Biologically inspired vision systems in robotics

During the last years, the International Journal of Advanced Robotic Systems, under the Topic of Vision Systems, especially welcomes papers that cover any aspect of biologically inspired vision in robots. As Guest Editors of the Special Issue on “Biologically Inspired Vision Systems in Robotics,” we feel that living beings have still much to tell us about the design and development of robotics

Features and Cost Comparison of Biologically Inspired Vision Systems

The economic analysis of the advantages of known analogues of biologically inspired systems for unmanned aerial vehicles (UAVs), quadrocopters, etc

Straight or curved? From deterministic to probabilistic models of 3D motion perception

A commentary on Detection of 3D curved trajectories: the role of binocular disparity by Pierce, R. S., Bian, Z., Braunstein, M. L., and Andersen, G. J. (2013). Front. Behav. Neurosci. 7:12. doi: 10.3389/fnbeh.2013.0001

Biomimetic visual navigation in a corridor: to centre or not to centre?

International audienceAs a first step toward an Automatic Flight Control System (AFCS) for Micro-Air Vehicle (MAV) obstacle avoidance, we introduce a vision based autopilot (LORA: Lateral Optic flow Regulation Autopilot), which is able to make a hovercraft automatically follow a wall or centre between the two walls of a corridor. A hovercraft is endowed with natural stabilization in pitch and roll while keeping two translational degrees of freedom (X and Y) and one rotational degree of freedom (yaw Ψ). We show the feasibility of an OF regulator that maintains the lateral Optic Flow (OF) on one wall equal to an OF set-point. The OF sensors used are Elementary Motion Detectors (EMDs), whose working was directly inspired by the housefly motion detecting neurons. The properties of these neurons were previously analysed at our laboratory by performing electrophysiological recordings while applying optical microstimuli to single photoreceptor cells of the compound eye. The simulation results show that depending on the OF set-point, the hovercraft either centres along the midline of the corridor or follows one of the two walls, even with local lack of optical texture on one wall, such as caused, for instance, by an open door or a T-junction. All these navigational tasks are performed with one and the same feedback loop, which consists of a lateral OF regulation loop that permits relatively high-speed navigation (1m/s, i.e 3 body-lengths per second). The passive visual sensors and the simple processing system are suitable for use with MAVs with an avionic payload of only a few grams. The goal is to achieve MAV automatic guidance or to relieve a remote operator from guiding it in challenging environments such as urban canyons or indoor environments

Low-power low-noise CMOS amplifier for neural recording applications

Journal ArticleThere is a need among scientists and clinicians for low-noise low-power biosignal amplifiers capable of amplifying signals in the millihertz-to-kilohertz range while rejecting large dc offsets generated at the electrode-tissue interface. The advent of fully implantable multielectrode arrays has created the need for fully integrated micropower amplifiers. We designed and tested a novel bioamplifier that uses a MOS-bipolar pseudoresistor element to amplify low-frequency signals down to the millihertz range while rejecting large dc offsets. We derive the theoretical noise-power tradeoff limit-the noise efficiency factor-for this amplifier and demonstrate that our VLSI implementation approaches this limit by selectively operating MOS transistors in either weak or strong inversion. The resulting amplifier, built in a standard 1.5- m CMOS process, passes signals from 0.025 Hz to 7.2 kHz with an input-referred noise of 2.2 Vrms and a power dissipation of 80 W while consuming 0.16 mm2 of chip area. Our design technique was also used to develop an electroencephalogram amplifier having a bandwidth of 30 Hz and a power dissipation of 0.9 W while maintaining a similar noise-power tradeoff

CMOS analog map decoder for (8,4) hamming code

Journal ArticleAbstract-Design and test results for a fully integrated translinear tail-biting MAP error-control decoder are presented. Decoder designs have been reported for various applications which make use of analog computation, mostly for Viterbi-style decoders. MAP decoders are more complex, and are necessary components of powerful iterative decoding systems such as Turbo codes. Analog circuits may require less area and power than digital implementations in high-speed iterative applications. Our (8, 4) Hamming decoder, implemented in an AMI 0.5- m process, is the first functioning CMOS analog MAP decoder. While designed to operate in subthreshold, the decoder also functions above threshold with a small performance penalty. The chip has been tested at bit rates up to 2 Mb/s, and simulations indicate a top speed of about 10 Mb/s in strong inversion. The decoder circuit size is 0.82 mm2, and typical power consumption is 1 mW at 1 Mb/s

Vision-based people detection using depth information for social robots: an experimental evaluation

Robots are starting to be applied in areas which involve sharing space with humans. In particular, social robots and people will coexist closely because the former are intended to interact with the latter. In this context, it is crucial that robots are aware of the presence of people around them. Traditionally, people detection has been performed using a flow of two-dimensional images. However, in nature, animals' sight perceives their surroundings using color and depth information. In this work, we present new people detectors that make use of the data provided by depth sensors and red-green-blue images to deal with the characteristics of human-robot interaction scenarios. These people detectors are based on previous works using two-dimensional images and existing people detectors from different areas. The disparity of the input and output data used by these types of algorithms usually complicates their integration into robot control architectures. We propose a common interface that can be used by any people detector, resulting in numerous advantages. Several people detectors using depth information and the common interface have been implemented and evaluated. The results show a great diversity among the different algorithms. Each one has a particular domain of use, which is reflected in the results. A clever combination of several algorithms appears as a promising solution to achieve a flexible, reliable people detector.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research leading to these results has received funding from the projects Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad, and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU

On the potential role of lateral connectivity in retinal anticipation

We analyse the potential effects of lateral connectivity (amacrine cells and gap junctions) on motion anticipation in the retina. Our main result is that lateral connectivity can-under conditions analysed in the paper-trigger a wave of activity enhancing the anticipation mechanism provided by local gain control [8, 17]. We illustrate these predictions by two examples studied in the experimental literature: differential motion sensitive cells [1] and direction sensitive cells where direction sensitivity is inherited from asymmetry in gap junctions connectivity [73]. We finally present reconstructions of retinal responses to 2D visual inputs to assess the ability of our model to anticipate motion in the case of three different 2D stimuli

Evolutionary Many-objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation

Many real-world optimization problems have more than three objectives, which has triggered increasing research interest in developing efficient and effective evolutionary algorithms for solving many-objective optimization problems. However, most many-objective evolutionary algorithms have only been evaluated on benchmark test functions and few applied to real-world optimization problems. To move a step forward, this paper presents a case study of solving a many-objective hybrid electric vehicle controller design problem using three state-of-the-art algorithms, namely, a decomposition based evolutionary algorithm (MOEA/D), a non-dominated sorting based genetic algorithm (NSGA-III), and a reference vector guided evolutionary algorithm (RVEA). We start with a typical setting aiming at approximating the Pareto front without introducing any user preferences. Based on the analyses of the approximated Pareto front, we introduce a preference articulation method and embed it in the three evolutionary algorithms for identifying solutions that the decision-maker prefers. Our experimental results demonstrate that by incorporating user preferences into many-objective evolutionary algorithms, we are not only able to gain deep insight into the trade-off relationships between the objectives, but also to achieve high-quality solutions reflecting the decision-maker’s preferences. In addition, our experimental results indicate that each of the three algorithms examined in this work has its unique advantages that can be exploited when applied to the optimization of real-world problems

Bidirectional long short-term memory network for proto-object representation

Researchers have developed many visual saliency models in order to advance the technology in computer vision. Neural networks, Convolution Neural Networks (CNNs) in particular, have successfully differentiate objects in images through feature extraction. Meanwhile, Cummings et al. has proposed a proto-object image saliency (POIS) model that shows perceptual objects or shapes can be modelled through the bottom-up saliency algorithm. Inspired from their work, this research is aimed to explore the imbedding features in the proto-object representations and utilizing artificial neural networks (ANN) to capture and predict the saliency output of POIS. A combination of CNN and a bi-directional long short-term memory (BLSTM) neural network is proposed for this saliency model as a machine learning alternative to the border ownership and grouping mechanism in POIS. As ANNs become more efficient in performing visual saliency tasks, the result of this work would extend their application in computer vision through successful implementation for proto-object based saliency