Knowledge-based vision and simple visual machines

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong

Neuromimetic Robots inspired by Insect Vision

International audienceEquipped with a less-than-one-milligram brain, insects fly autonomously in complex environments without resorting to any Radars, Ladars, Sonars or GPS. The knowledge gained during the last decades on insects' sensory-motor abilities and the neuronal substrates involved provides us with a rich source of inspiration for designing tomorrow's self-guided vehicles and micro-vehicles, which are to cope with unforeseen events on the ground, in the air, under water or in space. Insects have been in the business of sensory-motor integration for several 100 millions years and can therefore teach us useful tricks for designing agile autonomous vehicles at various scales. Constructing a "biorobot" first requires exactly formulating the signal processing principles at work in the animal. It gives us, in return, a unique opportunity of checking the soundness and robustness of those principles by bringing them face to face with the real physical world. Here we describe some of the visually-guided terrestrial and aerial robots we have developed on the basis of our biological findings. These robots (Robot Fly, SCANIA, FANIA, OSCAR, OCTAVE and LORA) all react to the optic flow (i.e., the angular speed of the retinal image). Optic flow is sensed onboard the robots by miniature vision sensors called Elementary Motion Detectors (EMDs). The principle of these electro-optical velocity sensors was derived from optical/electrophysiological studies where we recorded the responses of single neurons to optical microstimulation of single photoreceptor cells in a model visual system: the fly's compound eye. Optic flow based sensors rely solely on contrast provided by reflected (or scattered) sunlight from any kind of celestial bodies in a given spectral range. These nonemissive, powerlean sensors offer potential applications to manned or unmanned aircraft. Applications can also be envisaged to spacecraft, from robotic landers and rovers to asteroid explorers or space station dockers, with interesting prospects as regards reduction in weight and consumption

Biologically Inspired Guidance for Autonomous Systems

Animals and humans can perform purposeful actions using only their senses. Birds can perch on branches; bats use echolocation to hunt prey and humans are able to control vehicles. It must therefore be possible for autonomous systems to replicate this autonomous behaviour if an understanding of how animals and humans perceive their environment and guide their movements is obtained. Tau theory offers a potential explanation as to how this is achieved in nature. Tau theory posits, that in combination with the so-called ‘motion guides’, animals and humans perform useful movements by closing action-gaps, i.e. gaps between the current state and a desired state. The theory suggests that the variabl

A Service Robot for Navigation Assistance and Physical Rehabilitation of Seniors

The population of the advanced countries is ageing, with the direct consequence that an increasing number of people will have to live with sensitive, cognitive and physical disabilities. People with impaired physical ability are not confident to move alone, especially in crowded environment and for long journeys, highly reducing the quality of their life. We propose a new generation of robotic walking assistants whose mechanical and electronic components are conceived to optimize the collaboration between the robot and its users. We will apply these general ideas to investigate the interaction between older adults and a robotic walker, named FriWalk, exploiting it either as a navigational or as a rehabilitation aid. For the use of the FriWalk as a navigation assistance, the system guides the user securing high levels of safety, a perfect compliance with the social rules and non-intrusive interaction between human and machine. To this purpose, we developed several guidance systems ranging from completely passive strategies to active solutions exploiting either the rear or the front motors mounted on the robot. The common strategy at the basis of all the algorithms is that the responsibility of the locomotion belongs always to the user, both to increase the mobility of elder users and to enhance their perception of control over the robot. This way the robot intervenes only whenever it is strictly necessary not to mitigate the user safety. Moreover, the robotic walker has been endowed with a tablet and graphical user interface (GUI) which provides the user with the visual indications about the path to follow. Since the FriWalk was developed to suit the needs of users with different deficits, we conducted extensive human-robot interaction (HRI) experiments with elders, complemented with direct interviews of the participants. As concerns the use of the FriWalk as a rehabilitation aid, force sensing to estimate the torques applied by the user and change the user perceived inertia can be exploited by doctors to let the user feel the device heavier or lighter. Moreover, thanks to a new generation of sensors, the device can be exploited in a clinical context to track the performance of the users' rehabilitation exercises, in order to assist nurses and doctors during the hospitalization of older adults

Critical Scenario Identification for Testing of Autonomous Driving Systems

Background: Autonomous systems have received considerable attention from academia and are adopted by various industrial domains, such as automotive, avionics, etc. As many of them are considered safety-critical, testing is indispensable to verify their reliability and safety. However, there is no common standard for testing autonomous systems efficiently and effectively. Thus new approaches for testing such systems must be developed.Aim: The objective of this thesis is two-fold. First, we want to present an overview of software testing of autonomous systems, i.e., relevant concepts, challenges, and techniques available in academic research and industry practice. Second, we aim to establish a new approach for testing autonomous driving systems and demonstrate its effectiveness by using real autonomous driving systems from industry.Research Methodology: We conducted the research in three steps using the design science paradigm. First, we explored the existing literature and industry practices to understand the state of the art for testing of autonomous systems. Second, we focused on a particular sub-domain - autonomous driving - and proposed a systematic approach for critical test scenario identification. Lastly, we validated our approach and employed it for testing real autonomous driving systems by collaborating with Volvo Cars.Results: We present the results as four papers in this thesis. First, we conceptualized a definition of autonomous systems and classified challenges and approaches, techniques, and practices for testing autonomous systems in general. Second, we designed a systematic approach for critical test scenario identification. We employed the approach for testing two real autonomous driving systems from the industry and have effectively identified critical test scenarios. Lastly, we established a model for predicting the distribution of vehicle-pedestrian interactions for realistic test scenario generation for autonomous driving systems. Conclusion: Critical scenario identification is a favorable approach to generate test scenarios and facilitate the testing of autonomous driving systems in an efficient way. Future improvement of the approach includes (1) evaluating the effectiveness of the generated critical scenarios for testing; (2) extending the sub-components in this approach; (3) combining different testing approaches, and (4) exploring the application of the approach to test different autonomous systems

Assistive technology evolving as intelligent system

Different evolving technologies surround humans today. Among the various technologies, Assistive Technology has still not established itself firmly because there is an absence of proper integration of this technology with human life. However, in the future, it will become one of the most important and vital phenomena in everyone's life. Because humans want to make their life easier and longer and these are the reasons for the rapid growth in demand for Assistive Technology. Therefore, improvements in the technology and the way it is applied are essential and, for this reason, there is a requirement of a detailed study of the technology. This paper demonstrates the different milestones achieved in assistive technology by using different techniques to attempt to improve intelligence in assistive systems; and also, it describes the gaps that are still present even after such extensive works and, which are required to be either resolved or bridged. This study is done to understand where the assistive technology is today and in which direction it needs to get directed

Bio-inspired retinal optic flow perception in robotic navigation

This thesis concerns the bio-inspired visual perception of motion with emphasis on locomotion targeting robotic systems. By continuously registering moving visual features in the human retina, a sensation of a visual flow cue is created. An interpretation of visual flow cues forms a low-level motion perception more known as retinal optic flow. Retinal optic flow is often mentioned and credited in human locomotor research but only in theory and simulated environments so far. Reconstructing the retinal optic flow fields using existing methods of estimating optic flow and experimental data from naive test subjects provides further insight into how it interacts with intermittent control behavior and dynamic gazing. The retinal optic flow is successfully demonstrated during a vehicular steering task scenario and further supports the idea that humans may use such perception to aid their ability to correct their steering during navigation.To achieve the reconstruction and estimation of the retinal optic flow, a set of optic flow estimators were fairly and systematically evaluated on the criteria on run-time predictability and reliability, and performance accuracy. A formalized methodology using containerization technology for performing the benchmarking was developed to generate the results. Furthermore, the readiness in road vehicles for the adoption of modern robotic software and related software processes were investigated. This was done with special emphasis on real-time computing and introducing containerization and microservice design paradigm. By doing so, continuous integration, continuous deployment, and continuous experimentation were enabled in order to aid further development and research. With the method of estimating retinal optic flow and its interaction with intermittent control, a more complete vision-based bionic steering control model is to be proposed and tested in a live robotic system

Biologically Inspired Connected Advanced Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) have become commonplace in the automotive industry over the last few decades. Even with the advent of ADAS, however, there are still a significant number of accidents and fatalities. ADAS has in some instances been shown to significantly reduce the number and severity of accidents. Manufacturers are working to avoid ADAS plateauing for effectiveness, which has led the industry to pursue various avenues of investment to ascend the next mountain of challenges – vehicle autonomy, smart mobility, connectivity, and electrification – for reducing accidents and injuries. A number of studies pertaining to ADAS scrutinize a specific ADAS technology for its effectiveness at mitigating accidents and reducing injury severity. A few studies take holistic accounts of ADAS. There are a number of directions ADAS could be further progressed. Industry manufacturers are improving existing ADAS technologies through multiple avenues of technology advancement. A number of ADAS systems have already been improved from passive, alert or warning, systems to active systems which provide early warning and if no action is taken will control the vehicle to avoid a collision or reduce the impact of the collision. Studies about the individual ADAS technologies have found significant improvement for reduction in collisions, but when evaluating the actual vehicles driving the performance of ADAS has been fairly constant since 2015. At the same time, industry is looking at networking vehicle ADAS with fixed infrastructure or with other vehicles’ ADAS. The present literature surrounding connected ADAS be it with fixed systems or other vehicles with ADAS focuses on the why and the how information is passed between vehicles. The ultimate goal of ADAS and connected ADAS is the development of autonomous vehicles. Biologically inspired systems provide an intriguing avenue for examination by applying self-organization found in biological communities to connecting ADAS among vehicles and fixed systems. Biological systems developed over millions of years to become highly organized and efficient. Biological inspiration has been used with much success in several engineering and science disciplines to optimize processes and designs. Applying movement patterns found in nature to automotive transportation is a rational progression. This work strategizes how to further the effectiveness of ADAS through the connection of ADAS with supporting assets both fixed systems and other vehicles with ADAS based on biological inspiration. The connection priorities will be refined by the relative positioning of the assets interacting with a particular vehicle’s ADAS. Then based on the relative positioning data distribution among systems will be stratified based on level of relevance. This will reduce the processing time for incorporating the external data into the ADAS actions. This dissertation contributes to the present understanding of ADAS effectiveness in real-world situations and set forth a method for how to optimally connect local ADAS vehicles following from biological inspiration. Also, there will be a better understanding of how ADAS reduces accidents and injury severity. The method for how to structure an ADAS network will provide a framework for auto-manufacturers for the development of their proprietary networked ADAS. This method will lead to a new horizon for reducing accidents and injury severity through the design of connecting ADAS equipped vehicles.Ph.D