Soft strain resistive sensors based on eutectic gallium-indium liquid metal can play an important role in proprioceptive sensing for soft robots. However, there are no available mathematical models to accurately estimate the strain as a function of the measured resistance. Furthermore, non-uniform strain in the microchannels has not been analysed yet. In this paper, we introduce a new model to estimate the strain or elongation in sub-millimetre scale, and analyse its accuracy through a customised testing set-up and procedure. The effect of strain rate on the measurement accuracy is also studied. We compare existing theoretical models with our experimental results, and discuss the differences between them. Moreover, we analyse the effect of strain rate on hysteresis caused by the viscoelastic behaviour and introduce a new model for it to be potentially used for future work. This paper demonstrates, among other things, that rational models could provide high accuracy in strain estimation, and might help to enhance proprioceptive sensing and state control of soft robots.

Actively articulated wheel-on-leg rovers offer high degrees of mobility for traversing unstructured and scientifically interesting terrain. An ability to actively conform to the terrain to increase traversability is sought. A kinematic model based on recursive kinematic propagation in combination with a terrain map generated online is used to solve desired limb articulation angles that meet operator defined rover body trajectories. This technique is validated in field tests on a Martian analog terrain.

Autonomous docking for airborne energy transfer is an important unmanned aerial vehicle capability that has yet to be accomplished. The implications of this technology are far reaching because vehicle endurance can be significantly extended without requiring additional onboard energy storage or environmental energy collection. A major barrier to docking with a drogue is reliable and accurate knowledge of the drogue's state, relative to the aircraft that is performing the manoeuvre. We address this by propagating a derived cable-drogue dynamic model using onboard sensor measurements, then correcting the state and aerodynamic coefficients with air-to-air visual observations. The approach is verified through a series of close formation flights with a marker based infra-red vision system. These flights represent significant progress toward an autonomous docking demonstration.

Unmanned aerial vehicle (UAV) formation flight can vastly increase operational range and persistence through autonomous aerial refuelling or efficient flight on a wingman's wake vortices. Differencing individual UAV state estimates is not sufficiently accurate for close formation operations and must be augmented with vehicle-to-vehicle observations. To this end, we propose a quaternion based unscented Kalman filter to fuse information from each UAV sensor suite with relative vision observations. The result is a vastly improved relative state estimate that is resilient to brief vision dropouts and degrades gracefully during extended dropouts. Simulated formation flight results validate the approach and provide a numerical analysis of the algorithm performance. Ground based experiments demonstrate the algorithm running in real-time on a dual-UAV system. This represents a significant step towards an airborne implementation.

This paper presents a methodology for the system identification of a light weight, small-scale parafoil suspended motorized aircraft, known as a paramotor. The study is concerned with the acquisition of linear models describing both the lateral and longitudinal dynamics of the aircraft, with an emphasis on practical techniques that can be implemented in the real world. The mathematical models developed in this paper are first validated by comparison with real flight data, before being employed in the design of a guidance, navigation and control system, the performance of which is demonstrated by real autonomous flight tests.

Fixed-wing Unmanned Aerial Vehicle (UAV) rendezvous is necessary for reduced fuel consumption during leader-follower formation flight. We propose a heuristic direct search algorithm that plans a time-optimal path for a follower UAV to rendezvous with a leader whose future path is known and unchanging. The kinematic constraints of the UAV are considered and discontinuities inherent to the minimum-length paths are dealt with. Experiments using quadrotors to emulate fixed-wings, demonstrate the algorithm planning and replanning optimal paths to rendezvous in real-time.

In wildlife radio tagging, small radio transmitters attached to animals are located by human operators using directional antennas and analog receivers which provide audio output. The location of the transmitter is determined by listening to the signal and scanning the area while closing in. This procedure can be very tedious, especially in rough terrain. Searching radio tags with autonomous unmanned aerial vehicles (UAVs) offers a number of advantages, including better line-of-sight signal reception, terrain-independence and faster localization. In this paper we continue upon previous work by presenting a received-signal-strength (RSS) sensor implementation based on a modified commercial wildlife tracking receiver that is designed to operate on an autonomous fixed-wing UAV. Furthermore, an extension of the search and tracking framework for multiple targets that are undistinguishable from the sensors' point of view is proposed. After a brief system overview and a summary of the particle filter based approach, the signal processing theory and realization of the RSS sensor are outlined, including strategies for frequency tracking and receiver gain control. The paper also presents experimental results.

This paper proposes a control strategy based on forward reachable sets (FRSs) analysis for multiple pursuers to capture an evader with a higher maneuverability than the pursuers. The strategy first finds the pursuers' capture states at which their FRSs cover the evader's entire FRS so as to improve the possibility of capture. The pursuers approach these capture states from their initial states in feed-forward control at a low sampling rate. Upon reaching the capture states, the pursuers approach and capture the evader in a feedback manner. The proposed strategy was applied to two pursuit-evasion scenarios and compared to the generic motion tracking algorithm. Results show the efficacy of the proposed strategy and its superiority to the motion tracking algorithm

This paper presents the system architecture of a real time multi-UAV simulator (RMUS). The simulator has been implemented as both a testing and validation mechanism for the real demonstration of multiple UAVs conducting both decentralised data fusion and control. These mechanisms include the off-line simulation of complex scenarios, hardware-in-the-loop tests, validation of real test results, and online mission control system demonstrations. The paper also present CommLibX, a novel communication framework for the system which allows simulation modules to communicate over single or multiple virtual channels. This unique communication system is then easily ported onto the real hardware allowing for maximum reuse of software and integrity.