Aerial robots show significant potential for forest canopy research and environmental monitoring by providing data collection capabilities at high spatial and temporal resolutions. However, limited flight endurance hinders their application. Inspired by natural perching behaviours, we propose a multi-modal aerial robot system that integrates tensile perching for energy conservation and a suspended actuated pod for data collection. The system consists of a quadrotor drone, a slewing ring mechanism allowing 360° tether rotation, and a streamlined pod with two ducted propellers connected via a tether. Winding and unwinding the tether allows the pod to move within the canopy, and activating the propellers allows the tether to be wrapped around branches for perching or disentangling. We experimentally determined the minimum counterweights required for stable perching under various conditions. Building on this, we devised and evaluated multiple perching and disentangling strategies. Comparisons of perching and disentangling manoeuvres demonstrate energy savings that could be further maximized with the use of the pod or tether winding. These approaches can reduce energy consumption to only 22% and 1.5%, respectively, compared to a drone disentangling manoeuvre. We also calculated the minimum idle time required by the proposed system after the system perching and motor shut down to save energy on a mission, which is 48.9% of the operating time. Overall, the integrated system expands the operational capabilities and enhances the energy efficiency of aerial robots for long-term monitoring tasks.

Aerial robots have a wide range of applications, such as collecting data in hard-to-reach areas. This requires the longest possible operation time. However, because currently available commercial batteries have limited specific energy of roughly 300 W h kg-1, a drone's flight time is a bottleneck for sustainable long-term data collection. Inspired by birds in nature, a possible approach to tackle this challenge is to perch drones on trees, and environmental or man-made structures, to save energy whilst in operation. In this paper, we propose an algorithm to automatically generate trajectories for a drone to perch on a tree branch, using the proposed tethered perching mechanism with a pendulum-like structure. This enables a drone to perform an energy-optimised, controlled 180° flip to safely disarm upside down. To fine-tune a set of reachable trajectories, a soft actor critic-based reinforcement algorithm is used. Our experimental results show the feasibility of the set of trajectories with successful perching. Our findings demonstrate that the proposed approach enables energy-efficient landing for long-term data collection tasks.

We present a virtual reality (VR) teleoperation interface for a ground-based robot, featuring dense 3D environment reconstruction and a low latency video stream, with which operators can immersively explore remote environments. At the UK Atomic Energy Authority's (UKAEA) Remote Applications in Challenging Environments (RACE) facility, we applied the interface in a user study where trained robotics operators completed simulated nuclear monitoring and decommissioning style tasks to compare VR and traditional teleoperation interface designs. We found that operators in the VR condition took longer to complete the experiment, had reduced collisions, and rated the generated 3D map with higher importance when compared to non-VR operators. Additional physiological data suggested that VR operators had a lower objective cognitive workload during the experiment but also experienced increased physical demand. Overall the presented results show that VR interfaces may benefit work patterns in teleoperation tasks within the nuclear industry, but further work is needed to investigate how such interfaces can be integrated into real world decommissioning workflows.

Using a delta-manipulator for stabilisation of an end-effector to perform precise spatial positioning is a current area of interest in aerial manipulation. High speed precision movements of a manipulator can cause disturbances to the aerial platform, which hinders trajectory tracking and in some cases could be sufficient to cause a loss of control of the vehicle. In this paper, a statically balanced delta aerial manipulator is developed and evaluated. The system is balanced using three counter-masses to reduce the force imparted onto the base and thus reduce perturbations to the movement of the drone. The system is thoroughly tested following trajectories while mounted to a force sensor and while on-board an aerial vehicle. Results show that the forces transmitted to the base in all axes are reduced considerably, however improvements in overall flight accuracy are not observed in aerial settings. Design lessons to make a balanced delta-manipulator viable for practical implementation on an aerial vehicle are discussed in depth. A video summarising the flight testing results is available at https://youtu.be/fXKnosnVKCk.

The recent momentum in aerial manipulation has led to an interest in developing virtual reality interfaces for aerial physical interaction tasks with simple, intuitive, and reliable control and perception. However, this requires the use of expensive subsystems and there is still a research gap between interface design, user evaluations and the effect on aerial manipulation tasks. Here, we present a methodology for low-cost available drone systems with a Unity-based interface for immersive FPV teleoperation. We applied our approach in a flight track where a cluttered environment is used to simulate a demanding aerial manipulation task inspired by forestry drones and canopy sampling. Through objective measures of teleoperation performance and subjective questionnaires, we found that operators performed worse using the FPV interface and had higher perceived levels of cognitive load when compared to traditional interface design. Additional analysis of physiological measures highlighted that objective stress levels and cognitive load were also influenced by task duration and perceived performance, providing an insight into what interfaces could target to support teleoperator requirements during aerial manipulation tasks.

Current aerial robots are increasingly adaptive; they can morph to enable operation in changing conditions to complete diverse missions. Each mission may require the robot to conduct a different task. A conventional learning approach can handle these variations when the system is trained for similar tasks in a representative environment. However, it may result in overfitting to the new data stream or the failure to adapt, leading to degradation or a potential crash. These problems can be mitigated with an excessive amount of data and embedded model, but the computational power and the memory of the aerial robots are limited. In order to address the variations in the model, environment as well as the tasks within onboard computation limitations, we propose a deep neuromorphic controller approach with variable topologies to handle each different condition and the data stream with a feasible computation and memory allocation. The proposed approach is based on a deep neuromorphic (multi and variable layered neural network) controller with dynamic depth and progressive layer adaptation for each new data stream. This adaptive structure is combined with a switching function to form a sliding mode controller. The network parameter update rule guarantees the stability of the closed loop system by the convergence of the error dynamics to the sliding surface. Being the first implementation on an aerial robot in this context, the results illustrate the adaptation capability, stability, computational efficiency as well as the real-time validation.

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments.

Whilst Micro Aerial Vehicles (MAVs) possess a variety of promising capabilities, their high energy consumption severely limits applications where flight endurance is of high importance. Reducing energy usage is one of the main challenges in advancing aerial robot utility. To address this bottleneck in the development of unmanned aerial vehicle applications, this work proposes an bioinspired mechanical approach and develops an aerial robotic system for greater endurance enabled by low power station-keeping. The aerial robotic system consists of an multirotor MAV and anchoring modules capable of launching multiple tensile anchors to fixed structures in its operating envelope. The resulting tensile perch is capable of providing a mechanically stabilized mode for high accuracy operation in 3D workspace. We explore generalised geometric and static modelling of the stabilisation concept using screw theory. Following the analytical modelling of the integrated robotic system, the tensile anchoring modules employing high pressure gas actuation are designed, prototyped and then integrated to a quadrotor platform. The presented design is validated with experimental tests, demonstrating the stabilization capability even in a windy environment.

Water sampling with autonomous aerial vehicles has major applications in water monitoring and chemical accident response. Currently, no robot exists that is capable of both underwater locomotion and flight. This is principally because of the major design tradeoffs for operation in both water and air. A major challenge for such an aerial-aquatic mission is the transition to flight from the water. The use of high power density jet propulsion would allow short, impulsive take-offs by Micro Air Vehicles (MAVs). In this paper, we present a high power water jet propulsion system capable of launching a 70 gram vehicle to speeds of 11m/s in 0.3s, designed to allow waterborne take off for an Aquatic Micro Air Vehicle (AquaMAV). Jumps propelled by the jet are predicted to have a range of over 20m without gliding. Propulsion is driven by a miniaturised 57 bar gas release system, with many other applications in pneumatically actuated robots. We will show the development of a theoretical model to allow designs to be tailored to specific missions, and free flying operation of the jet.

Extensive work has been devoted recently to the development of 3D printing or additive layer manufacturing technologies, as well as to the field of flying robots. However, to the best of the authors7knowledge, no robotic prototype has been presented so far that combines additive layer manufacturing techniques with aerial robotics. In this paper, we examine the feasibility of such a hybrid approach and present the design and characterisation of an aerial 3D printer; a flying robot capable of depositing polyurethane expanding foam in mid-flight. We evaluate various printing materials and describe the design and integration of a lightweight printing module onto a quadcopter, as well as discuss the limitations and opportunities for aerial construction with flying robots using the developed technologies. Potential applications include ad-hoc construction of first response structures in search and rescue scenarios, printing structures to bridge gaps in discontinuous terrain, and repairing damaged surfaces in areas that are inaccessible by ground-based robots.

Alternating gliding and active propulsion is a potentially energy saving strategy for small-scale flight. With the goal of finding optimal wing shapes for flapping-gliding robots we evaluate the quasi-steady aerodynamic performance of four butterfly species (Monarch (Danaus plexippus), the Orange Aeroplane (Pantoporia consimilis), the Glasswing (Acraea andromacha) and the Four-barred Swordtail (Protographium Ieosthenes)). We fabricate at-scale wing models based on measured wing shapes and vary the forewing angle in nine steps to account for the ability of the butterfly to change the relative orientation of its forewing and hindwing during flight. For comparison we include twelve non-biological planforms as performance benchmarks for the butterfly wing shapes. We then test these 48 wing models at 2m/s, 3.5m/s and 5m/s (Reynolds number between 2597 and 12632) in a low speed wind tunnel which allows lift and drag force measurements of centimeter-size wings. The results indicate that the forewing orientation which maximizes the wing span offers the best gliding performance and that overall the gliding ratios are highest at 3.5m/s. The wing shapes with the best gliding ratio are found in the Glasswing butterfly with a maximum of 6.26 which is very high compared to the gliding performance of similarly sized flying robots. The results from this study are important for the development of novel biologically-inspired flying micro robots as well as for biomechanics studies in biology.

In nature, many animals are able to jump, upright themselves after landing and jump again. This allows them to move in unstructured and rough terrain. As a further development of our previously presented 7 g jumping robot, we consider various mechanisms enabling it to recover and upright after landing and jump again. After a weighted evaluation of these different solutions, we present a spherical system with a mass of 9.8 g and a diameter of 12 cm that is able to jump, upright itself after landing and jump again. In order to do so autonomously, it has a control unit and sensors to detect its orientation and spring charging state. With its current configuration it can overcome obstacles of 76 cm at a take-off angle of 75°.

Scanning laser range sensors (ladars) are frequently used in mobile robotics applications because their ability to accurately measure the environment in 3D makes them well-suited for perception tasks like terrain modeling and obstacle detection. The choice of ladar sensor and the manner in which it is configured and integrated into a robot platform is usually determined subjectively based on the experience of the project team members. This paper develops a method for evaluating ladar sensors and sensor configurations that objectively measures the quality of a sensor/configuration choice in terms of density and uniformity of measurements within a region of interest. The method is applicable to static sensors and environments as well as scenarios with moving objects and mobile sensors. It can be used to compare different sensors, to evaluate specific sensor configurations and search for the optimal one, and to aid in designing new ladar sensors tailored to specific applications. We find that popular ladar configurations are often not the best configuration choice, and that alternative configurations not commonly used would offer better data density and uniformity.

Jumping can be a very efficient mode of locomotion for small robots to overcome large obstacles and travel in natural, rough terrain. In this paper we present the development and characterization of a novel 5 cm, 7g jumping robot. It can jump obstacles more than 27 times its own size and outperforms existing jumping robots by one order of magnitude with respect to jump height per weight and jump height per size. It employs elastic elements in a four bar linkage leg system to allow for very powerful jumps and adjustment of the jumping force, take-off angle and force profile during the acceleration phase.

Unpowered flight can be used in microrobotics to overcome ground obstacles and to increase the traveling distance per energy unit. In order to explore the potential of goal-directed gliding in the domain of miniature robotics, we developed a 22cm microglider weighing a mere 1.5g and flying at around 1.5m/s. It is equipped with sensors and electronics to achieve phototaxis, which can be seen as a minimal level of control autonomy. A novel 0.2g Shape Memory Alloy (SMA) actuator for steering control has been specifically designed and integrated to keep the overall weight as low as possible. In order to characterize autonomous operation of this robot, we developed an experimental setup consisting of a launching device and a light source positioned 1m below and 4m away with varying angles with respect to the launching direction. Statistical analysis of 36 autonomous flights demonstrate its flight and phototaxis efficiency.