With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively.

This research proposes a novel, dynamically reconfigurable, and force-balanced aerial manipulator design for fast variable payload tasks. Its force-balancing minimizes aerial platform disturbances from the manipulator during fast end-effector movements. The manipulator is composed of three pantograph legs connecting the end-effector to the drone base, each equipped with two countermasses moved by bespoke fast linear actuators that ensure force-balancing of the manipulator for different payloads. Testing on a floating base setup and in flight showed a 45% reduction in reaction forces transferred to the base in the balanced vs. unbalanced configurations with no payload, and 17% with a 53 g payload. The position-tracking error in flight reduced with 19% and 34%, respectively.

Highlights: What are the main findings? The proposed approach exploits tactile feedback from collisions to infer obstacle locationsin the environment. Our collision-aware estimator uses pre-collision velocities, rates and tactile feedback topredict post-collision velocities and rates alongside a vector-field-based path representationand recovery strategy to improve state estimation and ensure safe traversal ofcluttered environments at low computational cost. What are the implications of the main findings? The proposed method enables robust navigation in environments where traditionalvision- or range-based sensing is unreliable. The proposed method allows drones to recover in-flight from high-speed collisions andadapt their paths afterwards, preventing repeated impacts and improving resilience incluttered settings. Aerial robots are a well-established solution for exploration, monitoring, and inspection, thanks to their superior maneuverability and agility. However, in many environments, they risk crashing and sustaining damage after collisions. Traditional methods focus on avoiding obstacles entirely, but these approaches can be limiting, particularly in cluttered spaces or on weight- and computationally constrained platforms such as drones. This paper presents a novel approach to enhance drone robustness and autonomy by developing a path recovery and adjustment method for a high-speed collision-resilient aerial robot equipped with lightweight, distributed tactile sensors. The proposed system explicitly models collisions using pre-collision velocities, rates and tactile feedback to predict post-collision dynamics, improving state estimation accuracy. Additionally, we introduce a computationally efficient vector-field-based path representation that guarantees convergence to a user-specified path, while naturally avoiding known obstacles. Post-collision, contact point locations are incorporated into the vector field as a repulsive potential, enabling the drone to avoid obstacles while naturally returning to its path. The effectiveness of this method is validated through Monte Carlo simulations and demonstrated on a physical prototype, showing successful path following, collision recovery, and adjustment at speeds up to (Formula presented.) (Formula presented.) / (Formula presented.).

With advancements in drones' perception and control, the demand for enhanced mechanical design and integrated physical intelligence in these robots continues to grow. Effective landing gear systems are essential for preserving the integrity of agile, modern drones, where a careful combination of weight, durability, and complexity must be achieved. In this paper, we design, model and validate a continuum twisted tower origami to serve as a shock-absorbing landing gear for drones. Multiple different configurations with varying number of sides in the base and varying heights were 3D printed with a flexible material as monolithic structures. Characterization was performed using quasi-static testing and drone landing impact force measurements. The shock absorption was successfully demonstrated with a reduction of the total impact force of up to 75% for one of the tested configurations compared to a rigid landing gear during drop testing. Taller landing gears led to better impact force reduction, however with more units the whole structure bends excessively. The presented framework allows for scaling the landing structure in multiple ways, enabling the adaption to different drone platforms in the future, while keeping a single-material 3D-printing process without the need for further assembly.

Unmanned air vehicles (UAVs) have traditionally been considered as "eyes in the sky", that can move in three dimensions and need to avoid any contact with their environment. On the contrary, contact should not be considered as a problem, but as an opportunity to expand the range of UAVs applications. In this paper, we designed, fabricated, and characterized a whisker sensor unit based on MEMS barometers suitable for tactile localization on UAVs, featuring lightweight, low stiffness, high sensitivity, a broad sensing range, and scalability. Then, for the challenging task of contact point localization, we propose a Recurrent Multi-output Network (RMN) for predicting 3D contact points under continuous contact conditions to address the problems of non-linearity, hysteresis, and non-injective mapping between signals and contact points by considering time series. In addition, we propose an azimuth prediction loss function which reduces the RMSE by 3.24° compared to L₁ loss. Finally, we conduct experiments on a linear stage to validate the 3D contact point localization capability of the proposed whisker system and model. The results show that our localization can achieve excellent performance, with an inference time of 1.4 ms and a mean error of only 9.18 mm in Euclidean distance within 3D space, laying a robust foundation for future implementation of tactile localization on UAVs. The design files, dataset, and source code are available on: https://github.com/BioMorphic-Intelligence-Lab/Whisker-3D-Localization.

Drones have been increasingly used in various domains, including ecological monitoring in forests. However, the endurance and noise of drones have limited their deployment to short flight missions above canopies. To address these limitations, we introduce ALBERO: a framework comprising a mechanical solution and an optimal planner to realise agile quadrotor perching on tree branches of steep incline. The gripper features an ultra-fast active mechanism inspired by birds' claws that enables quadrotors to perch swiftly on randomly-oriented tree branches. By perching, the drone can preserve energy for extended periods of time, while silently gathering forest data in the canopy. The intrinsic properties of the gripper allow for extra flexibility in size, surface roughness and shape imperfections of natural perches, such as those found in the wild. The gripper also has good scalability properties and can be easily matched to different drones' sizes. The biggest advantage of this novel design lays in its ability to close reactively and ultra-fast (67ms) on the large gripper, 42ms on the small gripper), enabling the quadrotor to perform agile perching manoeuvres from different angles and at different approach speeds. ALBERO's software module comprises of a trajectory planning algorithm adapted for branch perching, ensuring that the drone can perch on inclined cylindrical targets from any starting location in the proximity of the branch. These requirements translate in stringent positioning and orientation accuracy, but they enable the drone to land dynamically from a variety of positions within the forest.

The increasing popularity of helium-assisted blimps for extended monitoring or data collection applications is hindered by a critical limitation-single-point failure when the balloon malfunctions or bursts. To address this, we introduce Janus, a hybrid blimp-drone platform equipped with integrated balloon failure detection and recovery capability. Janus employs a triggered mechanism that seamlessly transitions the platform from a blimp to a standard quad-rotor drone. Utilizing multiple sensors and fusing their readings, we have developed a robust balloon failure detection system. Janus demonstrates omnidirectional mobility in blimp mode and transitions promptly into quadrotor mode upon receiving the signal. Our results affirm the successful recovery of the system from balloon failure, with a rapid response time of 66 ms to balloon failure detection. The drone morphs into a quadrotor and achieves recovery within 0.362 seconds in 90% of cases. By amalgamating the enduring flight capabilities of blimps with the agility of quad-rotors within a morphing platform like Janus, we cater to applications demanding both prolonged flight duration and enhanced agility.

Aerial flyers in nature utilize strain sensing to monitor forces in real time, crucial for navigating through wind disturbances and obstacles during flight. While micro air vehicles (MAVs) typically utilize vision and airflow sensing [1], [2], the potential of strain sensing remains relatively unexplored, despite the abundance of available solutions crafted via advanced microfabrication techniques. After surveying available techniques in the literature, we introduce a streamlined fabrication process for rapid prototyping of strain gauges that requires a minimal set of low-cost tools, suitable for roboticists with limited microfabrication experience or resources. To showcase the effectiveness of our method, two kinds of strain gauges (with ginkgo-leaf-inspired patterns and conventional meander patterns) are integrated on a pair of flapping wings to monitor the wing deformation during flapping cycles. We aim to inspire researchers in aerial robotics to incorporate this lightweight and affordable strain-sensing technology to enhance flight navigation and control, opening new avenues for lightweight autonomy and intelligence.

In this paper, we present the ADAPT, a novel reconfigurable force-balanced parallel manipulator for spatial motions and interaction capabilities underneath a drone. The reconfigurable aspect allows different motion-based 3-DoF operation modes like translational, rotational, planar, and so on, without the need for disassembly. For the purpose of this study, the manipulator is used in translation mode only. A kinematic model is developed and validated for the manipulator. The design and motion capabilities are also validated both by conducting dynamics simulations of a simplified model on MSC ADAMS, and experiments on the physical setup. The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation applications, or generic floating-base applications.

Mobile robots have revolutionized the public and private sectors for transportation, exploration, and search and rescue. Efficient energy consumption and robust environmental interaction needed for complex tasks can be achieved in aerial–terrestrial robots by combining advantages of each locomotion mode. This review surveys over two decades of development in multimodal robots that move on the ground and in air. Multimodality can be achieved by leveraging three main design approaches: adding morphological features, adapting forms for locomotion transitions, and integrating multiple vehicle platforms. Each classification is thoroughly examined and synthesized, encompassing both qualitative and quantitative aspects. The authors delved into the intricacies of these approaches and explored the challenges and opportunities that lie ahead in pursuit of the next generation of mobile robots. This review aims to advance future deployment of multimodal robots in the real world for challenging operations in dangerous, unstructured, contact-prone, cluttered and subterranean environments.

Traditional landing gear consists of rigid linkages with dampers. They require a flat surface to function. In unstructured environments such as lunar craters or the martian polar region, these conditions are not always met. In this work, we equip a conventional quadrotor with four continuously deformable passive landing limbs with a logarithmic spiral geometry. By choosing the right geometric design as well as tension on the tendon running through the length of the limbs, we ensure that the limbs support the overall weight while passively complying to the environment. Hence, no active control during the landing process is needed in order to adapt to irregular ground. In a set of experiments, these compliant limbs showcase their ability to adjust to uneven landing terrain while maintaining the horizontal attitude of the base vehicle. Overall, this work highlights the future potential to access more challenging environments, leveraging physical compliance for robust landings.

Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. These bio-inspired and lightweight flying robots still present limitations in their ability to fly in direct wind and gusts, as their stability is severely compromised in contrast with their biological counterparts. To this end, this work aims at making in-gust flight of flapping wing drones possible using an embodied airflow sensing approach combined with an adaptive control framework at the velocity and position control loops. At first, an extensive experimental campaign is conducted on a real FWMAV to generate a reliable and accurate model of the in-gust flight dynamics, which informs the design of the adaptive position and velocity controllers. With an extended experimental validation, this embodied airflow-sensing approach integrated with the adaptive controller reduces the root-mean-square errors along the wind direction by 25.15% when the drone is subject to frontal wind gusts of alternating speeds up to 2.4 m/s, compared to the case with a standard cascaded PID controller. The proposed sensing and control framework improve flight performance reliably and serve as the basis of future progress in the field of in-gust flight of lightweight FWMAVs.

Aerial manipulators have the unique ability to cover wide-spread areas within a single mission, making them ideal for the transport and placement of sensors required to build an instrumented environment. Recent work in the field has focused on controllers for aerial interaction that account for compliance during contact-based tasks, omitting integration concerns that are critical to an automated solution. Furthermore, state-of-the-art flying base manipulators are often mechanically and computationally complex, reducing their endurance. Within this work, we present an interactive framework for autonomous sensor placement that incorporates both mechanical and software based compliance, optimised for use on a simple coplanar quadrotor. Under appropriate actuation and perception constraints, we detail the development of a control, perception, and motion planning strategy to enable sensor placement that relies solely on onboard computation and sensing, thus presenting a fully contained and accessible sensor placement approach capable of robust interaction with the environment. An extended finite-state machine is developed to facilitate automated mission planning. Extensive flight experiments are performed to validate the effectiveness of each sub-system, as well as the integrated solution. Experiments result in trajectory tracking errors under 10 mm as well as onboard mass estimation errors under 0.7% for sensors of various weights. A statistical analysis of 162 flight experiments shows the proposed framework's ability to autonomously place sensors within 10 cm of the target with a success rate of 93.8% and 95% confidence interval of (89%, 97%), thus confirming the robustness of our approach.1.

Flyers in nature equip different airflow sensing mechanisms to navigate through wind disturbances with remarkable flight stability. Embracing bio-inspiration, airflow sensing with conventional sensors has long been utilized in flight control for larger micro air vehicles (MAVs). Bio-inspired flapping wing MAVs (FWMAVs) have extremely limited power and payload, therefore implementing onboard airflow sensing has remained a challenge in spite of various attempts at miniaturized airflow sensor designs. This work characterizes the measurement performance of a lightweight off-the-shelf thermistor-based airflow sensor through comparison with a hot-wire probe. Wind tunnel tethered flight tests on a 31.3-gram FWMAV Delfly Nimble examine the onboard sensing performance at low flow speeds (up to 2 m/s), under the influence of flapping motion. This performance characterization further motivates a miniaturized re-design of the airflow sensor with over 40% size and weight reduction. The redesigned airflow sensor helps to realize the first flapping wing MAV free flight with onboard airspeed measurements, providing remarkable flight stability under wind speeds in the range of approximately 0.5 to 1.2 m/s. This embodied sensing configuration pushes the weight and power limit of miniaturized electronics for FWMAVs, providing an easy-to-integrate solution with good performance, and paving the way for more complex control of FWMAVs in dynamic conditions.

Sensor launching is an approach to remote sensor placement which can accurately deploy sensor nodes while maintaining a safe distance from obstacles, making it a promising method for hazardous environments such as nuclear facilities. Moreover, as long as the sensor’s trajectory can be accurately predicted, up to ±5 cm precision can be achieved with little onboard computation and perception. This extended abstract covers a robust method to predict said trajectories and the formulation of an optimal problem to find effective initial launching poses.

The tasks that unmanned aerial vehicles (UAVs) have taken upon have progressively grown in complexity over the years, alongside with the level of autonomy with which they are carried out. In this work, we present an example
of aerial screwing operations with a fully-actuated tilt-rotor platform. Key contributions include a new control framework to automate screwing operations through a robust hole search and in-hole detection algorithm. These are achieved without a-priori knowledge of the exact hole location, and without
the use of external tools, such as vision based hole detection or force sensors. Wrench coupling is implemented to account for the platform's kinematic constraints during screwing. The application of a constant contact force and a compliant response
to induced disturbances are obtained with the use of admittance
control. The full framework is validated with extensive flight
experiments that demonstrate the effectiveness of each subsystem,
as well as the complete architecture. We also validate
the robustness of the detection algorithm against false positives.
Within the results we demonstrate the ability to perform the
automated task with a 86% success rate over 35 flights, and
measured hole search time of 9s (median value).

Unmanned Aerial Vehicles (UAVs) are widely used for environmental surveying and exploration thanks to their maneuverability and accessibility. Until recently, however, these platforms were mainly used as passive systems that observe their environments visually and do not interact physically. The capability of UAVs to physically interact with their environment, also known as Aerial Manipulators (AMs), allows them to do a wider variety of tasks. These tasks include contact inspection, manipulation of objects, and more. To successfully interact with the environment, the AM must compensate for the contact-induced disturbance forces. One approach is to estimate the contact force and compensate for it within the control approach. This work introduces a framework to estimate the contact force at the End-Effector (EE) using only state measurements of the generic AM. Further, the evaluation of the framework in a simulation of an AM with a tendon-driven robotic arm shows that it precisely estimates the contact force.

Research into remote sensing tools for environmental monitoring is an essential aspect for ensuring a healthy an thriving biosphere under the canopy, but also for preventing forest-related hazards from occurring. The challenges associated with forestry robotics are posed by the environment itself, as forests are widespread areas with a high density of obstacles and complex geometries to navigate in. Multirotors are Unmanned Aerial Vehicles (UAVs) that offer great agility and an unbounded operational workspace within a compact size. These features make their deployment in such wide areas most suitable for depositing wireless sensor networks that provide real-time forest monitoring. Hence, within this paper we propose a novel paradigm to environmental monitoring which exploits UAVs as the carriers of environmental sensors to be deployed in forests. Three different methodologies and systems are hereby presented and discussed for sensor placement tasks, leveraging bespoke design, navigation and control techniques.

On-site inspection of large-scale infrastructure often involves high risks for the operators and high insurance costs. Despite several safety measures already in place to avoid accidents, an increasing concern has brought the need to remotely monitor hard-to-reach locations, for which the use of aerial robots able to interact with the environment has arisen. In this paper a novel approach to aerial manipulation is presented, where a compact manipulator with a single degree-of-freedom is tailored for the placement and retrieval of sensors in the environment. The proposed design integrates on-board sensing, a high-performance force controller on the manipulator, and a thrust-to-force mapping on the flight controller. Experimental results demonstrate the high reliability achieved during both placement and retrieval tasks on flat surfaces (e.g., a bridge wall) and cylindrical surfaces (e.g., tree trunks). A total number of 89 flight experiments were carried out to demonstrate the robustness and potential of the compact, bespoke aerial design.