Insect Vision: A Few Tricks to Regulate Flight Altitude

SummaryA recent study sheds new light on the visual cues used by Drosophila to regulate flight altitude. The striking similarity with previously identified steering mechanisms provides a coherent basis for novel models of vision-based flight control in insects and robots

Vision-based control of near-obstacle flight

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 results shed new light on flight control by suggesting that the complex sensors and processing required for 6 degree-of-freedom state estimation may not be necessary for autonomous flight and pave the way toward the integration of autonomy into current and upcoming gram-scale flying platform

Evolved swarming without positioning information: anapplication in aerial communication relay

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 agent

Toward Indoor 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, ultra-light 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

3-D relative positioning sensor for indoor flying robots

Swarms of indoor flying robots are promising for many applications, including searching tasks in collapsing buildings, or mobile surveillance and monitoring tasks in complex man-made structures. For tasks that employ several flying robots, spatial-coordination between robots is essential for achieving collective operation. However, there is a lack of on-board sensors capable of sensing the highly-dynamic 3-D trajectories required for spatial-coordination of small indoor flying robots. Existing sensing methods typically utilise complex SLAM based approaches, or absolute positioning obtained from off-board tracking sensors, which is not practical for real-world operation. This paper presents an adaptable, embedded infrared based 3-D relative positioning sensor that also operates as a proximity sensor, which is designed to enable inter-robot spatial-coordination and goal-directed flight. This practical approach is robust to varying indoor environmental illumination conditions and is computationally simpl

Flying over the reality gap: From simulated to real indoor airships

Because of their ability to naturally float in the air, indoor airships (often called blimps) constitute an appealing platform for research in aerial robotics. However, when confronted to long lasting experiments such as those involving learning or evolutionary techniques, blimps present the disadvantage that they cannot be linked to external power sources and tend to have little mechanical resistance due to their low weight budget. One solution to this problem is to use a realistic flight simulator, which can also significantly reduce experimental duration by running faster than real time. This requires an efficient physical dynamic modelling and parameter identification procedure, which are complicated to develop and usually rely on costly facilities such as wind tunnels. In this paper, we present a simple and efficient physics-based dynamic modelling of indoor airships including a pragmatic methodology for parameter identification without the need for complex or costly test facilities. Our approach is tested with an existing blimp in a vision-based navigation task. Neuronal controllers are evolved in simulation to map visual input into motor commands in order to steer the flying robot forward as fast as possible while avoiding collisions. After evolution, the best individuals are successfully transferred to the physical blimp, which experimentally demonstrates the efficiency of the proposed approac

Optic-flow-based steering and altitude control for ultra-light indoor aircraft

Our goal is to demonstrate the ability of bio-inspired techniques to solve the problem of piloting an autonomous 40-grams aircraft within textured indoor environments. Because of severe weight and energy constraints, inspiration has been taken from the fly and only visual and vestibular-like sensors are employed. This paper describes the models and algorithms that will be used for altitude control and frontal obstacle avoidance, mainly relying on optic flow. For experimental convenience, both mechanisms have first been implemented and tested on a small wheeled robot featuring the same hardware as the targeted aircraft

Steerable miniature jumping robot

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 controlled prototype repetitively moving over an obstacle course where it has to climb stairs and go through a window. (See videos 1-4 in the electronic supplementary material.

Toward 30-gram Autonomous Indoor Aircraft: Vision-based Obstacle Avoidance and Altitude Control

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

Optic-flow-based Navigation for Ultralight Indoor Aircraft

The goal of this project is to develop an autonomous microflyer capable of navigating within houses or small built environments using vision as main source of information. Flying indoor implies a number of challenges that are not found in outdoor autonomous flight. These include small size and slow speed for maneuverability, light weight to stay airborne, low-consumption electronics, and smart sensing and control to fly in textured environment