Towards Developing Efficient Area Coverage Approach in Domestic Robots

東京電機大学201

Practical investigations in robot localization using ultra-wideband sensors

Robot navigation is rudimentary compared to the capabilities of humans and animals to move about their environments. One of the core processes of navigation is localization, the problem of answering where one is at the present time. Robot localization is the science of using various sensors to inform a robot of where it is within its environment. Ultra-wideband (UWB) radio is one such sensor technology that can return absolute position information. The algorithm to accomplish this is known as multilateration, which uses a collection of distance measurements between multiple robot tag and environment anchor pairs to calculate the tag’s position. UWB is especially suited to the task of returning precise distance measurements due to its capabilities of short duration, high amplitude pulse generation and detection. Decawave Ltd. has created an UWB integrated circuit to perform ranging and a suite of products to support this technology. Claimed and verified accuracies using this implementation are on the order of 10cm. This thesis describes various experiments carried out using Decawave technology for robot localization. The progression of the chapters starts with commercial product verification before moving into development and testing in various environments of an open-source driver package for the Robot Operating System (ROS), then the development of a novel phase difference of arrival (PDoA) sensor for three-dimensional robot localization without an UWB anchor mesh, before concluding with future research directions and commercialization potential of UWB. This thesis is designed as a compilation of all that the author has learned through primary and secondary research over the past three years of investigation. The primary contributions are: 1. A modular ROS UWB driver framework and series of ROS bags for offline experimentation with multilateration algorithms. 2. A robust ROS framework for comparing motion capture system (MoCap) ground truth vs sensor data for rigorous statistical analysis and characterization of multiple sensors. 3. Development of a novel UWB PDoA sensor array and data model to allow 3D localization of a target from a single point without the deployment of an antenna mesh

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Context-Independent Task Knowledge for Neurosymbolic Reasoning in Cognitive Robotics

One of the current main goals of artificial intelligence and robotics research is the creation of an artificial assistant which can have flexible, human like behavior, in order to accomplish everyday tasks. A lot of what is context-independent task knowledge to the human is what enables this flexibility at multiple levels of cognition. In this scope the author analyzes how to acquire, represent and disambiguate symbolic knowledge representing context-independent task knowledge, abstracted from multiple instances: this thesis elaborates the incurred problems, implementation constraints, current state-of-the-art practices and ultimately the solutions newly introduced in this scope. The author specifically discusses acquisition of context-independent task knowledge from large amounts of human-written texts and their reusability in the robotics domain; the acquisition of knowledge on human musculoskeletal dependencies constraining motion which allows a better higher level representation of observed trajectories; the means of verbalization of partial contextual and instruction knowledge, increasing interaction possibilities with the human as well as contextual adaptation. All the aforementioned points are supported by evaluation in heterogeneous setups, to bring a view on how to make optimal use of statistical & symbolic applications (i.e. neurosymbolic reasoning) in cognitive robotics. This work has been performed to enable context-adaptable artificial assistants, by bringing together knowledge on what is usually regarded as context-independent task knowledge

Foresighted People Finding and Following

Mobile service robots are needed in several applications (e.g., transportation systems, autonomous shopping carts, household activities ... etc). In such scenarios the robot aids the user with tasks that require the robot to move freely across the environment in addition to direct interaction at certain times. Therefore, such a robot needs a strategy to quickly find the user whenever needed, in addition to a strategy that enables the robot to reason about the user's intended destination to be able to follow him in a foresighted manner if the user needs its help at that destination. In this dissertation, we tackle each of those problems separately in a divide and conquer manner. We present an approach to learn optimal navigation actions for assistance tasks in which the robot aims at efficiently reaching the final navigation goal of a human where service has to be provided. Always following the human at a close distance might hereby result in inefficient trajectories, since people regularly do not move on the shortest path to their destination (e.g., they may move to grab the phone or make a note). Therefore, a service robot should infer the human's intended navigation goal and compute its own motion based on that prediction. We propose to perform a prediction about the human's future movements and use this information in a reinforcement learning framework to generate foresighted navigation actions for the robot. Since frequent occlusions of the human will occur due to obstacles and the robot's constrained field of view, the estimate about the humans's position and the prediction of the next destination are affected by uncertainty. Our approach deals with such situations by explicitly considering occlusions in the reward function such that the robot automatically considers to execute actions to get the human in its field of view. We show in simulated and real-world experiments that our technique leads to significantly shorter paths compared to an approach in which the robot always tries to closely follow the user and, additionally, can handle occlusions. On the other side, an autonomous robot that directly helps users with certain tasks often first has to quickly find a user, especially when this person moves around frequently. A search method that relies on a greedy approach that do not perform any predictions about the user's most likely location, even when it is provided with background information about the frequently visited destinations of the user, might not be the best option. In this dissertation, we propose to compute the likelihood of the user's observability at each possible location in the environment based on simulations that rely on hidden Markov model based predictions. As the robot needs time to reach the search locations, we take this time into account as well as the visibility constraints. In this way we aim at selecting effective search locations for the robot to find the user as fast as possible. As our experiments in various simulated environments show, our approach leads to significantly shorter search times compared to the greedy approach

Cloud point labelling in optical motion capture systems

109 p.This Thesis deals with the task of point labeling involved in the overall workflow of Optical Motion Capture Systems. Human motion capture by optical sensors produces at each frame snapshots of the motion as a cloud of points that need to be labeled in order to carry out ensuing motion analysis. The problem of labeling is tackled as a classification problem, using machine learning techniques as AdaBoost or Genetic Search to train a set of weak classifiers, gathered in turn in an ensemble of partial solvers. The result is used to feed an online algorithm able to provide a marker labeling at a target detection accuracy at a reduced computational cost. On the other hand, in contrast to other approaches the use of misleading temporal correlations has been discarded, strengthening the process against failure due to occasional labeling errors. The effectiveness of the approach is demonstrated on a real dataset obtained from the measurement of gait motion of persons, for which the ground truth labeling has been verified manually. In addition to the above, a broad sight regarding the field of Motion Capture and its optical branch is provided to the reader: description, composition, state of the art and related work. Shall it serve as suitable framework to highlight the importance and ease the understanding of the point labeling

Seguimento ativo de agentes dinâmicos multivariados usando informação vectorial

Doutoramento em Engenharia MecânicaO objeto principal da presente tese é o estudo de sistemas avançados de segurança, no âmbito da segurança automóvel, baseando-se na previsão de movimentos e ações dos agentes externos. Esta tese propõe tratar os agentes como entidades dinâmicas, com motivações e constrangimentos próprios. Apresenta-se, para tal, novas técnicas de seguimento dos referidos agentes levando em linha de conta as suas especificidades. Em decorrência, estuda-se dedicadamente dois tipos de agentes: os veículos automóveis e os peões. Quanto aos veículos automóveis, propõe-se melhorar a capacidade de previsão de movimentos recorrendo a modelos avançados que representam corretamente os constrangimentos presentes nos veículos. Assim, foram desenvolvidos algoritmos avançados de seguimento de agentes com recurso a modelos de movimento não holonómicos. Estes algoritmos fazem uso de dados vectoriais de distância fornecidos por sensores de distância laser. Para os peões, devido à sua complexidade (designadamente a ausência de constrangimentos de movimentos) propõe-se que a análise da sua linguagem corporal permita detetar atempadamente possíveis intenções de movimentos. Assim, foram desenvolvidos algoritmos de perceção de pose de peões adaptados ao campo da segurança automóvel com recurso a uso de dados de distâncias 3D obtidos com uma câmara stereo. De notar que os diversos algoritmos foram testados em experiências realizadas em ambiente real.The main topic of this thesis is the study of advanced safety systems, in the field of automotive safety, based on the prediction of the movement and actions of external agents. This thesis proposes to treat the agents as dynamic entities with their own motivations as constraints. As so, new target tracking techniques are proposed taking into account the targets’ specificities. Therefore, two different types of agents are dedicatedly studied: automobile vehicles and pedestrians. For the automobile vehicles, a technique to improve motion prediction by the use of advanced motion models is proposed, these models will correctly represent the constrains that exist in this kind of vehicle. With this goal, advanced target tracking algorithms coupled with nonholonomic motion models were developed. These algorithms make use of vectorial range data supplied by laser range sensors. Concerning the pedestrians, due to the problem complexity (mainly due to the lack of any specific motion constraint), it is proposed that the analysis of the pedestrians body language will allow to detected early the pedestrian intentions and movements. As so, pedestrian pose estimation algorithms specially adapted to the field of automotive safety were developed; these algorithms use 3D point cloud data obtained with a stereo camera. The various algorithms were tested in experiments conducted in real conditions

Bioinspired approaches for coordination and behaviour adaptation of aerial robot swarms

Behavioural adaptation is a pervasive component in a myriad of animal societies. A well-known strategy, known as Levy Walk, has been commonly linked to such adaptation in foraging animals, where the motion of individuals couples periods of localized search and long straight forward motions. Despite the vast number of studies on Levy Walks in computational ecology, it was only in the past decade that the first studies applied this concept to robotics tasks. Therefore, this Thesis draws inspiration from the Levy Walk behaviour, and its recent applications to robotics, to design biologically inspired models for two swarm robotics tasks, aiming at increasing the performance with respect to the state of the art. The first task is cooperative surveillance, where the aim is to deploy a swarm so that at any point in time regions of the domain are observed by multiple robots simultaneously. One of the contributions of this Thesis, is the Levy Swarm Algorithm that augments the concept of Levy Walk to include the Reynolds’ flocking rules and achieve both exploration and coordination in a swarm of unmanned aerial vehicles. The second task is adaptive foraging in environments of clustered rewards. In such environments behavioural adaptation is of paramount importance to modulate the transition between exploitation and exploration. Nature enables these adaptive changes by coupling the behaviour to the fluctuation of hormones that are mostly regulated by the endocrine system. This Thesis draws further inspiration from Nature and proposes a second model, the Endocrine Levy Walk, that employs an Artificial Endocrine System as a modulating mechanism of Levy Walk behaviour. The Endocrine Levy Walk is compared with the Yuragi model (Nurzaman et al., 2010), in both simulated and physical experiments where it shows its increased performance in terms of search efficiency, energy efficiency and number of rewards found. The Endocrine Levy Walk is then augmented to consider social interactions between members of the swarm by mimicking the behaviour of fireflies, where individuals attract others when finding suitable environmental conditions. This extended model, the Endocrine Levy Firefly, is compared to the Levy+ model (Sutantyo et al., 2013) and the Adaptive Collective Levy Walk Nauta et al. (2020). This comparison is also made both in simulated and physical experiments and assessed in terms of search efficiency, number of rewards found and cluster search efficiency, strengthening the argument in favour of the Endocrine Levy Firefly as a promising approach to tackle collaborative foragin