Robotic and Sensor Technologies for Mobility in Older People

Maintaining independent mobility is fundamental to independent living and to the quality of life of older people. Robotic and sensor technologies may offer a lot of potential and can make a significant difference in the lives of older people and to their primary caregivers. The aim of this study was to provide a presentation of the methods that are used up till now for analysis and evaluation of human mobility utilizing sensor technologies and to give the state of the art in robotic platforms for supporting older people with mobility limitations. The literature was reviewed and systematic reviews of cohort studies and other authoritative reports were identified. The selection criteria included (1) patients with age â\u89¥60 years; (2) patients with unstable gait, with or without recurrent falls; (3) patients with slow movements, short strides, and little trunk movement; (4) sensor technologies that are currently used for mobility evaluation; and (5) robotic technologies that can serve as a supporting companion for older people with mobility limitations. One hundred eighty-one studies published up until February 2017 were identified, of which 36 were included. Two categories of research were identified from the review regarding the robot and sensor technologies: (1) sensor technologies for mobility analysis and (2) robots for supporting older people with mobility limitations. Potential for robotic and sensor technologies can be taken advantage of for evaluation and support at home for elder persons with mobility limitations in an automated way without the need of the physical presence of any medical personnel, reducing the stress of caregivers

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots

This letter presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modeling long-term, pseudo-periodic variations caused by human activities or natural processes. Unlike previous approaches, the proposed method does not treat time and space separately, and its continuous nature respects both the temporal and spatial continuity of the modeled phenomena. The key idea is to extend the spatial model with a set of wrapped time dimensions that represent the periodicities of the observed events. By performing clustering over this extended representation, we obtain a model that allows the prediction of probabilistic distributions of future states and events in both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets acquired by mobile robots and show that the method enables a robot to predict future states of representations with different dimensions. The experiments further show that the method achieves more accurate predictions than the previous state of the art

Inferring Temporal Models of People Presence from Environment Structrure

Cílem této práce je prezentovat projekt FreMEn contra COVID, jeho technickou stránku, a experimentálně ohodnotit jeho přínos. Aby bylo možné systém nasadit v nových oblastech i přes malé množství dat, možnost přenosu chronorobotických temporálních modelů, při modelovaní lidského davového chování, je taktéž testována. Přenos temporálních modelů je způsob, jak se vypořádat s extrémně malými množství dat, běžnými pro robotiku, která jsou velkým problémem při potřebě rychlého nasazení robotického systému, pokud je jeho funkcionalita na temporálních modelech závislá. Z důvodu společenské potřeby, způsobené světovou pandemií v roce 2020⁠—kvůli které project FreMEn contra COVID vznikl⁠—přenos temporálních modelů byl vyhodnocen pro zlepšení aplikace Nebojsa (angl. FreMEn Advisor) s cílem pomoci lidem zavést do jejich života principy sociálního odstupu. Tato aplikace doporučuje čas k návštěvě veřejných míst, kde jsou vysoké koncentrace lidí tak, aby se mohl uživatel vyhnout vytíženým časům. Společně s přenosem temporálních modelů i efekt systému Nebojsa na riziko, kterému se lidé vystavují při nutných pochůzkách, je testován včetně různých možných zdrojů predikcí zaplněnosti daných míst. Výsledky ukazují, že riziko je významně nižší pro uživatele, kteří se řídí doporučeními a že přenesené modely jsou slibným způsobem, jak službu zajistit i v místech, která nejsou pokrata daty pro přesné modelování.The goal of this thesis is to present the FreMEn contra COVID project from a technical perspective and experimentally evaluate its impact. To allow for deployment in new areas, because of low amounts of data the possibility of transferring chronorobotic temporal models in the application is tested. Temporal transfer presents a way to deal with extremely small amounts of data, common to robotics, that pose a significant problem to quick deployment of robotic systems dependent on those. Because of the social need caused by the world-wide pandemic of 2020⁠—for which the FreMEn contra COVID project was founded⁠—the temporal transfer has been evaluated in the context of boosting the performance of a system meant to aid individuals to implement social distancing measures FreMEn Advisor app. This app gives recommendations to the time of visits to public locations, where many people concentrate so that the user can avoid crowded times. With the temporal transfer, also the impact of the FreMEn Advisor is tested to the risk people experience while doing necessary tasks in public places, like shops, with different possible sources of predictions of human behaviour in given places. The results show that the risk is significantly lower for users following the recommendations and that transferred models present a promising way to provide recommendations for places not covered by data for exact modelling

Safe navigation and human-robot interaction in assistant robotic applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Survey on video anomaly detection in dynamic scenes with moving cameras

The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie

Dual-Mandate Patrols: Multi-Armed Bandits for Green Security

Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i.e., patrollers), who must patrol vast areas to protect from attackers (e.g., poachers or illegal loggers). Defenders must choose how much time to spend in each region of the protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. We formulate the problem as a stochastic multi-armed bandit, where each action represents a patrol strategy, enabling us to guarantee the rate of convergence of the patrolling policy. However, a naive bandit approach would compromise short-term performance for long-term optimality, resulting in animals poached and forests destroyed. To speed up performance, we leverage smoothness in the reward function and decomposability of actions. We show a synergy between Lipschitz-continuity and decomposition as each aids the convergence of the other. In doing so, we bridge the gap between combinatorial and Lipschitz bandits, presenting a no-regret approach that tightens existing guarantees while optimizing for short-term performance. We demonstrate that our algorithm, LIZARD, improves performance on real-world poaching data from Cambodia.Comment: Published at AAAI 2021. 9 pages (paper and references), 3 page appendix. 6 figures and 1 tabl

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

Liquid Cybernetic Systems: The Fourth‐Order Cybernetics

Technological development in robotics, computing architectures and devices, and information storage systems, in one single word: cybernetic systems, has progressed according to a jeopardized connection scheme, difficult if not impossible to track and picture in all its streams. Aim of this progress report is to critically introduce the most relevant limits and present a promising paradigm that might bring new momentum, offering features that naturally and elegantly overcome current challenges and introduce several other advantages: liquid cybernetic systems. The topic describing the four orders of cybernetic systems identified so far is introduced, evidencing the features of the fourth order that includes liquid systems. Then, current limitations to the development of conventional, von Neumann‐based cybernetic systems are briefly discussed: device integration, thermal design, data throughput, and energy consumption. In the following sections, liquid‐state machines are introduced, providing a computational paradigm (free from in materio considerations) that goes into the direction of solving such issues. Two original in materio implementation schemes are proposed: the COlloIdal demonsTratOR (COgITOR) autonomous robot, and a soft holonomic processor that is also proposed to realize an autolographic system