A toolbox for neuromorphic perception in robotics

The third generation of artificial intelligence (AI) introduced by neuromorphic computing is revolutionizing the way robots and autonomous systems can sense the world, process the information, and interact with their environment. Research towards fulfilling the promises of high flexibility, energy efficiency, and robustness of neuromorphic systems is widely supported by software tools for simulating spiking neural networks, and hardware integration (neuromorphic processors). Yet, while efforts have been made on neuromorphic vision (event-based cameras), it is worth noting that most of the sensors available for robotics remain inherently incompatible with neuromorphic computing, where information is encoded into spikes. To facilitate the use of traditional sensors, we need to convert the output signals into streams of spikes, i.e., a series of events (+1,-1) along with their corresponding timestamps. In this paper, we propose a review of the coding algorithms from a robotics perspective and further supported by a benchmark to assess their performance. We also introduce a ROS (Robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot. This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI, with the opportunity to adapt traditional off-the-shelf sensors to spiking neural nets within one of the most powerful robotic tools, ROS.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio

Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors

The great promises of neuromorphic sensing and processing for robotics have led researchers and engineers to investigate novel models for robust and reliable control of autonomous robots (navigation, obstacle detection and avoidance, etc.), especially for quadrotors in challenging contexts such as drone racing and aggressive maneuvers. Using spiking neural networks, these models can be run on neuromorphic hardware to benefit from outstanding update rates and high energy efficiency. Yet, low-level controllers are often neglected and remain outside of the neuromorphic loop. Designing low-level neuromorphic controllers is crucial to remove the standard PID, and therefore benefit from all the advantages of closing the neuromorphic loop. In this paper, we propose a parsimonious and adjustable neuromorphic PID controller, endowed with a minimal number of 93 neurons sparsely connected to achieve autonomous, onboard altitude control of a quadrotor equipped with Intel's Loihi neuromorphic chip. We successfully demonstrate the robustness of our proposed network in a set of experiments where the quadrotor is requested to reach a target altitude from take-off. Our results confirm the suitability of such low-level neuromorphic controllers, ultimately with a very high update frequency. Control & Simulatio

A Novel Multi-vision Sensor Dataset for Insect-Inspired Outdoor Autonomous Navigation

Insects have—over millions of years of evolution—perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency, one might want to look at and take inspiration from how these insects achieve their feats. Currently, no dataset exists that allows bio-inspired navigation models to be evaluated over long >100 m real-life routes. We present a novel dataset containing omnidirectional event vision, frame-based vision, depth frames, inertial measurement (IMU) readings, and centimeter-accurate GNSS positioning over kilometer long stretches in and around the TUDelft campus. The dataset is used to evaluate familiarity-based insect-inspired neural navigation models on their performance over longer sequences. It demonstrates that current scene familiarity models are not suited for long-ranged navigation, at least not in their current form.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio

Neuromorphic computing for attitude estimation onboard quadrotors

ompelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can achieve. Such promising features could be advantageous in critical embedded systems, especially in robotics. To date, the constraints inherent in robots (e.g., size and weight, battery autonomy, available sensors, computing resources, processing time, etc), and particularly in aerial vehicles, severely hamper the performance of fully-autonomous on-board control, including sensor processing and state estimation. In this work, we propose a spiking neural network capable of estimating the pitch and roll angles of a quadrotor in highly dynamic movements from six-degree of freedom inertial measurement unit data. With only 150 neurons and a limited training dataset obtained using a quadrotor in a real world setup, the network shows competitive results as compared to state-of-the-art, non-neuromorphic attitude estimators. The proposed architecture was successfully tested on the Loihi neuromorphic processor on-board a quadrotor to estimate the attitude when flying. Our results show the robustness of neuromorphic attitude estimation and pave the way toward energy-efficient, fully autonomous control of quadrotors with dedicated neuromorphic computing systems.Control & Simulatio

A Novel Obstacle Detection and Avoidance Dataset for Drones

In this paper, we introduce the Obstacle Detection and Avoidance (ODA) Dataset for Drones, aiming at providing raw data obtained in a real indoor environment with sensors adapted for aerial robotics in the context of obstacle detection and avoidance. Our micro air vehicle (MAV) is equipped with the following sensors: (i) an event-based camera, the performance of which makes it optimized for drone applications; (ii) a standard RGB camera; (iii) a 24-GHz radar sensor to enhance multi-sensory solutions; and (iv) a 6-Axes IMU. The ground truth position and attitude are provided by an OptiTrack motion capture system. The resulting dataset consists of more than 1350 sequences obtained in four distinct conditions (one or two obstacles, full or dim light). It is intended for benchmarking algorithmic and neural solutions for obstacle detection and avoidance with UAVs, but also course estimation and in general autonomous navigation. The dataset is available at: https://github.com/tudelft/ODA_Dataset [6]. Control & Simulatio

Insect-inspired AI for autonomous robots

Autonomous robots are expected to perform a wide range of sophisticated tasks in complex, unknown environments. However, available onboard computing capabilities and algorithms represent a considerable obstacle to reaching higher levels of autonomy, especially as robots get smaller and the end of Moore's law approaches. Here, we argue that inspiration from insect intelligence is a promising alternative to classic methods in robotics for the artificial intelligence (AI) needed for the autonomy of small, mobile robots. The advantage of insect intelligence stems from its resource efficiency (or parsimony) especially in terms of power and mass. First, we discuss the main aspects of insect intelligence underlying this parsimony: embodiment, sensory-motor coordination, and swarming. Then, we take stock of where insect-inspired AI stands as an alternative to other approaches to important robotic tasks such as navigation and identify open challenges on the road to its more widespread adoption. Last, we reflect on the types of processors that are suitable for implementing insect-inspired AI, from more traditional ones such as microcontrollers and field-programmable gate arrays to unconventional neuromorphic processors. We argue that even for neuromorphic processors, one should not simply apply existing AI algorithms but exploit insights from natural insect intelligence to get maximally efficient AI for robot autonomy.Control & Simulatio

Accommodating unobservability to control flight attitude with optic flow

Attitude control is an essential flight capability. Whereas flying robots commonly rely on accelerometers1 for estimating attitude, flying insects lack an unambiguous sense of gravity2,3. Despite the established role of several sense organs in attitude stabilization3–5, the dependence of flying insects on an internal gravity direction estimate remains unclear. Here we show how attitude can be extracted from optic flow when combined with a motion model that relates attitude to acceleration direction. Although there are conditions such as hover in which the attitude is unobservable, we prove that the ensuing control system is still stable, continuously moving into and out of these conditions. Flying robot experiments confirm that accommodating unobservability in this manner leads to stable, but slightly oscillatory, attitude control. Moreover, experiments with a bio-inspired flapping-wing robot show that residual, high-frequency attitude oscillations from flapping motion improve observability. The presented approach holds a promise for robotics, with accelerometer-less autopilots paving the road for insect-scale autonomous flying robots6. Finally, it forms a hypothesis on insect attitude estimation and control, with the potential to provide further insight into known biological phenomena5,7,8 and to generate new predictions such as reduced head and body attitude variance at higher flight speeds9.Control & SimulationEducation A

An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System

This paper discusses a low-cost, open-source and open-hardware design and performance evaluation of a low-speed, multi-fan wind system dedicated to micro air vehicle (MAV) testing. In addition, a set of experiments with a flapping wing MAV and rotorcraft is presented, demonstrating the capabilities of the system and the properties of these different types of drones in response to various types of wind. We performed two sets of experiments where a MAV is flying into the wake of the fan system, gathering data about states, battery voltage and current. Firstly, we focus on steady wind conditions with wind speeds ranging from 0.5 m S-1 to 3.4 m S-1. During the second set of experiments, we introduce wind gusts, by periodically modulating the wind speed from 1.3 m S−1 to 3.4 m S−1 with wind gust oscillations of 0.5 Hz, 0.25 Hz and 0.125 Hz. The “Flapper” flapping wing MAV requires much larger pitch angles to counter wind than the “CrazyFlie” quadrotor. This is due to the Flapper's larger wing surface. In forward flight, its wings do provide extra lift, considerably reducing the power consumption. In contrast, the CrazyFlie's power consumption stays more constant for different wind speeds. The experiments with the varying wind show a quicker gust response by the CrazyFlie compared with the Flapper drone, but both their responses could be further improved. We expect that the proposed wind gust system will provide a useful tool to the community to achieve such improvements.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio

Insect-inspired robots: Bridging biological and artificial systems

This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.Control & Simulatio