We are witnessing the advent of a new era of robots - drones - that can autonomously fly in natural and man-made environments. These robots, often associated with defence applications, could have a major impact on civilian tasks, including transportation, communication, agriculture, disaster mitigation and environment preservation. Autonomous flight in confined spaces presents great scientific and technical challenges owing to the energetic cost of staying airborne and to the perceptual intelligence required to negotiate complex environments. We identify scientific and technological advances that are expected to translate, within appropriate regulatory frameworks, into pervasive use of autonomous drones for civilian applications.

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Science, technology and the future of small autonomous drones

Deep artificial neural networks apply principles of the brain’s information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels. Despite several decades of research, until recently, very few published results have shown that today’s neuromorphic chips can demonstrate quantitative computational value. This is now changing with the advent of Intel’s Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures. This survey reviews results that are obtained to date with Loihi across the major algorithmic domains under study, including deep learning approaches and novel approaches that aim to more directly harness the key features of spike-based neuromorphic hardware. While conventional feedforward deep neural networks show modest if any benefit on Loihi, more brain-inspired networks using recurrence, precise spike-timing relationships, synaptic plasticity, stochasticity, and sparsity perform certain computation with orders of magnitude lower latency and energy compared to state-of-the-art conventional approaches. These compelling neuromorphic networks solve a diverse range of problems representative of brain-like computation, such as event-based data processing, adaptive control, constrained optimization, sparse feature regression, and graph search.

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Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

In this paper we present the design and control of a novel six degrees-of-freedom aerial vehicle. Based on a static force and torque analysis for generic actuator configurations, we derive an eight-rotor configuration that maximizes the vehicle's agility in any direction. The proposed vehicle design possesses full force and torque authority in all three dimensions. A control strategy that allows for exploiting the vehicle's decoupled translational and rotational dynamics is introduced. A prototype of the proposed vehicle design is built using reversible motor-propeller actuators and capable of flying at any orientation. Preliminary experimental results demonstrate the feasibility of the novel design and the capabilities of the vehicle.

Design, modeling and control of an omni-directional aerial vehicle

http://flyingmachinearena.org/wp-content/publications/2014/lupashin2014platform.pdf

A platform for aerial robotics research and demonstration: The Flying Machine Arena

The recent advances in state estimation, perception, and navigation algorithms have significantly contributed to the ubiquitous use of quadrotors for inspection, mapping, and aerial imaging. To further increase the versatility of quadrotors, recent works investigated the use of an adaptive morphology, which consists of modifying the shape of the vehicle during flight to suit a specific task or environment. However, these works either increase the complexity of the platform or decrease its controllability. In this letter, we propose a novel, simpler, yet effective morphing design for quadrotors consisting of a frame with four independently rotating arms that fold around the main frame. To guarantee stable flight at all times, we exploit an optimal control strategy that adapts on the fly to the drone morphology. We demonstrate the versatility of the proposed adaptive morphology in different tasks, such as negotiation of narrow gaps, close inspection of vertical surfaces, and object grasping and transportation. The experiments are performed on an actual, fully autonomous quadrotor relying solely on onboard visual-inertial sensors and compute. No external motion tracking systems and computers are used. This is the first work showing stable flight without requiring any symmetry of the morphology.

/pdf/the-foldable-drone-a-morphing-quadrotor-that-can-squeeze-and-1fmqg3g2kk.pdf

The Foldable Drone: A Morphing Quadrotor That Can Squeeze and Fly

This paper presents a novel system for autonomous,vision-based drone racing combining learned data abstraction,nonlinear filtering, and time-optimal trajectory planning. Thesystem has successfully been deployed at the first autonomousdrone racing world championship: the2019 AlphaPilot Challenge.Contrary to traditional drone racing systems, which only detectthe next gate, our approach makes use of any visible gate andtakes advantage of multiple, simultaneous gate detections tocompensate for drift in the state estimate and build a global mapof the gates. The global map and drift-compensated state estimateallow the drone to navigate through the race course even whenthe gates are not immediately visible and further enable to plana near time-optimal path through the race course in real timebased on approximate drone dynamics. The proposed system hasbeen demonstrated to successfully guide the drone through tightrace courses reaching speeds up to8 m/sand ranked second atthe2019 AlphaPilot Challenge

http://www.roboticsproceedings.org/rss16/p081.pdf

AlphaPilot: Autonomous Drone Racing

We present the design of a system that allows quadrocopters to balance an inverted pendulum, throw it into the air, and catch and balance it again on a second vehicle. Based on first principles models, a launch condition for the pole is derived and used to design an optimal trajectory to throw the pole towards a second quadrocopter. An optimal catching instant is derived and the corresponding position is predicted by simulating the current position and velocity estimates forward in time. An algorithm is introduced that generates a trajectory for moving the catching vehicle to the predicted catching point in real time. By evaluating the pole state after the impact, an adaptation strategy adapts the catch maneuver such that the pole rotates into the upright equilibrium by itself. Experimental results demonstrate the performance of the system.

/pdf/quadrocopter-pole-acrobatics-1qz8rdf2j8.pdf

Quadrocopter pole acrobatics

Nonlinear Quadrocopter Attitude Control

This paper presents an attitude tracking control law and control allocation strategy for quadrocopters which prioritizes the vehicle’s ability to achieve a desired translational acceleration. The quadrocopter’s attitude error is split into a reduced attitude error, which describes the misalignment of the thrust direction, and a yaw error, which describes the orientation error about the thrust direction. A model-based proportional–derivative control law is derived, where the proportional action is in terms of the reduced attitude and yaw errors, and the derivative action is in terms of the quadrocopter’s angular velocity error. Almost global asymptotic convergence of the reduced attitude error is established, and the convergence of the yaw error is proven. It is further shown that the attitude control law decouples the reduced attitude error dynamics from the yaw error dynamics. A control allocation strategy is derived which exploits the decoupling in order to prioritize the correction of the reduced attitude over the yaw error. The proposed control strategy is computationally lightweight and therefore well-suited to run on board quadrocopters at high rates. Experimental results demonstrate improved error recovery and position tracking performance.

Dario Brescianini

Papers

Design, modeling and control of an omni-directional aerial vehicle

AlphaPilot: Autonomous Drone Racing

Quadrocopter pole acrobatics

Nonlinear Quadrocopter Attitude Control

Tilt-Prioritized Quadrocopter Attitude Control