Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 h) were conducted based on the DARPA Subterranean Challenge. Missions involved deploying a heterogeneous team of robots to locate and identify artefacts such as climbing rope, drills and a mannequin representing a human survivor. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human interventions included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items ( \(+\) 10.52%), traversed more distance ( \(+\) 12.71%), covered more unique ground ( \(+\) 10.56%), and longer time between safety-related events (34%). In routine conditions, both condition scores were comparable for artefacts, distance and coverage. Human-Robot Teams were faster at finding the first artefact but slower to respond to information from the robot team. Overall, operators contribute to mission-based outcomes, help to overcome environmental situations that can impede progress, and can assist robots to recover faster from difficult events.

In this paper we present the design and control strategies of a novel quadruped climbing robot (named Magneto) with three degrees of freedom (3-DOF) actuated limbs and a 3-DOF compliant magnetic foot. By exploiting its high degrees of freedom, Magneto is able to deform its body shape to squeeze through gaps of 23cm, which is smaller than standard human entry portholes of industrial confined spaces. Its compact foot design of footprint 4cm allows Magneto to walk on narrow beams of thickness less than 5cm, even at varying separation. The inherent high dimensional system design enables the body to be positioned in a wide range of orientations and seamlessly switch a limb function from locomotion to manipulation mode mid-climb. This capability enables access to confined space openings and occluded pockets and navigation through complex 3-D structures previously not demonstrated on legged climbing robots.

This paper describes the development of a novel vision-based autonomous surface vehicle with the purpose of performing coordinated docking manoeuvres with a target, such as an autonomous underwater vehicle, at the water's surface. The system architecture integrates two small processor units; the first performs vehicle control and implements a virtual force based docking strategy, with the second performing vision-based target segmentation and tracking. Furthermore, the architecture utilises wireless sensor network technology allowing the vehicle to be observed by, and even integrated within an ad-hoc sensor network. Simulated and experimental results are presented demonstrating the autonomous vision- based docking strategy on a proof-of-concept vehicle.