Recent developments in planetary exploration have shown the potential of Uncrewed Aerial Vehicles (UAVs), such as the Ingenuity helicopter that provided valuable mapping data. However, limited payload capabilities constrain the flight times and compute available for localization which restrict their applicability. By providing a tethered connection, issues such as battery and computational constraints are offloaded to the base rover. At the same time, the cable can be exploited for non-drifting localization. This work presents a novel Tether-Inertial Localization approach that uses tether length, and angle measurements to estimate the UAV position relative to its base. The method combines a computationally efficient analytical catenary model with a Gaussian Process (GP) residual error compensation. This accounts for systematic sensor inaccuracies and model limitations. Experimental validation across circular, triangular, and figure-eight trajectories with tether lengths up to 4.5 m and a total flight time of 37 minutes demonstrates the effectiveness of the proposed approach. Using only tether-based position estimates for feedback, the analytical catenary model achieves an average RMSE of 7.4 cm, which is further reduced to 5.2 cm through GP-based residual compensation, one order of magnitude better than the state-of-the-art. These results establish Tether-Inertial Localization as a practical alternative to vision- and GNSS-based localization for Tethered Uncrewed Aerial Vehicles (TUAVs).

Tiny flying robots hold great potential for search-and-rescue, safety inspections, and environmental monitoring, but their small size and limited computational resources constrain onboard sensing capabilities. Inspired by animals such as rats and moles which rely on lightweight whiskers to navigate and perceive their surroundings through touch, we present a 3.2-gram whisker-based tactile sensing apparatus that enables tiny drones to perceive and interact with their environment through gentle physical contact, even in complete darkness. The apparatus employs barometers at the base of each whisker to estimate contact depth in flight, enabling obstacle localization while minimizing contact-induced destabilization. To compensate for sensor noise and drift during sustained contact, we develop a tactile depth estimation pipeline that achieves millimeter-scale depth estimation accuracy. Together, these innovations enable tiny drones to autonomously avoid obstacles, contour surfaces, and explore confined spaces, guided by onboard tactile sensing across both rigid and soft environments. Running entirely onboard a microcontroller with just 192 KB of memory, our system demonstrates autonomous tactile flight across various scenarios. This bio-inspired approach extends perception for mobile robots beyond vision, opening new possibilities for autonomous operations in visually degraded and GPS-denied environments.

Flying insects’ thorax houses the flight muscles that provide efficient, multi-axis wing actuation. Such bio-inspiration is essential for developing future flapping wing micro air vehicles (FWMAVs) that combine advanced maneuverability with design simplicity, low weight, and high power efficiency. In this work, we propose a novel unibody with distributed compliant joints inspired by the multiple degrees of actuation freedom of an insect thorax—in particular, wing stroke plane modulation for active pitch and yaw—yielding a compact multifunctional structural component for the 24.6 g FWMAV: Delfly Flex. All of these functions are achieved within a single 3.73 g 3D-printed integrated airframe. To design this unibody, we provide an analytical framework that guides compliant joint geometry using differential flexure beam analysis, along with an optimal joint orientation analysis for seamless integration into the unibody. To ensure sufficient structural endurance, we investigate various resin materials and printing configurations, resulting in a robust resin-printed unibody that incorporates two compliant joints and wing-root stabilizers. This single structure replaces the conventional multi-component FWMAV body composed of rigid-hinge-based dihedral pitch & yaw mechanisms attached to a rod-like fuselage. We characterize the flight capabilities of Delfly Flex through tethered experiments measuring force and moment generation. The results show thrust generation and yaw moment arms equivalent to its predecessor, while the pitch moment arm is approximately 50% smaller due to the concentrated mass distribution inherent to the unibody design. Free-flight experiments further validate the concept, demonstrating controlled pitch and yaw maneuvers enabled by compliant beams as thin as 0.4 mm. Combined with simplified assembly and more than 10% mass reduction, this unibody concept opens pathways toward future designs with increased deformability and expanded control authority. Overall, this study highlights the synergy between aero-mechanical design and additive manufacturing, achieving enhanced body intelligence through insect-thorax-inspired FWMAV structures.

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.).

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.

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.

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.

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.

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.

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.

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.