NCCR Robotics publishes open source software and datasets, please see below for a list and links to where they can be downloaded. Feature Tracking Analysis for Event Cameras Scaramuzza’s… Read more
The consortium is keen on supporting entrepreneurship. The below spin-offs were granted the NCCR Robotics spin fund. For a comprehensive list of spin fund holders please see our spin-off page.… Read more
The NCCR Robotics Spin Fund committee has granted Przemyslaw Kornatowski the Spin Fund for Dronistics. Dronistics is the 11th NCCR Robotics Spin-off and is hosted at Floreano Lab.
NCCR drones can now be effortlessly controlled with pointing gestures. A video demonstration of the system developed by IDSIA has been published at the Human-Robot Interaction (HRI 2018) conference, March 5-8, 2018, Chicago, IL, USA. More info: http://people.idsia.ch/~gromov/hri-landing/
Fotokite, an NCCR Robotics spin-off, has been selected amongst the 6 finalists of the Genius NY Competition The Highlights The cohort will arrive at The Tech Garden in downtown Syracuse, NY in January for nearly 12 months of acceleration and incubation. Two phase in-residence accelerator program Phase One: Six teams receive a monthly stipend ($10,000 per month …
8 Oct – 9 Oct 2018
Aerial Futures: The Drone Frontier @ HUBweek
Boston District Hall, Boston
|Swissnex Boston is gathering a selection of some of the most exciting drone exhibitors from Switzerland and the United States to bring to HUBweek. Expect an eclectic selection of UAVs...|
Looking for publications? You might want to consider searching on the EPFL Infoscience site which provides advanced publication search capabilities.
Flying robots that can locomote efficiently in GPS-denied cluttered environments have many applications, such as in search and rescue scenarios. However, dealing with the high amount of obstacles inherent to such environments is a major challenge for flying vehicles. Conventional flying platforms cannot afford to collide with obstacles, as the disturbance from the impact may provoke a crash to the ground, especially when friction forces generate torques affecting the attitude of the platform. We propose a concept of resilient flying robots capable of colliding into obstacles without compromising their flight stability. Such platforms present great advantages over existing robots as they are capable of robust flight in cluttered environments without the need for complex sense and avoid strategies or 3D mapping of the environment. We propose a design comprising an inner frame equipped with conventional propulsion and stabilization systems enclosed in a protective cage that can rotate passively thanks to a 3-axis gimbal system, which reduces the impact of friction forces on the attitude of the inner frame. After addressing important design considerations thanks to a collision model and validation experiments, we present a proof-of-concept platform, named GimBall, capable of flying in various cluttered environments. Field experiments demonstrate the robot’s ability to fly fully autonomously through a forest while experiencing multiple collisions.
Flying robots are increasingly adopted in search and rescue missions because of their capability to quickly collect and stream information from remote and dangerous areas. To further enhance their use, we are investigating the development of a new class of drones, foldable sensorized hubs that can quickly take off from rescuers’ hands as soon as they are taken out of a pocket or a backpack. With this aim, this paper presents the development of a foldable wing inspired by insects. The wing can be packaged for transportation or deployed for flight in half a second with a simple action from the user. The wing is manufactured as a thick origami structure with a foldable multi-layer material. The prototype of the foldable wing is experimentally characterized and validated in flight on a mini-drone.
We report on an actuator based on dielectric elastomers that is capable of antagonistic actuation and passive folding. This actuator enables foldability in robots with simple structures. Unlike other antagonistic dielectric elastomer devices, our concept uses elastic hinges to allow the folding of the structure, which also provides an additional design parameter. To validate the actuator concept through a specific application test, a foldable elevon actuator with outline size of 70 mm × 130 mm is developed with angular displacement range and torque specifications matched to a 400-mm wingspan micro-air vehicle (MAV) of mass 130 g. A closed-form analytical model of the actuator is constructed, which was used to guide the actuator design. The actuator consists of 125-μm-thick silicone membranes as the dielectric elastomers, 0.2mm-thick fiberglass plate as the frame structure, and 50-μm-thick polyimide as the elastic hinge. We measured voltage-controllable angular displacement up to ±26° and torque of 2720 mN · mm at 5 kV, with good agreement between the model and the measured data. Two elevon actuators are integrated into the MAV, which was successfully flown, with the foldable actuators providing stable and well-controlled flight. The controllability was quantitatively evaluated by calculating the correlation between the control signal and the MAV motion, with a correlation in roll axis of over 0.7 measured during the flights, illustrating the high performance of this foldable actuator.
We aim at developing autonomous miniature hovering flying robots capable of navigating in unstructured GPS-denied environments. A major challenge is the miniaturization of the embedded sensors and processors that allow such platforms to fly by themselves. In this paper, we propose a novel ego-motion estimation algorithm for hovering robots equipped with inertial and optic-flow sensors that runs in real- time on a microcontroller and enables autonomous flight. Unlike many vision-based methods, this algorithm does not rely on feature tracking, structure estimation, additional dis- tance sensors or assumptions about the environment. In this method, we introduce the translational optic-flow direction constraint, which uses the optic-flow direction but not its scale to correct for inertial sensor drift during changes of direction. This solution requires comparatively much sim- pler electronics and sensors and works in environments of any geometry. Here we describe the implementation and per- formance of the method on a hovering robot equipped with eight 0.65 g optic-flow sensors, and show that it can be used for closed-loop control of various motions.
Proceedings of the 2nd International Symposium on Aerial Robotics Most current drones are designed with a static morphology aimed at exploiting a single locomotion mode. This results in limited versatility and adaptability to multi-domain environments, such as those encountered in rescue missions, agriculture and inspection, where multiple locomotion capabilities could be more effective. For …
Robots capable of hover flight in constrained indoor environments have many applications, however their range is constrained by the high energetic cost of airborne locomotion. Perching allows flying robots to scan their environment without the need to remain aloft. This paper presents the design of a mechanism that allows indoor flying robots to attach to vertical surfaces. To date, solutions that enable flying robot with perching capabilities either require high precision control of the dynamics of the robot or a mechanism robust to high energy impacts. We propose in this article a perching mechanism comprising a compliant deployable pad and a passive self-alignment system, that does not require any active control during the attachment procedure. More specifically, a perching mechanism using fibre-based dry adhesives was implemented on a 300 g flying platform. An adhesive pad was first modeled and optimized in shape for maximum attachment force at the low pre-load forces inherent to hovering platforms. It was then mounted on a deployable mechanism that stays within the structure of the robot during flight and can be deployed when a perching maneuver is initiated. Finally, the perching mechanism is integrated onto a real flying robot and successful perching maneuvers are demonstrated as a proof of concept.
We present a conceptually and computationally lightweight method for the design and iterative learning of fast maneuvers for quadrocopters. We use first-principles, reduced-order models and we do not require nor make an attempt to follow a specific state trajectory-only the initial and the final states of the vehicle are taken into account. We evaluate the adaptation scheme through experiments on quadrocopters in the ETH Flying Machine Arena that perform multi-flips and other high-performance maneuvers.
Flying robots have unique advantages in the exploration of cluttered environments such as caves or collapsed buildings. Current systems however have difficulty in dealing with the large amount of obstacles inherent to such environments. Collisions with obstacles generally result in crashes from which the platform can no longer recover. This paper presents a method for designing active uprighting mechanisms for protected rotorcraft-type flying robots that allow them to upright and subsequently take off again after an otherwise mission-ending collision. This method is demonstrated on a tailsitter flying robot which is capable of consistently uprighting after falling on its side using a spring-based ’leg’ and returning to the air to continue its mission.
Authors: Helen Oleynikova, Christian Lanegger, Zachary Taylor, Michael Pantic, Alexander Millane, Roland Siegwart & Juan Nieto
We present an open‐source system for Micro‐Aerial Vehicle (MAV) autonomous navigation from vision‐based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field‐of‐view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real‐world scenarios are presented. We focus our experiments on evaluating global planning, path smoothing, and local planning methods on real maps made on MAVs in realistic search‐and‐rescue and industrial inspection scenarios. We also perform thousands of simulations in cluttered synthetic environments, and finally validate the complete system in real‐world experiments.
Autonomous navigation in obstacle-dense indoor environments is very challenging for flying robots due to the high risk of collisions, which may lead to mechanical damage of the platform and eventual failure of the mission. While conventional approaches in autonomous navigation favor obstacle avoidance strategies, recent work showed that collision-robust flying robots could hit obstacles without breaking and even self-recover after a crash to the ground. This approach is particularly interesting for autonomous navigation in complex environments where collisions are unavoidable, or for reducing the sensing and control complexity involved in obstacle avoidance. This paper aims at showing that collision-robust platforms can go a step further and exploit contacts with the environment to achieve useful navigation tasks based on the sense of touch. This approach is typically useful when weight restrictions prevent the use of heavier sensors, or as a low-level detection mechanism supplementing other sensing modalities. In this paper, a solution based on force and inertial sensors used to detect obstacles all around the robot is presented. Eight miniature force sensors, weighting 0.9g each, are integrated in the structure of a collision-robust flying platform without affecting its robustness. A proof-of-concept experiment demonstrates the use of contact sensing for exploring autonomously a room in 3D, showing significant advantages compared to a previous strategy. To our knowledge this is the first fully autonomous flying robot using touch sensors as only exteroceptive sensors.