Optimality principles in spacecraft neural guidance and control.

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.

Accommodating unobservability to control flight attitude with optic flow.

Attitude 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.

Flying Into the Wind: Insects and Bio-Inspired Micro-Air-Vehicles With a Wing-Stroke Dihedral Steer Passively Into Wind-Gusts.

Natural 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.

Autonomous Flying With Neuromorphic Sensing.

Autonomous 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.

How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study.

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.

Bumblebees land rapidly and robustly using a sophisticated modular flight control strategy.

When 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.

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints.

This 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.

Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception.

The 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.

A tailless aerial robotic flapper reveals that flies use torque coupling in rapid banked turns.

Insects 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.

On-board communication-based relative localization for collision avoidance in Micro Air Vehicle teams.

To 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.

Abstraction, Sensory-Motor Coordination, and the Reality Gap in Evolutionary Robotics.

One 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.

Monocular distance estimation with optical flow maneuvers and efference copies: a stability-based strategy.

The 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).

Behavior Trees for Evolutionary Robotics.

Evolutionary 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.

Autonomous spacecraft landing through human pre-attentive vision.

In 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.