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Register a new userFinding the Gap: Neuromorphic Motion Vision in Cluttered Environments
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Added by
Thorben Schoepe
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Many animals meander in environments and avoid collisions. How the underlying neuronal machinery can yield robust behaviour in a variety of environments remains unclear. In the fly brain, motion-sensitive neurons indicate the presence of nearby objects and directional cues are integrated within an area known as the central complex. Such neuronal machinery, in contrast with the traditional stream-based approach to signal processing, uses an event-based approach, with events occurring when changes are sensed by the animal. Contrary to von Neumann computing architectures, event-based neuromorphic hardware is designed to process information in an asynchronous and distributed manner. Inspired by the fly brain, we model, for the first time, a neuromorphic closed-loop system mimicking essential behaviours observed in flying insects, such as meandering in clutter and gap crossing, which are highly relevant for autonomous vehicles. We implemented our system both in software and on neuromorphic hardware. While moving through an environment, our agent perceives changes in its surroundings and uses this information for collision avoidance. The agents manoeuvres result from a closed action-perception loop implementing probabilistic decision-making processes. This loop-closure is thought to have driven the development of neural circuitry in biological agents since the Cambrian explosion. In the fundamental quest to understand neural computation in artificial agents, we come closer to understanding and modelling biological intelligence by closing the loop also in neuromorphic systems. As a closed-loop system, our system deepens our understanding of processing in neural networks and computations in biological and artificial systems. With these investigations, we aim to set the foundations for neuromorphic intelligence in the future, moving towards leveraging the full potential of neuromorphic systems.
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The sampling based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT called RRT* has been proposed that ensures asymptotic optimality. Subsequently its bidirectional version has also been introduced in the literature known as Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT* and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed for complex cluttered environments. IB-RRT* utilizes the bidirectional trees approach and introduces intelligent sample insertion heuristic for fast convergence to the optimal path solution using uniform sampling heuristics. The proposed algorithm is evaluated theoretically and experimental results are presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental results demonstrate the superior efficiency of IB-RRT* in comparison with RRT* and B-RRT in complex cluttered environments.
Insects have tiny brains but complicated visual systems for motion perception. A handful of insect visual neurons have been computationally modeled and successfully applied for robotics. How different neurons collaborate on motion perception, is an open question to date. In this paper, we propose a novel embedded vision system in autonomous micro-robots, to recognize motion patterns in dynamic robot scenes. Here, the basic motion patterns are categorized into movements of looming (proximity), recession, translation, and other irrelevant ones. The presented system is a synthetic neural network, which comprises two complementary sub-systems with four spiking neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for sensing looming and recession, and the direction selective neurons (DSN-R and DSN-L) in flies for translational motion extraction. Images are transformed to spikes via spatiotemporal computations towards a switch function and decision making mechanisms, in order to invoke proper robot behaviors amongst collision avoidance, tracking and wandering, in dynamic robot scenes. Our robot experiments demonstrated two main contributions: (1) This neural vision system is effective to recognize the basic motion patterns corresponding to timely and proper robot behaviors in dynamic scenes. (2) The arena tests with multi-robots demonstrated the effectiveness in recognizing more abundant motion features for collision detection, which is a great improvement compared with former studies.
We present an open-source system for Micro-Aerial Vehicle autonomous navigation from vision-based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field of view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real-world scenarios are presented. We focus our experiments on evaluating global planning, path smoothing, and local planning methods on real maps made on MAVs in realistic search and rescue and industrial inspection scenarios. We also perform thousands of simulations in cluttered synthetic environments, and finally validate the complete system in real-world experiments.
Decoding the direction of translating objects in front of cluttered moving backgrounds, accurately and efficiently, is still a challenging problem. In nature, lightweight and low-powered flying insects apply motion vision to detect a moving target in highly variable environments during flight, which are excellent paradigms to learn motion perception strategies. This paper investigates the fruit fly textit{Drosophila} motion vision pathways and presents computational modelling based on cutting-edge physiological researches. The proposed visual system model features bio-plausible ON and OFF pathways, wide-field horizontal-sensitive (HS) and vertical-sensitive (VS) systems. The main contributions of this research are on two aspects: 1) the proposed model articulates the forming of both direction-selective (DS) and direction-opponent (DO) responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; 2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response. Accordingly, the direction of translating objects is decoded as global responses of both the HS and VS systems with positive or negative output indicating preferred-direction (PD) or null-direction (ND) translation. The experiments have verified the effectiveness of the proposed neural system model, and demonstrated its responsive preference to faster-moving, higher-contrast and larger-size targets embedded in cluttered moving backgrounds.
Neuromorphic computing systems uses non-volatile memory (NVM) to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate NVMs cause aging of CMOS-based transistors in each neuron and synapse circuit in the hardware, drifting the transistors parameters from their nominal values. Aggressive device scaling increases power density and temperature, which accelerates the aging, challenging the reliable operation of neuromorphic systems. Existing reliability-oriented techniques periodically de-stress all neuron and synapse circuits in the hardware at fixed intervals, assuming worst-case operating conditions, without actually tracking their aging at run time. To de-stress these circuits, normal operation must be interrupted, which introduces latency in spike generation and propagation, impacting the inter-spike interval and hence, performance, e.g., accuracy. We propose a new architectural technique to mitigate the aging-related reliability problems in neuromorphic systems, by designing an intelligent run-time manager (NCRTM), which dynamically destresses neuron and synapse circuits in response to the short-term aging in their CMOS transistors during the execution of machine learning workloads, with the objective of meeting a reliability target. NCRTM de-stresses these circuits only when it is absolutely necessary to do so, otherwise reducing the performance impact by scheduling de-stress operations off the critical path. We evaluate NCRTM with state-of-the-art machine learning workloads on a neuromorphic hardware. Our results demonstrate that NCRTM significantly improves the reliability of neuromorphic hardware, with marginal impact on performance.
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