This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying neural models. Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, using consolidated tools in optimal control theory.
View Article and Find Full Text PDFPowered flight was once a capability limited only to animals, but by identifying useful attributes of animal flight and building on these with technological advances, engineers have pushed the frontiers of flight beyond our predecessors' wildest imaginations. Yet, there remain many key characteristics of biological flight that elude current aircraft design, motivating a careful re-analysis of what we have learned from animals already, and how this has been revealed experimentally, as well as a specific focus on identifying what remains unknown. Here, we review the literature to identify key contributions that began in biology and have since been translated into aeronautical devices or capabilities.
View Article and Find Full Text PDFFront Robot AI
December 2022
Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. These bio-inspired and lightweight flying robots still present limitations in their ability to fly in direct wind and gusts, as their stability is severely compromised in contrast with their biological counterparts. To this end, this work aims at making in-gust flight of flapping wing drones possible using an embodied airflow sensing approach combined with an adaptive control framework at the velocity and position control loops.
View Article and Find Full Text PDFAttitude control is an essential flight capability. Whereas flying robots commonly rely on accelerometers for estimating attitude, flying insects lack an unambiguous sense of gravity. Despite the established role of several sense organs in attitude stabilization, the dependence of flying insects on an internal gravity direction estimate remains unclear.
View Article and Find Full Text PDFNatural fliers utilize passive and active flight control strategies to cope with windy conditions. This capability makes them incredibly agile and resistant to wind gusts. Here, we study how insects achieve this, by combining Computational Fluid Dynamics (CFD) analyses of flying fruit flies with freely-flying robotic experiments.
View Article and Find Full Text PDFAutonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
November 2022
End-to-end trained convolutional neural networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks estimate optical flow.
View Article and Find Full Text PDFWhen approaching a landing surface, many flying animals use visual feedback to control their landing. Here, we studied how foraging bumblebees () use radial optic expansion cues to control in-flight decelerations during landing. By analyzing the flight dynamics of 4,672 landing maneuvers, we showed that landing bumblebees exhibit a series of deceleration bouts, unlike landing honeybees that continuously decelerate.
View Article and Find Full Text PDFThis work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group.
View Article and Find Full Text PDFThe identification and solution of a major efficiency loss in small flapping wing drones lead to more agile aerobatic maneuvers.
View Article and Find Full Text PDFThe combination of spiking neural networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This paper presents the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in an unsupervised fashion from the raw stimuli generated with an event-based camera. A novel adaptive neuron model and stable spike-timing-dependent plasticity formulation are at the core of this neural network governing its spike-based processing and learning, respectively.
View Article and Find Full Text PDFInsects are among the most agile natural flyers. Hypotheses on their flight control cannot always be validated by experiments with animals or tethered robots. To this end, we developed a programmable and agile autonomous free-flying robot controlled through bio-inspired motion changes of its flapping wings.
View Article and Find Full Text PDFRobust attitude control is an essential aspect of research on autonomous flight of flapping wing Micro Air Vehicles. The mechanical solutions by which the necessary control moments are realised come at the price of extra weight and possible loss of aerodynamic efficiency. Stable flight of these vehicles has been shown by several designs using a conventional tail, but also by tailless designs that use active control of the wings.
View Article and Find Full Text PDFTo avoid collisions, Micro Air Vehicles (MAVs) flying in teams require estimates of their relative locations, preferably with minimal mass and processing burden. We present a relative localization method where MAVs need only to communicate with each other using their wireless transceiver. The MAVs exchange on-board states (velocity, height, orientation) while the signal strength indicates range.
View Article and Find Full Text PDFOne of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute higher-level motor commands.
View Article and Find Full Text PDFThe visual cue of optical flow plays an important role in the navigation of flying insects, and is increasingly studied for use by small flying robots as well. A major problem is that successful optical flow control seems to require distance estimates, while optical flow is known to provide only the ratio of velocity to distance. In this article, a novel, stability-based strategy is proposed for monocular distance estimation, relying on optical flow maneuvers and knowledge of the control inputs (efference copies).
View Article and Find Full Text PDFEvolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this article we show the first application of the Behavior Tree framework on a real robotic platform using the evolutionary robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behavior over that of the traditional neural network formulation.
View Article and Find Full Text PDFTragopogon pratensis is a small herbaceous plant that uses wind as the dispersal vector for its seeds. The seeds are attached to parachutes that increase the aerodynamic drag force and increase the total distance travelled. Our hypothesis is that evolution has carefully tuned the air permeability of the seeds to operate in the most convenient fluid dynamic regime.
View Article and Find Full Text PDFIn this work, we exploit a computational model of human pre-attentive vision to guide the descent of a spacecraft on extraterrestrial bodies. Providing the spacecraft with high degrees of autonomy is a challenge for future space missions. Up to present, major effort in this research field has been concentrated in hazard avoidance algorithms and landmark detection, often by reference to a priori maps, ranked by scientists according to specific scientific criteria.
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