Fully Distributed Cooperative Multi-agent Underwater Obstacle Avoidance Under Dog Walking Paradigm

Navigation in cluttered underwater environments is challenging, especially when there are constraints on communication and self-localisation. Part of the fully distributed underwater navigation problem has been resolved by introducing multi-agent robot teams, however when the environment becomes cluttered, the problem remains unresolved. In this paper, we first studied the connection between everyday activity of dog walking and the cooperative underwater obstacle avoidance problem. Inspired by this analogy, we propose a novel dog walking paradigm and implement it in a multi-agent underwater system. Simulations were conducted across various scenarios, with performance benchmarked against traditional methods utilising Image-Based Visual Servoing in a multi-agent setup. Results indicate that our dog walking-inspired paradigm significantly enhances cooperative behavior among agents and outperforms the existing approach in navigating through obstacles.

Virtual Elastic Tether: a New Approach for Multi-agent Navigation in Confined Aquatic Environments

Underwater navigation is a challenging area in the field of mobile robotics due to inherent constraints in self-localisation and communication in underwater environments. Some of these challenges can be mitigated by using collaborative multi-agent teams. However, when applied underwater, the robustness of traditional multi-agent collaborative control approaches is highly limited due to the unavailability of reliable measurements. In this paper, the concept of a Virtual Elastic Tether (VET) is introduced in the context of incomplete state measurements, which represents an innovative approach to underwater navigation in confined spaces. The concept of VET is formulated and validated using the Cooperative Aquatic Vehicle Exploration System (CAVES), which is a sim-to-real multi-agent aquatic robotic platform. Within this framework, a vision-based Autonomous Underwater Vehicle-Autonomous Surface Vehicle leader-follower formulation is developed. Experiments were conducted in both simulation and on a physical platform, benchmarked against a traditional Image-Based Visual Servoing approach. Results indicate that the formation of the baseline approach fails under discrete disturbances, when induced distances between the robots exceeds 0.6 m in simulation and 0.3 m in the real world. In contrast, the VET-enhanced system recovers to pre-perturbation distances within 5 seconds. Furthermore, results illustrate the successful navigation of VET-enhanced CAVES in a confined water pond where the baseline approach fails to perform adequately.