-stationary environments. We first propose a simple algorithm, namely DOGD, which performs a gradient descent step for each delayed gradient according to their arrival order. We develop an improved algorithm, which reduces those dynamic regret bounds achieved by DOGD to $O(sqrtbardT(P_T+1))$.
  arXiv  Detail & Related papers  (2023-05-20T07:54:07Z)
• General Cutting Planes for Bound-Propagation-Based Neural Network Verification [144.7290035694459]
  We generalize the bound propagation procedure to allow the addition of arbitrary cutting plane constraints. We find that MIP solvers can generate high-quality cutting planes for strengthening bound-propagation-based verifiers. Our method is the first verifier that can completely solve the oval20 benchmark and verify twice as many instances on the oval21 benchmark.
  arXiv  Detail & Related papers  (2022-08-11T10:31:28Z)
• Distributed stochastic optimization with large delays [59.95552973784946]
  One of the most widely used methods for solving large-scale optimization problems is distributed asynchronous gradient descent (DASGD) We show that DASGD converges to a global optimal implementation model under same delay assumptions.
  arXiv  Detail & Related papers  (2021-07-06T21:59:49Z)
• 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.