Kanzhong Yao

Kanzhong Yao, Xueliang Cheng, Keir Groves et al.

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.

Liang Wang, Kanzhong Yao, Yang Liu et al.

Parkour tasks for quadrupeds have emerged as a promising benchmark for agile locomotion. While human athletes can effectively perceive environmental characteristics to select appropriate footholds for obstacle traversal, endowing legged robots with similar perceptual reasoning remains a significant challenge. Existing methods often rely on hierarchical controllers that follow pre-computed footholds, thereby constraining the robot's real-time adaptability and the exploratory potential of reinforcement learning. To overcome these challenges, we present PUMA, an end-to-end learning framework that integrates visual perception and foothold priors into a single-stage training process. This approach leverages terrain features to estimate egocentric polar foothold priors, composed of relative distance and heading, guiding the robot in active posture adaptation for parkour tasks. Extensive experiments conducted in simulation and real-world environments across various discrete complex terrains, demonstrate PUMA's exceptional agility and robustness in challenging scenarios.