DeepAI AI Chat
Log In Sign Up

Stop, Think, and Roll: Online Gain Optimization for Resilient Multi-robot Topologies

by   Marco Minelli, et al.

Efficient networking of many-robot systems is considered one of the grand challenges of robotics. In this article, we address the problem of achieving resilient, dynamic interconnection topologies in multi-robot systems. In scenarios in which the overall network topology is constantly changing, we aim at avoiding the onset of single points of failure, particularly situations in which the failure of a single robot causes the loss of connectivity for the overall network. We propose a method based on the combination of multiple control objectives and we introduce an online distributed optimization strategy that computes the optimal choice of control parameters for each robot. This ensures that the connectivity of the multi-robot system is not only preserved but also made more resilient to failures, as the network topology evolves. We provide simulation results, as well as experiments with real robots to validate theoretical findings and demonstrate the portability to robotic hardware.


page 1

page 2

page 3

page 4


Minimally Constrained Multi-Robot Coordination with Line-of-sight Connectivity Maintenance

In this paper, we consider a team of mobile robots executing simultaneou...

Distributed Adaptive and Resilient Control of Multi-Robot Systems with Limited Field of View Interactions

In this paper, we consider two coupled problems for distributed multi-ro...

Resilient robot teams: a review integrating decentralised control, change-detection, and learning

Purpose of review: This paper reviews opportunities and challenges for d...

OpTopNET: A Learning Optimal Topology Synthesizer for Ad-hoc Robot Networks

In this paper, we synthesize a machine-learning stacked ensemble model a...

Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems

Robustness is key to engineering, automation, and science as a whole. Ho...

Failout: Achieving Failure-Resilient Inference in Distributed Neural Networks

When a neural network is partitioned and distributed across physical nod...

Robust-by-Design Plans for Multi-Robot Pursuit-Evasion

This paper studies a multi-robot visibility-based pursuit-evasion proble...