With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance

• URL: http://arxiv.org/abs/2009.12106v1
• Date: Fri, 25 Sep 2020 09:49:22 GMT
• Title: With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance
• Authors: \'Alvaro Serra-G\'omez, Bruno Brito, Hai Zhu, Jen Jen Chung, Javier Alonso-Mora
• Abstract summary: This paper presents an efficient communication method that solves the problem of "when" and with "whom" to communicate in multi-robot collision avoidance scenarios. In this approach, every robot learns to reason about other robots' states and considers the risk of future collisions before asking for the trajectory plans of other robots.
• Score: 17.18628401523662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots. Especially in complex dynamic environments, the coordination boost allowed by communication is critical to avoid collisions between cooperating robots. However, the risk of collision between a pair of robots fluctuates through their motion and communication is not always needed. Additionally, constant communication makes much of the still valuable information shared in previous time steps redundant. This paper presents an efficient communication method that solves the problem of "when" and with "whom" to communicate in multi-robot collision avoidance scenarios. In this approach, every robot learns to reason about other robots' states and considers the risk of future collisions before asking for the trajectory plans of other robots. We evaluate and verify the proposed communication strategy in simulation with four quadrotors and compare it with three baseline strategies: non-communicating, broadcasting and a distance-based method broadcasting information with quadrotors within a predefined distance.

Related papers

• Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning [72.86540018081531]
  Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance. This problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets.
  arXiv  Detail & Related papers  (2024-09-29T23:57:25Z)
• Multi-Robot Informative Path Planning for Efficient Target Mapping using Deep Reinforcement Learning [11.134855513221359]
  We propose a novel deep reinforcement learning approach for multi-robot informative path planning. We train our reinforcement learning policy via the centralized training and decentralized execution paradigm. Our approach outperforms other state-of-the-art multi-robot target mapping approaches by 33.75% in terms of the number of discovered targets-of-interest.
  arXiv  Detail & Related papers  (2024-09-25T14:27:37Z)
• The Shortcomings of Force-from-Motion in Robot Learning [48.036338624248835]
  We argue for more interaction-explicit action spaces in robot learning. Current robot learning approaches focus on motion-centric action spaces that do not explicitly give the policy control over the interaction.
  arXiv  Detail & Related papers  (2024-07-03T08:23:02Z)
• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [80.86089324742024]
  We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem. CNN processes localized perception; a graph neural network (GNN) facilitates robot communications. Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
  arXiv  Detail & Related papers  (2024-01-10T00:08:00Z)
• Asynchronous Perception-Action-Communication with Graph Neural Networks [93.58250297774728]
  Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments. The robots must execute a Perception-Action-Communication loop -- they perceive their local environment, communicate with other robots, and take actions in real time. Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control. This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication.
  arXiv  Detail & Related papers  (2023-09-18T21:20:50Z)
• Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration [2.958532752589616]
  Communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots.
  arXiv  Detail & Related papers  (2023-05-26T16:56:00Z)
• Simulation of robot swarms for learning communication-aware coordination [0.0]
  We train end-to-end Neural Networks that take as input local observations obtained from an omniscient centralised controller. Experiments are run in Enki, a high-performance open-source simulator for planar robots.
  arXiv  Detail & Related papers  (2023-02-25T17:17:40Z)
• Improving safety in physical human-robot collaboration via deep metric learning [36.28667896565093]
  Direct physical interaction with robots is becoming increasingly important in flexible production scenarios. In order to keep the risk potential low, relatively simple measures are prescribed for operation, such as stopping the robot if there is physical contact or if a safety distance is violated. This work uses the Deep Metric Learning (DML) approach to distinguish between non-contact robot movement, intentional contact aimed at physical human-robot interaction, and collision situations.
  arXiv  Detail & Related papers  (2023-02-23T11:26:51Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)

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.