Prof. Dr. Stefano Mintchev

Distributed tactile sensing for multi-force detection is crucial for various aerial robot interaction tasks. However, current contact sensing solutions on drones only exploit single end-effector sensors and cannot provide distributed multi-contact sensing. Designed to be easily mounted at the bottom of a drone, we propose an optical tactile sensor that features a large and curved soft sensing surface, a hollow structure and a new illumination system. Even when spaced only 2cm apart, multiple contacts can be detected simultaneously using our software pipeline, which provides real-world quantities of 3D contact locations (mm) and 3D force vectors (N), with an accuracy of 1.5 mm and 0.17 N respectively. We demonstrate the sensor's applicability and reliability onboard and in real-time with two demos related to i) the estimation of the compliance of different perches and subsequent re-alignment and landing on the stiffer one, and ii) the mapping of sparse obstacles. The implementation of our distributed tactile sensor represents a significant step towards attaining the full potential of drones as versatile robots capable of interacting with and navigating within complex environments.

Animals traverse vegetation by direct physical interaction using their entire body to push aside and slide along compliant obstacles. Current drones lack this interaction versatility that stems from synergies between body morphology and feedback control modulated by sensing. Taking inspiration from nature, we show that a task-oriented design allows a drone with a minimalistic controller to traverse obstacles with unknown elastic responses. A discoid sensorized shell allows to establish and sense contacts anywhere along the shell and facilitates sliding along obstacles. This simplifies the formalization of the control strategy, which does not require a model of the interaction with the environment, nor high-level switching conditions for alternating between pushing and sliding. We utilize an optimization-based controller that ensures safety constraints on the robot's state and dampens the oscillations of the environment during interaction, even if the elastic response is unknown and variable. Experimental evaluation, using a hinged surface with three different stiffness values ranging from 18 to 155.5 N mm rad⁻¹, validates the proposed embodied aerial physical interaction strategy. By also showcasing the traversal of isolated branches, this work makes an initial contribution toward enabling drone flight across cluttered vegetation, with potential applications in environmental monitoring, precision agriculture, and search and rescue.

Covering over a third of all terrestrial land area, forests are crucial environments; as ecosystems, for farming, and for human leisure. However, they are challenging to access for environmental monitoring, for agricultural uses, and for search and rescue applications. To enter, aerial robots need to fly through dense vegetation, where foliage can be pushed aside, but occluded branches pose critical obstacles. Therefore, we propose pixel-wise depth regression of occluded branches using three different U-Net inspired architectures. Given RGB-D input of trees with partially occluded branches, the models estimate depth values of only the wooden parts of the tree. A large photorealistic simulation dataset comprising around 44K images of nine different tree species is generated, on which the models are trained. Extensive evaluation and analysis of the models on this dataset is shown. To improve network generalization to real-world data, different data augmentation and transformation techniques are performed. The approaches are then also successfully demonstrated on real-world data of broadleaf trees from Swiss temperate forests and a tropical Masoala Rainforest. This work showcases the previously unexplored task of frame-by-frame pixel-based occluded branch depth reconstruction to facilitate robot traversal of forest environments. All models, code, and data are available online.

Increased flight time and advanced sensors are making Micro Aerial Vehicles (MAVs) easier to use, facilitating their widespread adoption in fields such as precision agriculture or environmental monitoring. However, current applications are limited mainly to passive visual observation from far above; to enable the next generation of aerial robot applications, MAV s must begin to directly physically interact with objects in the environment, such as placing and collecting sensors. Enabling these applications for a wide spectrum of end-users is only possible if the mechanism is safe and easy to use, without overburdening the user with complex integration, complicated control, or overwhelming and convoluted feedback. To this end we propose a self-sufficient passive payload system to enable both the deployment and retrieval of sensors for agriculture. This mechanism can be simply mechanically attached to a commercial, off-the-shelf MAV, without requiring further electrical or software integration. The user-centric design and mechanical intelligence of the system facilitates ease of use through simplified control with targeted perceptual feedback. The usability of the system is validated quantitatively and qualitatively in a user study demonstrating sensor deployment and collection. All participants were able to deploy and collect at least four sensors both within 10 minutes in visual line-of-sight and within 12 minutes in beyond visualline-of-sight, after only three minutes of practice. Enabling MAVs to physically interact with their environment will usher in the next stage of MAV utility and applications. Complex tasks, such as sensor deployment and retrieval, can be realized relatively simply, by relying on a mechanically passive system designed with the user in mind, these payloads can enable such applications to be more widely available and inclusive to end-users.

Seed-inspired minimalist microflyers are showing potential as dispersal platforms for sensor networks and seeds. Their function relies on the sheer number of low-cost flyers, inevitably raising concerns about the post-operation environmental impact. We propose a biodegradable paper glider platform fabricated through origami inkjet printing. This method can fold origami along printing patterns on different paper varieties. We created a printing pattern that allows 2D paper sheets to self-fold into a 3D flying wing glider with designable wing geometry and center of gravity (CG). The design allows stable and repeatable gliding behavior, proven in our gliding tests. It successfully achieves the dispersal of multiple gliders from a hovering drone, covering an average horizontal distance larger than the release height. We also tested the biodegradation of different paper types compatible with the printing method, showing near-complete degradation after 15 weeks in moist soil. Our study presents a novel, potentially scalable approach for fabricating environmentally friendly microflyers, offering new avenues for remote environmental sensing and automated forest restoration programs.

Adaptive-morphology multirotors exhibit superior versatility and task-specific performance compared to traditional multirotors owing to their functional morphological adaptability. However, a notable challenge lies in the contrasting requirements of locking each morphology for flight controllability and efficiency while permitting low-energy reconfiguration. A novel design approach is proposed for reconfigurable multirotors utilizing soft multistable composite laminate airframes. These airframes show kinematically determinate morphologies corresponding to multiple minima in their elastic potential energy landscape. By varying design parameters, the methodology allows for tuning the energy landscape characteristics governing each morphology's structural stability and reconfiguration energetics. The airframe, composed of multistable composite laminate grids, is optimized to maximize rigidity under flight loads and minimize reconfiguration work. The 130-g reconfigurable multirotor design demonstrates self-locking properties in an open and a folded configuration, enabling a 48% reduction in width-span without compromising stability during flight. Soft pneumatic actuators, actuated using a tethered compressed air supply, enable reversible reconfiguration on the ground between open and folded configurations. The design resolves the conflicting requirements of high-stiffness to lock each flight configuration and low-actuation work for reconfigurability. By exploiting soft yet multistable structures, the approach combines the stability observed in rigid-linked reconfigurable multirotors with the low-effort reconfigurability of soft multirotors, offering new methods for designing adaptive-morphology multirotors.

Environmental DNA (eDNA) analysis is a powerful tool for studying biodiversity in forests and tree canopies. However, collecting representative eDNA samples from these high and complex environments remains challenging. Traditional methods, such as surface swabbing or tree rolling, are labor-intensive and require significant effort to achieve adequate coverage. This study proposes a novel approach for unmanned aerial vehicles (UAVs) to collect eDNA within tree canopies by using a surface swabbing technique. The method involves lowering a probe from a hovering UAV into the canopy and collecting eDNA as it descends and ascends through branches and leaves. To achieve this, a custom-designed robotic system was developed featuring a winch and a probe for eDNA collection. The design of the probe was optimized, and a control logic for the winch was developed to reduce the risk of entanglement while ensuring sufficient interaction force to facilitate transfer of eDNA onto the probe. The effectiveness of this method was demonstrated during the XPRIZE Rainforest Semi-Finals as 10 eDNA samples were collected from the rainforest canopy, and a total of 152 molecular operational taxonomic units (MOTUs) were identified using eDNA metabarcoding. We further investigate how the number of probe interactions with vegetation, the penetration depth, and the sampling duration influence the DNA concentration and community composition of the samples.

Despite the elegantly simple operation principle, the design of everting vine robots still presents two complexities: a tip-device required for buckle-free retraction; a pressurised base chamber to maintain inflation while allowing the everted body to be winded onto a motorised reel. We create a new type of vine robots with a unique eversion method: instead of using a single everting tube, our design comprises multiple interconnected tubes that bend to induce an overall eversion. Our design eliminates the pressurised base and the tip-device, while allowing buckle-free retraction. It also enables new designs and functionalities beyond conventional vine robots, including drone-based vine robots capable of cantilevered operations, everting grippers that grasp then transport objects into the robot body, and solid-state vine robots without the need for inflation.

The protection and restoration of the biosphere is crucial for human resilience and well-being, but the scarcity of data on the status and distribution of biodiversity puts these efforts at risk. DNA released into the environment by organisms, i.e., environmental DNA (eDNA), can be used to monitor biodiversity in a scalable manner if equipped with the appropriate tool. However, the collection of eDNA in terrestrial environments remains a challenge because of the many potential surfaces and sources that need to be surveyed and their limited accessibility. Here, we propose to survey biodiversity by sampling eDNA on the outer branches of tree canopies with an aerial robot. The drone combines a force-sensing cage with a haptic-based control strategy to establish and maintain contact with the upper surface of the branches. Surface eDNA is then collected using an adhesive surface integrated in the cage of the drone. We show that the drone can autonomously land on a variety of branches with stiffnesses between 1 and 10³ newton/meter without prior knowledge of their structural stiffness and with robustness to linear and angular misalignments. Validation in the natural environment demonstrates that our method is successful in detecting animal species, including arthropods and vertebrates. Combining robotics with eDNA sampling from a variety of unreachable aboveground substrates can offer a solution for broad-scale monitoring of biodiversity.

Forest canopies are vital ecosystems, but remain understudied due to difficult access. Forests could be monitored with a network of biodegradable sensors that break down into environmentally friendly substances at the end of their life. As a first step in this direction, this paper details the development of a biodegradable origami gripper to attach conventional sensors to branches, deployable with an aerial robot. Through exposure to sufficient moisture the gripper loses contractile force, dropping the sensor to the ground for easier collection. The origami design of the gripper as well as biodegradable materials selection is detailed, allowing for further extensions utilizing biodegradable origami. Both the gripper and the gelatin hydrogel used as an actuating elastic element for generating the grasping force are experimentally characterized, with the gripper demonstrating a maximum holding force of 1 N. Additionally, the degradation of the gripper until failure in the presence of moisture is also investigated, where the gripper can absorb up to 10 ml of water before falling off a branch. Finally, deployment of the gripper on a tree branch with an aerial robot is demonstrated. Overall, the biodegradable origami gripper represents a first step towards a more scalable and environmentally sustainable approach for ecosystem monitoring.

Global agriculture is challenged to provide food for a human population that is larger than ever before and still increasing. This is accompanied by the need to reduce the large global impacts of agriculture while increasing yields. Early identification of plant stress enables fast intervention to limit crop losses and optimized application of pesticides and fertilizer to reduce environmental impacts. Current image-based approaches identify plant stress responses hours or days after the stress event, usually only after substantial damage has occurred and visual cues become apparent. In contrast, plant volatiles are released seconds to hours after stress events and can quickly indicate both the type and severity of stress. An automatable and nondisruptive sampling method is needed to enable the use of plant volatiles for monitoring plant stress in precision agriculture. In this work, we detail the development of a plant volatile sampler that can be deployed and collected with an uncrewed aerial vehicle (UAV). The effect of sampling flow rate, horizontal distance to volatile source, and overhead downwash on collected volatiles is investigated, along with the deployment accuracy and retrieval successes with manual flight. Finally, volatile sampling is validated in outdoor tests. The possibility of robotic collection of plant volatiles is a first and important step toward the use of chemical signals for early stress detection and opens up new avenues for precision agriculture beyond visual remote sensing.

Forests provide vital resources and services for humanity, but preserving and restoring them is challenging due to the difficulty of obtaining actionable data, especially in inaccessible areas such as forest canopies. To address this, we follow the lead of arboreal animals that exploit multiple modes of locomotion.We combine aerial and tethered movements to enable AVOCADO to navigate with in a tree canopy.Starting from the top of a tree, it can descend with the tether and maneuver around obstacles with thrusters. We extend our previous work with a new mechanical design with a protective shell, increased computational power and cameras for state estimation. We introduce a dynamic model and simulation, and perform a quasi-static and dynamic validation. For autonomy, we derive a control framework in simulation to regulate tether length,tilt and heading, before transfer to the robot. We evaluate the controllers for trajectory tracking through experiments. AVOCADO can follow trajectories around obstacles and reject disturbances on the tether. Exploiting multimodal mobility will advance the exploration of tree canopies to actively monitor the true value of our forests.

Labour and resource intensive data collection methods drive the use of unmanned aerial vehicles in the field of environmental monitoring. UAV supported sensor deployment in forests can improve localized and continuous monitoring. To advance this field, we present a two fingered gripper with spines integrated on three phalanxes. The softness of the fingers combined with compliantly-supported microspines integrated into adjustable microspine-clusters allow the gripper to wrap and adhere to tree branches. With a differential drive actuation, microspine cluster adjustability as well as load-sharing between spine clusters is achieved. We characterize the bending behaviour of the soft fingers that adapt to curved and irregular objects. We show that the implementation of compliantly-supported and adjustable microspine-clusters increase holding force and that load-sharing between spine clusters is achieved with the differential drive actuation. The demonstration of UAV perching on a tree branch with the gripper shows that sensor deployment in these environments can be achieved.

Dense vegetation is an example of a structurally complex environment that robots encounter in many outdoor applications such as agriculture, environmental monitoring, or search and rescue. For a robot to safely navigate or perform manipulation tasks in dense vegetation, it is important to detect and distinguish rigid branches from the surrounding soft foliage. This task is challenging for traditional sensing approaches, such as vision, because the foliage can partially or totally occlude the branches. We present a haptic sensing system capable of detecting contact with the vegetation and locating branches occluded by soft foliage. The system follows a vision-based tactile sensing approach consisting of an array of compliant whiskers, with fiducial markers attached to them, and a camera to track their displacement. When the whiskers are inserted in the vegetation and the array is oscillated, rigid branches cause the whiskers to deflect significantly, while deflections caused by softer foliage are smaller. We developed a sampling strategy and a software pipeline to detect the location of the branches based on the deflection magnitude of the whiskers. Upon indoor and outdoor experiments with artificial and natural vegetation, we demonstrate the ability to locate branches among compliant foliage.

Access and exploration of confined and cluttered spaces is a major challenge in search and rescue, maintenance of infrastructures, and environmental monitoring. However, existing drones can only access passageways that are 30% narrower of their size. Herein, a drone that can squeeze its way through arbitrarily long passages that are half its width is presented. This is achieved by developing a quadrotor that synergistically embodies a soft foldable frame, multimodal mobility, and autonomous navigation. The drone exploits visual perception to detect the entrance of the gap and aerial mobility to align and fly toward it. The entry is made possible by the soft design of the frame, which passively folds without breaking when the drone flies and then collides at a controlled speed with the entrance of the passage, i.e., the "crash to squash" entry maneuver. Once inside, the quadrotor uses terrestrial locomotion for the traversal. The mechanical design of the drone and the performance of the “crash to squash” entry maneuver in passageways of different sizes are experimentally characterized. Finally, the control method is validated by indoor autonomous flights.

Understanding forest functioning is limited by the scalability of monitoring solutions and difficulty of access. Manual sensor placement can reach most locations but lacks scalability. Micro-aerial vehicles (MAVs) allow for scalable sensor delivery, but current solutions are limited to attaching sensors to the trunk or large branches with spines or adhesives. The thinner branches of the outer canopy remain inaccessible, despite being of particular interest due to the important physiological processes occurring in the foliage. Herein, a MAV-deployable bistable helically coiling origami gripper is developed. The unfurled state allows for transport with a MAV, and when pushed against a branch triggers the second helically coiled state, which permits secure attachment to branches. Origami manufacturing keeps the weight of the gripper below 5 g, despite holding up to 280 g, and gripping diameters from 8 mm to 38 mm inclined up to 30°. The holding force, activation force, and resistance to tilt and rotation offsets are experimentally characterized. The deployment and retrieval of the gripper and sensor are demonstrated outside, where sensor data are collected from previously inaccessible branches in the outer canopy. Enabling robust sensor attachment in the outer canopy marks a step toward scalable environmental monitoring of forest ecosystems.

The collection of environmental and biodiversity data is essential to manage, preserve and restore forests, but this task remains challenging due to the inaccessibility of these ecosystems. Compared to human intervention, aerial robots can access tree canopies, but their limited flight time and noise continue to stall widespread application. To address this challenge, we present a perching mechanism which allows small drones to rest on overhanging branches and extend their mission while remaining silent. We developed an origami spine with two folding flaps containing a layer of high-friction material. When the spine engages with a branch, the flaps open and conform to irregular branch surfaces generating sufficient friction to support the weight of a drone. With HEDGEHOG, a drone integrating multiple spines on a protective cage, we demonstrated its application in a controlled indoor as well as in a forest environment. We modelled the perching strategy and measured the effects of materials and geometric parameters on the drone's perching performance. By leveraging interactions with nature, our drone can perch on tree branches with diameters up to 86 mm and inclined up to +-15 and potentially remain in the canopy for extended periods of time to acquire data or monitor returning wildlife.

Forest canopies are the biggest habitat for terrestrial life, yet our understanding of environmental processes and biodiversity inside the canopy continues to be limited due to labour and resource intensive data collection. Existing aerial and climbing robots also struggle to access these complex environments, while animals easily navigate them using multiple means of locomotion. Following this insight we present a robot with multimodal mobility obtained by combining aerial and tethered locomotion. After the robot is deployed at the top of the tree, it can descend with the tether and maneuver around leaves and branches with its thrusters. The tether increases robustness and safety and allows for resting as well as emergency retrieval of the system. The aerial locomotion grants the system the ability to move in a conical 3D space constrained by the tether. We modelled the static system and validated the impact of design parameters on it. A simple control architecture for teleoperation is discussed and its performance is analyzed. The proposed multimodal mobility is demonstrated in preliminary outdoor tests, which show how our robot can move within the canopy while continuously monitoring the environment.

Collision resilience is an important feature of robots deployed in unstructured and partially unpredictable environments. Herein, a novel dual stiffness (DS) tensegrity platform to integrate collision resilience into a robot body is proposed. The proposed DS tensegrity platform is rigid during normal robot operation, but softens upon collision to withstand the impact. The DS behavior is achieved by means of a novel DS strut that is rigid, but can buckle without breaking under high loads, thus preventing damage to the robot. Compression tests and finite element method simulations show that both the DS struts and DS tensegrities undergo substantial stiffness change with maximum load-bearing ratios up to 10.5 and 5.74, respectively, before and after buckling. These DS tensegrity structures are integrated into two types of robots, a drone and a rover, that are shown to withstand falls from 2 and 5 m, respectively. The mechanical tunability of the proposed DS tensegrity system makes it suitable for impact attenuation in a wide range of situations and robot types.

The aerodynamic designs of winged drones are optimized for specific flight regimes. Large lifting surfaces provide maneuverability and agility but result in larger power consumption, and thus lower range, when flying fast compared with small lifting surfaces. Birds like the northern goshawk meet these opposing aerodynamic requirements of aggressive flight in dense forests and fast cruising in the open terrain by adapting wing and tail areas. Here, we show that this morphing strategy and the synergy of the two morphing surfaces can notably improve the agility, maneuverability, stability, flight speed range, and required power of a drone in different flight regimes by means of an avian-inspired drone. We characterize the drone's flight capabilities for different morphing configurations in wind tunnel tests, optimization studies, and outdoor flight tests. These results shed light on the avian use of wings and tails and offer an alternative design principle for drones with adaptive flight capabilities.