Abstract-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 5cm, 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.

This paper presents a novel control strategy, which we call optiPilot, for autonomous flight in the vicinity of obstacles. Most existing autopilots rely on a complete 6-degree-of-freedom state estimation using a GPS and an Inertial Measurement Unit (IMU) and are unable to detect and avoid obstacles. This is a limitation for missions such as surveillance and environment monitoring that may require near-obstacle flight in urban areas or mountainous environments. OptiPilot instead uses optic flow to estimate proximity of obstacles and avoid them.Our approach takes advantage of the fact that, for most platforms in translational flight (as opposed to near-hover flight), the translatory motion is essentially aligned with the aircraft main axis. This property allows us to directly interpret optic flow measurements as proximity indications. We take inspiration from neural and behavioural strategies of flying insects to propose a simple mapping of optic flow measurements into control signals that requires only a lightweight and power-efficient sensor suite and minimal processing power.In this paper, we first describe results obtained in simulation before presenting the implementation of optiPilot on a real flying platform equipped only with lightweight and inexpensive optic computer mouse sensors, MEMS rate gyroscopes and a pressure-based airspeed sensor. We show that the proposed control strategy not only allows collision-free flight in the vicinity of obstacles, but is also able to stabilise both attitude and altitude over flat terrain. These re-

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ABSTRACT:This paper presents an affordable, fully automated and accurate mapping solutions based on ultra-light UAV imagery. Several datasets are analysed and their accuracy is estimated. We show that the accuracy highly depends on the ground resolution (flying height) of the input imagery. When chosen appropriately this mapping solution can compete with traditional mapping solutions that capture fewer high-resolution images from airplanes and that rely on highly accurate orientation and positioning sensors on board. Due to the careful integration with recent computer vision techniques, the post processing is robust and fully automatic and can deal with inaccurate position and orientation information which are typically problematic with traditional techniques.

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.

Jumping is used in nature by many small animals to locomote in cluttered environments or in rough terrain. It offers small systems the benefit of overcoming relatively large obstacles at a low energetic cost. In order to be able to perform repetitive jumps in a given direction, it is important to be able to upright after landing, steer and jump again. In this article, we review and evaluate the uprighting and steering principles of existing jumping robots and present a novel spherical robot with a mass of 14g and a size of 18cm that can jump up to 62cm at a take-off angle of 75 • , recover passively after landing, orient itself, and jump again. We describe its design details and fabrication methods, characterize its jumping performance, and demonstrate the remote

In most swarm systems, agents are either aware of the position of their direct neighbors or they possess a substrate on which they can deposit information (stigmergy). However, such resources are not always obtainable in real-world applications because of hardware and environmental constraints. In this paper we study in 2D simulation the design of a swarm system which does not make use of positioning information or stigmergy.This endeavor is motivated by an application whereby a large number of Swarming Micro Air Vehicles (SMAVs), of fixed-wing configuration, must organize autonomously to establish a wireless communication network (SMAVNET) between users located on ground. Rather than relative or absolute positioning, agents must rely only on their own heading measurements and local communication with neighbors.Designing local interactions responsible for the emergence of the SMAVNET deployment and maintenance is a challenging task. For this reason, artificial evolution is used to automatically develop neuronal controllers for the swarm of homogenous agents. This approach has the advantage of yielding original and efficient swarming strategies. A detailed behavioral analysis is then performed on the fittest swarm to gain insight as to the behavior of the individual agents.

Abstract-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. I. CHALLENGES AND STATE OF THE ARTThe idea of using flying robots to explore indoor environments has become popular within the robotic community in recent times1 2 . 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-eyeview 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, which is orders of magnitude higher than that of terrestrial locomotion.Imagine a robot that can fly around indoors, its task is to search a building for a pre-defined target, for example an injured human. It flies into a room and uses its onboard thermal vision sensors to scan the room for the injured human. After finding no positive matches the robot flies into the next room. The robot searches three rooms in this manner and locates the injured human in the last room. The robot has a limited amount of energy. If the robot was required to search more than these three rooms, then it is likely that its limit is reached before finding its target. If the robot could attach to the ceiling while it is searching the room, instead of remaining airborne, the search could be extended from minutes to hours, which could make all the difference in such a situation.Valenti and collaborators have developed a health management system to aid online mission planning for swarms of hovering Unmanned Air Vehicles (UAV) [6]. They have found that it is possible to estimate the remaining flight endurance by comparing the platforms battery voltage and This paper tackles the energy problem of aerial exploration within indoor environments, first by using ceiling attachment as a means for preserving energy, while still maintaining the birds-eye-view and second by providing an estimation model to estimate the endurance of a hover-capable flying robot. The proposed model for endurance estimation has been extensively tested using a custom-developed quadrotor and ceiling attachment system (Fig. 1). The ceiling attachment feature has been successfully demonstrated by autonomously flying ...