Protecting and restoring forest ecosystems has become an important conservation issue. Although various robots have been used for field data collection to protect forest ecosystems, the complex terrain and dense canopy make the data collection less efficient. To address this challenge, an aerial platform with bio-inspired behaviour facilitated by a bio-inspired mechanism is proposed. The platform spends minimum energy during data collection by perching on tree branches. A raptor inspired vision algorithm is used to locate a tree trunk, and then a horizontal branch on which the platform can perch is identified. A tendon-driven mechanism inspired by bat claws which requires energy only for actuation, secures the platform onto the branch using the mechanism’s passive compliance. Experimental results show that the mechanism can perform perching on branches ranging from 30 mm to 80 mm in diameter. The real-world tests validated the system’s ability to select and adapt to target points, and it is expected to be useful in complex forest ecosystems. Project website: https://aerialroboticsgroup.github.io/branch-perching-project/

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.

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.

Being motivated by ceiling inspection applications via unmanned aerial vehicles (UAVs) which require close proximity flight to surfaces, a systematic control approach enabling safe and accurate close proximity flight is proposed in this work. There are two main challenges for close proximity flights: (i) the trust characteristics varies drastically for the different distance from the ceiling which results in a complex nonlinear dynamics; (ii) the system needs to consider physical and environmental constraints to safely fly in close proximity. To address these challenges, a novel framework consisting of a constrained optimization-based force estimation and an optimization-based nonlinear controller is proposed. Experimental results illustrate that the performance of the proposed control approach can stabilize UAV down to 1 cm distance to the ceiling. Furthermore, we report that the UAV consumes up to 12.5% less power when it is operated 1 cm distance to ceiling, which is promising potential for more battery-efficient inspection flights.

This paper presents an unmanned aerial vehicle (UAV) push recovery operation using model predictive control (MPC) and moving horizon estimation (MHE) in a symmetric manner. This utilization is motivated by the active use of UAVs, particularly for the contact based inspection of the surrounding's ceilings. To enable a physical interaction operation by an optimization-based algorithm, a primal-dual quadratic programming (QP) solver is structured for the MPC and MHE. The designed system consists of (a) an interaction model to be implemented both on the control and the estimation; (b) an integral action in the predictive controller; (c) a disturbance estimation by MHE to update the MPC. Consequently, the nominal MPC, the integral action in MPC, and the disturbance observer based MPC are compared for a UAV push recovery operation. The numerical investigations demonstrate the applicability of the proposed approach.

On one hand, we are aware of the fact that quadrotors have been becoming a part of our daily life day to day; on the other hand, their control is still a challenging task as, unlike from the ground vehicles, they do not have enough friction forces to stabilize their motion. What is more, quadrotor's six DOF motion (three translational and three rotational) is controlled by varying only the speeds of its four independent rotors, resulting in under-actuated, highly nonlinear and coupled dynamics. In this paper, conventional proportional-derivative (PD), Mamdani-type fuzzy and TSK-type fuzzy neural network-based controllers have been designed, and their performance have been compared based on both control accuracy and control effort. A realistic trajectory, which is feasible regarding the input constraints of the quadrotor, is generated to test the accuracy and efficiency of the proposed methods. Realistic uncertainties, such as wind and gust conditions, are also given to the system to demonstrate the robustness of the controllers in real-time operation. The adaptive fuzzy-neural controller gives the most accurate trajectory tracking results for a 4D trajectory reducing the error by a factor of 4 when compared to the conventional PD and fuzzy controller although the control effort increases only by 10%.