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.

This paper addresses the problem of adequately protecting flying robots from damage resulting from collisions that may occur when exploring constrained and cluttered environments. A method for designing protective structures to meet the specific constraints of flying systems is presented and applied to the protection of a small coaxial hovering platform. Protective structures in the form of Euler springs in a tetrahedral configuration are designed and optimised to elastically absorb the energy of an impact while simultaneously minimizing the forces acting on the robot's stiff inner frame. These protective structures are integrated into a 282 g hovering platform and shown to consistently withstand dozens of collisions undamaged.

Because of their low weight, cost and energy consumption, optic-flow sensors attract growing interest in robotics for tasks such as self-motion estimation or depth measurement. Most applications require a large number of these sensors, which involves a fair amount of calibration work for each setup. In particular, the viewing direction of each sensor has to be measured for proper operation. This task is often cumbersome and prone to errors, and has to be carried out every time the setup is slightly modified. This paper proposes an algorithm for viewing direction calibration relying on rate gyroscope readings and a recursive weighted linear least square estimation of the rotation matrix elements. The method only requires the user to realize random rotational motions of its setup by hand. The algorithm provides hints about the current precision of the estimation and what motions should be performed to improve it. To assess the validity of the method, tests were performed on an experimental setup and the results compared to a precise manual calibration. The repeatability of the gyroscope-based calibration process reached ±1.7° per axis.

Swarms of flying robots are promising in many applications due to rapid terrain coverage. However, there are numerous challenges in realising autonomous operation in unknown indoor environments. A new autonomous flight methodology is presented using relative positioning sensors in reference to nearby static robots. The entirely decentralised approach relies solely on local sensing without requiring absolute positioning, environment maps, powerful computation or long-range communication. The swarm deploys as a robotic network facilitating navigation and goal directed flight. Initial validation tests with quadrotors demonstrated autonomous flight within a confined indoor environment, indicating that they could traverse a large network of static robots across expansive environments.

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 to design active uprighting mechanisms for protected rotorcraft-type flying robots that allow them to become 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.

The success of swarm behaviors often depends on the range at which robots can communicate and the speed at which they change their behavior. Challenges arise when the communication range is too small with respect to the dynamics of the robot, preventing interactions from lasting long enough to achieve coherent swarming. To alleviate this dependency, most swarm experiments done in laboratory environments rely on communication hardware that is relatively long range and wheeled robotic platforms that have omnidirectional motion. Instead, we focus on deploying a swarm of small fixed-wing flying robots. Such platforms have limited payload, resulting in the use of short-range communication hardware. Furthermore, they are required to maintain forward motion to avoid stalling and typically adopt low turn rates because of physical or energy constraints. The tradeoff between communication range and flight dynamics is exhaustively studied in simulation in the scope of Reynolds flocking and demonstrated with up to 10 robots in outdoor experiments.

Aerial robots are often required to remain within the communication range of a base station on the ground to exchange commands, sensor data or as a safety mechanism. For this purpose, we propose a minimal control strategy for steering flying robots using only communication hardware (e.g. WiFi module or radio modem) instead of GPS or cameras. To avoid being dependent on the specifics of the communication hardware or its driver, we propose to measure the number of messages the robot receives from the base as a control input. Leashing is then performed by having the robot react to low message rates by moving towards the base in order to improve the communication. Results show both in theory and reality that this strategy can leash the robot to the base in scenarios with limited wind or base mobility.

This paper presents a new paradigm in the design of indoor flying robots that replaces collision avoidance with collision robustness. Indoor flying robots must operate within constrained and cluttered environments where even nature's most sophisticated flyers such as insects cannot avoid all obstacles and should thus be able to withstand collisions and recover from them autonomously. A prototype platform specifically designed to withstand collisions and recover without human intervention is presented. Its dimensions are optimized to fulfill the varying constraints of aerodynamics, robustness and self-recovery, and new construction techniques focusing on shock absorption are presented. Finally, the platform is tested both in-flight and during collisions to characterize its collision robustness and self-recovery capability.

The ability to fly at low altitude while actively avoiding collisions with the terrain and other objects is a great challenge for small unmanned aircraft. This paper builds on top of a control strategy called optiPilot whereby a series of optic-flow detectors pointed at divergent viewing directions around the aircraft main axis are linearly combined into roll and pitch commands using two sets of weights. This control strategy already proved successful at controlling flight and avoiding collisions in reactive navigation experiments. This paper shows how optiPilot can be coupled with a GPS in order to provide goal-directed, nap-of-the-earth flight control in presence of static obstacles. Two fully autonomous flights of 25 minutes each are described where a 400-gram unmanned aircraft is flying at approx. 9 m above the terrain on a circular path including two copses of trees requiring efficient collision avoidance actions.

It is generally challenging to design decentralized controllers for swarms of robots because there is often no obvious relation between the individual robot behaviors and the final behavior of the swarm. As a solution, we use artificial evolution to automatically discover neural controllers for swarming robots. Artificial evolution has the potential to find simple and efficient strategies which might otherwise have been overlooked by a human designer. However, evolved controllers are often unadapted when used in scenarios that differ even slightly from those encountered during the evolutionary process. By reverse-engineering evolved controllers we aim towards hand-designed controllers which capture the simplicity and efficiency of evolved neural controllers while being easy to optimize for a variety of scenarios.

In nature, many animals are able to jump, upright themselves after landing and jump again. This allows them to move in unstructured and rough terrain. As a further development of our previously presented 7 g jumping robot, we consider various mechanisms enabling it to recover and upright after landing and jump again. After a weighted evaluation of these different solutions, we present a spherical system with a mass of 9.8 g and a diameter of 12 cm that is able to jump, upright itself after landing and jump again. In order to do so autonomously, it has a control unit and sensors to detect its orientation and spring charging state. With its current configuration it can overcome obstacles of 76 cm at a take-off angle of 75°.

Scanning laser range sensors (ladars) are frequently used in mobile robotics applications because their ability to accurately measure the environment in 3D makes them well-suited for perception tasks like terrain modeling and obstacle detection. The choice of ladar sensor and the manner in which it is configured and integrated into a robot platform is usually determined subjectively based on the experience of the project team members. This paper develops a method for evaluating ladar sensors and sensor configurations that objectively measures the quality of a sensor/configuration choice in terms of density and uniformity of measurements within a region of interest. The method is applicable to static sensors and environments as well as scenarios with moving objects and mobile sensors. It can be used to compare different sensors, to evaluate specific sensor configurations and search for the optimal one, and to aid in designing new ladar sensors tailored to specific applications. We find that popular ladar configurations are often not the best configuration choice, and that alternative configurations not commonly used would offer better data density and uniformity.

Most autopilots of existing miniature unmanned air vehicles (MUAVs) rely on control architectures that typically use a large number of sensors (gyros, accelerometers, magnetometers, GPS) and a computationally demanding estimation of flight states. As a consequence, they tend to be complex, require a significant amount of processing power and are usually expensive. Many research projects that aim at experiments with one, or even several, MUAVs would benefit from a simpler, potentially smaller, lighter and less expensive autopilot for their flying platforms. In this paper, we present a minimalist control strategy for fixed-wing MUAVs that provides the three basic functionalities of airspeed, altitude and heading turnrate control while only using two pressure sensors and a single-axis rate gyro. To achieve this, we use reactive control loops, which rely on direct feedback from the sensors instead of full state information. In order to characterize the control strategy, it was implemented on a custom-made autopilot. With data recorded during flight experiments, we carried out a statistical analysis of step responses to altitude and turnrate commands as well as responses to artificial perturbations.

In the growing field of collective robotics, spatial co-ordination between robots is often critical and usually achieved via local relative positioning sensors. We believe that range and bearing sensing, based on infrared technology, has the potential to fulfil the strict requirements of real-world collective robots. These requirements include: small size, light weight, large range, high refresh rate, immunity against tilting and misalignment, immunity against ambient light changes, and good range and bearing accuracy. Currently, there are no range and bearing systems that have been designed to cope with such strict requirements. This paper presents a custom range and bearing system, based on a novel cascaded filtering technology, complemented by hybrid infrared/Radio Frequency (RF) communication, which has been designed specifically to meet all these expectations. The system has been characterised and tested, proving its viability.

Jumping can be a very efficient mode of locomotion for small robots to overcome large obstacles and travel in natural, rough terrain. In this paper we present the development and characterization of a novel 5 cm, 7g jumping robot. It can jump obstacles more than 27 times its own size and outperforms existing jumping robots by one order of magnitude with respect to jump height per weight and jump height per size. It employs elastic elements in a four bar linkage leg system to allow for very powerful jumps and adjustment of the jumping force, take-off angle and force profile during the acceleration phase.

Flying has an advantage when compared to ground based locomotion, as it simplifies the task of overcoming obstacles and allows for rapid coverage of an area while also providing a birds-eye-view of the environment. One of the key challenges that has prevented engineers from coming up with convincing aerial solutions for indoor exploration is the energetic cost of flying. This paper presents a way of mitigating the energy problem regarding aerial exploration within indoor environments. This is achieved by means of a model to estimate the endurance of a hover-capable flying robot and by using ceiling attachment as a means of preserving energy while maintaining a birds-eye-view. The proposed model for endurance estimation has been extensively tested using a custom-developed quadrotor and autonomous ceiling attachment system.

Fully autonomous control of ultra-light indoor airplanes has not yet been achieved because of the strong limitations on the kind of sensors that can be embedded making it difficult to obtain good estimations of altitude. We propose to revisit altitude control by considering it as an obstacle avoidance problem and introduce a novel control scheme where the ground and ceiling is avoided based on translatory optic flow, in a way similar to existing vision-based wall avoidance strategies. We show that this strategy is successful at controlling a simulated microflyer without any explicit altitude estimation and using only simple sensors and processing that have already been embedded in an existing 10-gram microflyer. This result is thus a significant step toward autonomous control of indoor flying robots.

Unpowered flight can be used in microrobotics to overcome ground obstacles and to increase the traveling distance per energy unit. In order to explore the potential of goal-directed gliding in the domain of miniature robotics, we developed a 22cm microglider weighing a mere 1.5g and flying at around 1.5m/s. It is equipped with sensors and electronics to achieve phototaxis, which can be seen as a minimal level of control autonomy. A novel 0.2g Shape Memory Alloy (SMA) actuator for steering control has been specifically designed and integrated to keep the overall weight as low as possible. In order to characterize autonomous operation of this robot, we developed an experimental setup consisting of a launching device and a light source positioned 1m below and 4m away with varying angles with respect to the launching direction. Statistical analysis of 36 autonomous flights demonstrate its flight and phototaxis efficiency.

Autonomous control of ultra-light indoor microflyers is a difficult and largely unsolved task because of the strong limitations on the kind of sensors that can be embedded. We propose a new approach for altitude control of a 10-gram microflyer, where altitude as well as pitch angle are estimated using a set of visual, airspeed and gyroscopic sensors that weight about 1 (g) in total. This approach does not rely on an explicit estimation of optic flow, but rather takes as input the raw images as provided by the vision sensor. We show that altitude and pitch angle of a simulated agent can be successfully estimated. This result is thus a first step toward autonomous altitude control of indoor flying robots

We aim at developing ultralight autonomous microflyers capable of navigating within houses or small built environments. Our latest prototype is a fixed-wing aircraft weighing a mere 10 g, flying below 2 m/s and carrying the necessary electronics for airspeed regulation and obstacle avoidance. This microflyer is equipped with two tiny camera modules, two rate gyroscopes, an anemometer, a small microcontroller, and a Bluetooth radio module. In-flight tests are carried out in a new experimentation room specifically designed for easy changing of surrounding textures.

We aim at developing ultralight autonomous microflyers capable of navigating within houses or small built environments. Our latest prototype is a fixed-wing aircraft weighing a mere 10 g, flying around 1.5 m/s and carrying the necessary electronics for airspeed regulation and collision avoidance. This microflyer is equipped with two tiny camera modules, two rate gyroscopes, an anemometer, a small microcontroller, and a Bluetooth radio module. In-flight tests are carried out in a new experimentation room specifically designed for easy changing of surrounding textures

We describe evolution of spiking neural architectures to control navigation of autonomous mobile robots. Experimental results with simple fitness functions indicate that evolution can rapidly generate spiking circuits capable of navigating in textured environments with simple genetic representations that encode only the presence or absence of synaptic connections. Building on those results, we then describe a low-level implementation of evolutionary spiking circuits in tiny microcontrollers that capitalizes on compact genetic encoding and digital aspects of spiking neurons. The implementation is validated on a sugar-cube robot capable of developing functional spiking circuits for collision-free navigation. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1005–1024, 2006.

We aim at developing autonomous microflyers capable of navigating within houses or small indoor environments using vision as the principal source of information. Due to severe weight and energy constraints, inspiration is taken from the fly for the selection of sensors, for signal processing, and for the control strategy. The current 30-g prototype is capable of autonomous steering in a 16/spl times/16 m textured environment. This paper describes models and algorithms which allow for efficient course stabilization and collision avoidance using optic flow and inertial information.

We aim at developing autonomous micro-flyers capable of navigating within houses or small built environments. The severe weight and energy constraints of indoor flying platforms led us to take inspiration from flying insects for the selection of sensors, signal processing, and behaviors. This paper presents the control strategies enabling obstacle avoidance and altitude control using only optic flow and gyroscopic information. For experimental convenience, the control strategies are first implemented and tested separately on a small wheeled robot featuring the same hardware as the targeted aircraft. The obstacle avoidance system is then transferred to a 30-gram aircraft, which demonstrates autonomous steering within a square textured arena.

We give an overview of the EPFL indoor flying project, whose goal is to evolve neural controllers for autonomous, adaptive, indoor micro-flyers. Indoor flight is still a challenge because it requires miniaturization, energy efficiency, and control of nonlinear flight dynamics. This ongoing project consists of developing a flying, vision-based micro-robot, a bio-inspired controller composed of adaptive spiking neurons directly mapped into digital microcontrollers, and a method to evolve such a neural controller without human intervention. This article describes the motivation and methodology used to reach our goal as well as the results of a number of preliminary experiments on vision-based wheeled and flying robots.

We describe an incremental approach towards the development of autonomous indoor flyers that use only vision to navigate in textured environments. In order to cope with the severe weight and energy constraints of such systems, we use spiking neural controllers that can be implemented in tiny micro-controllers and map visual information into motor commands. The network morphology is evolved by means of an evolutionary process on the physical robots. This methodology is tested in three robots of increasing complexity, from a wheeled robot to a dirigible to a winged robot. The paper describes the approach, the robots, their degrees of complexity, and summarizes results. In addition, three compatible electronic boards and a choice of vision sensors suitable for these robots are described in more details. These boards allow a comparative and gradual development of spiking neural controllers for flying robots.

Developing a research autonomous plane for flying in a laboratory space is a challenge that forces one to understand the specific aerodynamic, power and construction constraints. In order to obtain a very slow flight while maintaining a high maneuverability, ultralight structures and adequate components are required. In this paper we analyze the wing, propeller and motor characteristics and propose a methodology to optimize the motor/gear/propeller system. The C4 model plane (50g, 1.5m/s) demonstrates the feasibility of such a laboratory flying test-bed.