Tootfinder

Decentralized Lifelong Path Planning for Multiple Ackerman Car-Like Robots
Teng Guo, Jingjin Yu
https://arxiv.org/abs/2402.11767 https://

Decentralized Lifelong Path Planning for Multiple Ackerman Car-Like Robots
Path planning for multiple non-holonomic robots in continuous domains constitutes a difficult robotics challenge with many applications. Despite significant recent progress on the topic, computationally efficient and high-quality solutions are lacking, especially in lifelong settings where robots must continuously take on new tasks. In this work, we make it possible to extend key ideas enabling state-of-the-art (SOTA) methods for multi-robot planning in discrete domains to the motion planning o…

This https://arxiv.org/abs/2309.10275 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Optimizing Crowd-Aware Multi-Agent Path Finding through Local Broadcasting with Graph Neural Networks
Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem in motion planning, aiming to find collision-free paths for all agents in the system. MAPF finds a wide range of applications in various domains, including aerial swarms, autonomous warehouse robotics, and self-driving vehicles. Current approaches to MAPF generally fall into two main categories: centralized and decentralized planning. Centralized planning suffers from the curse of dimensionality when the numb…

This https://arxiv.org/abs/2309.15943 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?
A flurry of recent work has demonstrated that pre-trained large language models (LLMs) can be effective task planners for a variety of single-robot tasks. The planning performance of LLMs is significantly improved via prompting techniques, such as in-context learning or re-prompting with state feedback, placing new importance on the token budget for the context window. An under-explored but natural next direction is to investigate LLMs as multi-robot task planners. However, long-horizon, hetero…

This https://arxiv.org/abs/2307.04004 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction
Next-Best View (NBV) planning is a long-standing problem of determining where to obtain the next best view of an object from, by a robot that is viewing the object. There are a number of methods for choosing NBV based on the observed part of the object. In this paper, we investigate how predicting the unobserved part helps with the efficiency of reconstructing the object. We present, Multi-Agent Prediction-Guided NBV (MAP-NBV), a decentralized coordination algorithm for active 3D reconstruction…

This https://arxiv.org/abs/2311.16916 has been replaced.
link: https://scholar.google.com/scholar?q=a

Stein Variational Belief Propagation for Multi-Robot Coordination
Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. The planning problem is particularly challenging in the presence of obstacles and different sources of uncertainty such as inaccurate dynamic models and sensor noise. In this paper, we introduce Stein Variational Belief Propagation (SVBP), a novel algorithm for performing inference over nonparametric marginal distributions of nodes in a graph. We apply SVBP to multi-robot coordination b…