Design, Control and Motion Planning for a Novel Modular Extendable Robotic Manipulator

This dissertation discusses an implementation of a design, control and motion planning for a novel extendable modular redundant robotic manipulator in space constraints, which robots may encounter for completing required tasks in small and constrained environment. The design intent is to facilitate the movement of the proposed robotic manipulator in constrained environments, such as rubble piles. The proposed robotic manipulator with multi Degree of Freedom (m-DOF) links is capable of elongating by 25% of its nominal length. In this context, a design optimization problem with multiple objectives is also considered. In order to identify the benefits of the proposed design strategy, the reachable workspace of the proposed manipulator is compared with that of the Jet Propulsion Laboratory (JPL) serpentine robot. The simulation results show that the proposed manipulator has a relatively efficient reachable workspace, needed in constrained environments. The singularity and manipulability of the designed manipulator are investigated. In this study, we investigate the number of links that produces the optimal design architecture of the proposed robotic manipulator. The total number of links decided by a design optimization can be useful distinction in practice. Also, we have considered a novel robust bio-inspired Sliding Mode Control (SMC) to achieve favorable tracking performance for a class of robotic manipulators with uncertainties. To eliminate the chattering problem of the conventional sliding mode control, we apply the Brain Emotional Learning Based Intelligent Control (BELBIC) to adaptively adjust the control input law in sliding mode control. The on-line computed parameters achieve favorable system robustness in process of parameter uncertainties and external disturbances. The simulation results demonstrate that our control strategy is effective in tracking high speed trajectories with less chattering, as compared to the conventional sliding mode control. The learning process of BLS is shown to enhance the performance of a new robust controller. Lastly, we consider the potential field methodology to generate a desired trajectory in small and constrained environments. Also, Obstacle Collision Avoidance (OCA) is applied to obtain an inverse kinematic solution of a redundant robotic manipulator

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Design, Control and Motion Planning for a Novel Modular Extendable Robotic Manipulator

This dissertation discusses an implementation of a design, control and motion planning for a novel extendable modular redundant robotic manipulator in space constraints, which robots may encounter for completing required tasks in small and constrained environment. The design intent is to facilitate the movement of the proposed robotic manipulator in constrained environments, such as rubble piles. The proposed robotic manipulator with multi Degree of Freedom (m-DOF) links is capable of elongating by 25% of its nominal length. In this context, a design optimization problem with multiple objectives is also considered. In order to identify the benefits of the proposed design strategy, the reachable workspace of the proposed manipulator is compared with that of the Jet Propulsion Laboratory (JPL) serpentine robot. The simulation results show that the proposed manipulator has a relatively efficient reachable workspace, needed in constrained environments. The singularity and manipulability of the designed manipulator are investigated. In this study, we investigate the number of links that produces the optimal design architecture of the proposed robotic manipulator. The total number of links decided by a design optimization can be useful distinction in practice. Also, we have considered a novel robust bio-inspired Sliding Mode Control (SMC) to achieve favorable tracking performance for a class of robotic manipulators with uncertainties. To eliminate the chattering problem of the conventional sliding mode control, we apply the Brain Emotional Learning Based Intelligent Control (BELBIC) to adaptively adjust the control input law in sliding mode control. The on-line computed parameters achieve favorable system robustness in process of parameter uncertainties and external disturbances. The simulation results demonstrate that our control strategy is effective in tracking high speed trajectories with less chattering, as compared to the conventional sliding mode control. The learning process of BLS is shown to enhance the performance of a new robust controller. Lastly, we consider the potential field methodology to generate a desired trajectory in small and constrained environments. Also, Obstacle Collision Avoidance (OCA) is applied to obtain an inverse kinematic solution of a redundant robotic manipulator

Positional voting rules generated by aggregation functions and the role of duplication

Producción CientíficaIn this paper, we consider a typical voting situation where a group of agents show their preferences over a set of alternatives. Under our approach, such preferences are codied into individual positional values which can be aggregated in several ways through particular functions, yielding positional voting rules and providing a social result in each case. We show that scoring rules belong to such class of positional voting rules. But if we focus our interest on OWA operators as aggregation functions, other well-known voting systems naturally appear. In particular, we determine those ones verifying duplication (i.e., clone irrelevance) and present a proposal of an overall social result provided by them.Ministerio de Economía, Industria y Competitividad (ECO2012-32178)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA066U13

Kinematics and Robot Design II (KaRD2019) and III (KaRD2020)

This volume collects papers published in two Special Issues “Kinematics and Robot Design II, KaRD2019” (https://www.mdpi.com/journal/robotics/special_issues/KRD2019) and “Kinematics and Robot Design III, KaRD2020” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2020), which are the second and third issues of the KaRD Special Issue series hosted by the open access journal robotics.The KaRD series is an open environment where researchers present their works and discuss all topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. It aims at being an established reference for researchers in the field as other serial international conferences/publications are. Even though the KaRD series publishes one Special Issue per year, all the received papers are peer-reviewed as soon as they are submitted and, if accepted, they are immediately published in MDPI Robotics. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”.KaRD2019 together with KaRD2020 received 22 papers and, after the peer-review process, accepted only 17 papers. The accepted papers cover problems related to theoretical/computational kinematics, to biomedical engineering and to other design/applicative aspects

Machine Learning Meets Advanced Robotic Manipulation

Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works

Bimanual robot skills: MP encoding, dimensionality reduction and reinforcement learning

Aplicat embargament des de la data de defensa fins 1/7/2018Premio a la mejor Tesis Doctoral sobre Robótica, Edición 2017, atorgat pel Comité Español de Automática.Finalista del 2018 George Girault PhD Award, from EuRoboticsIn our culture, robots have been in novels and cinema for a long time, but it has been specially in the last two decades when the improvements in hardware - better computational power and components - and advances in Artificial Intelligence (AI), have allowed robots to start sharing spaces with humans. Such situations require, aside from ethical considerations, robots to be able to move with both compliance and precision, and learn at different levels, such as perception, planning, and motion, being the latter the focus of this work. The first issue addressed in this thesis is inverse kinematics for redundant robot manipulators, i.e: positioning the robot joints so as to reach a certain end-effector pose. We opt for iterative solutions based on the inversion of the kinematic Jacobian of a robot, and propose to filter and limit the gains in the spectral domain, while also unifying such approach with a continuous, multipriority scheme. Such inverse kinematics method is then used to derive manipulability in the whole workspace of an antropomorphic arm, and the coordination of two arms is subsequently optimized by finding their best relative positioning. Having solved the kinematic issues, a robot learning within a human environment needs to move compliantly, with limited amount of force, in order not to harm any humans or cause any damage, while being as precise as possible. Therefore, we developed two dynamic models for the same redundant arm we had analysed kinematically: The first based on local models with Gaussian projections, and the second characterizing the most problematic term of the dynamics, namely friction. Such models allowed us to implement feed-forward controllers, where we can actively change the weights in the compliance-precision tradeoff. Moreover, we used such models to predict external forces acting on the robot, without the use of force sensors. Afterwards, we noticed that bimanual robots must coordinate their components (or limbs) and be able to adapt to new situations with ease. Over the last decade, a number of successful applications for learning robot motion tasks have been published. However, due to the complexity of a complete system including all the required elements, most of these applications involve only simple robots with a large number of high-end technology sensors, or consist of very simple and controlled tasks. Using our previous framework for kinematics and control, we relied on two types of movement primitives to encapsulate robot motion. Such movement primitives are very suitable for using reinforcement learning. In particular, we used direct policy search, which uses the motion parametrization as the policy itself. In order to improve the learning speed in real robot applications, we generalized a policy search algorithm to give some importance to samples yielding a bad result, and we paid special attention to the dimensionality of the motion parametrization. We reduced such dimensionality with linear methods, using the rewards obtained through motion repetition and execution. We tested such framework in a bimanual task performed by two antropomorphic arms, such as the folding of garments, showing how a reduced dimensionality can provide qualitative information about robot couplings and help to speed up the learning of tasks when robot motion executions are costly.A la nostra cultura, els robots han estat presents en novel·les i cinema des de fa dècades, però ha sigut especialment en les últimes dues quan les millores en hardware (millors capacitats de còmput) i els avenços en intel·ligència artificial han permès que els robots comencin a compartir espais amb els humans. Aquestes situacions requereixen, a banda de consideracions ètiques, que els robots siguin capaços de moure's tant amb suavitat com amb precisió, i d'aprendre a diferents nivells, com són la percepció, planificació i moviment, essent l'última el centre d'atenció d'aquest treball. El primer problema adreçat en aquesta tesi és la cinemàtica inversa, i.e.: posicionar les articulacions del robot de manera que l'efector final estigui en una certa posició i orientació. Hem estudiat el camp de les solucions iteratives, basades en la inversió del Jacobià cinemàtic d'un robot, i proposem un filtre que limita els guanys en el seu domini espectral, mentre també unifiquem tal mètode dins un esquema multi-prioritat i continu. Aquest mètode per a la cinemàtica inversa és usat a l'hora d'encapsular tota la informació sobre l'espai de treball d'un braç antropomòrfic, i les capacitats de coordinació entre dos braços són optimitzades, tot trobant la seva millor posició relativa en l'espai. Havent resolt les dificultats cinemàtiques, un robot que aprèn en un entorn humà necessita moure's amb suavitat exercint unes forces limitades per tal de no causar danys, mentre es mou amb la màxima precisió possible. Per tant, hem desenvolupat dos models dinàmics per al mateix braç robòtic redundant que havíem analitzat des del punt de vista cinemàtic: El primer basat en models locals amb projeccions de Gaussianes i el segon, caracteritzant el terme més problemàtic i difícil de representar de la dinàmica, la fricció. Aquests models ens van permetre utilitzar controladors coneguts com "feed-forward", on podem canviar activament els guanys buscant l'equilibri precisió-suavitat que més convingui. A més, hem usat aquests models per a inferir les forces externes actuant en el robot, sense la necessitat de sensors de força. Més endavant, ens hem adonat que els robots bimanuals han de coordinar els seus components (braços) i ser capaços d'adaptar-se a noves situacions amb facilitat. Al llarg de l'última dècada, diverses aplicacions per aprendre tasques motores robòtiques amb èxit han estat publicades. No obstant, degut a la complexitat d'un sistema complet que inclogui tots els elements necessaris, la majoria d'aquestes aplicacions consisteixen en robots més aviat simples amb costosos sensors d'última generació, o a resoldre tasques senzilles en un entorn molt controlat. Utilitzant el nostre treball en cinemàtica i control, ens hem basat en dos tipus de primitives de moviment per caracteritzar la motricitat robòtica. Aquestes primitives de moviment són molt adequades per usar aprenentatge per reforç. En particular, hem usat la búsqueda directa de la política, un camp de l'aprenentatge per reforç que usa la parametrització del moviment com la pròpia política. Per tal de millorar la velocitat d'aprenentatge en aplicacions amb robots reals, hem generalitzat un algoritme de búsqueda directa de política per a donar importància a les mostres amb mal resultat, i hem donat especial atenció a la reducció de dimensionalitat en la parametrització dels moviments. Hem reduït la dimensionalitat amb mètodes lineals, utilitzant les recompenses obtingudes EN executar els moviments. Aquests mètodes han estat provats en tasques bimanuals com són plegar roba, usant dos braços antropomòrfics. Els resultats mostren com la reducció de dimensionalitat pot aportar informació qualitativa d'una tasca, i al mateix temps ajuda a aprendre-la més ràpid quan les execucions amb robots reals són costoses.Award-winningPostprint (published version

Bimanual robot skills: MP encoding, dimensionality reduction and reinforcement learning

In our culture, robots have been in novels and cinema for a long time, but it has been specially in the last two decades when the improvements in hardware - better computational power and components - and advances in Artificial Intelligence (AI), have allowed robots to start sharing spaces with humans. Such situations require, aside from ethical considerations, robots to be able to move with both compliance and precision, and learn at different levels, such as perception, planning, and motion, being the latter the focus of this work. The first issue addressed in this thesis is inverse kinematics for redundant robot manipulators, i.e: positioning the robot joints so as to reach a certain end-effector pose. We opt for iterative solutions based on the inversion of the kinematic Jacobian of a robot, and propose to filter and limit the gains in the spectral domain, while also unifying such approach with a continuous, multipriority scheme. Such inverse kinematics method is then used to derive manipulability in the whole workspace of an antropomorphic arm, and the coordination of two arms is subsequently optimized by finding their best relative positioning. Having solved the kinematic issues, a robot learning within a human environment needs to move compliantly, with limited amount of force, in order not to harm any humans or cause any damage, while being as precise as possible. Therefore, we developed two dynamic models for the same redundant arm we had analysed kinematically: The first based on local models with Gaussian projections, and the second characterizing the most problematic term of the dynamics, namely friction. Such models allowed us to implement feed-forward controllers, where we can actively change the weights in the compliance-precision tradeoff. Moreover, we used such models to predict external forces acting on the robot, without the use of force sensors. Afterwards, we noticed that bimanual robots must coordinate their components (or limbs) and be able to adapt to new situations with ease. Over the last decade, a number of successful applications for learning robot motion tasks have been published. However, due to the complexity of a complete system including all the required elements, most of these applications involve only simple robots with a large number of high-end technology sensors, or consist of very simple and controlled tasks. Using our previous framework for kinematics and control, we relied on two types of movement primitives to encapsulate robot motion. Such movement primitives are very suitable for using reinforcement learning. In particular, we used direct policy search, which uses the motion parametrization as the policy itself. In order to improve the learning speed in real robot applications, we generalized a policy search algorithm to give some importance to samples yielding a bad result, and we paid special attention to the dimensionality of the motion parametrization. We reduced such dimensionality with linear methods, using the rewards obtained through motion repetition and execution. We tested such framework in a bimanual task performed by two antropomorphic arms, such as the folding of garments, showing how a reduced dimensionality can provide qualitative information about robot couplings and help to speed up the learning of tasks when robot motion executions are costly.A la nostra cultura, els robots han estat presents en novel·les i cinema des de fa dècades, però ha sigut especialment en les últimes dues quan les millores en hardware (millors capacitats de còmput) i els avenços en intel·ligència artificial han permès que els robots comencin a compartir espais amb els humans. Aquestes situacions requereixen, a banda de consideracions ètiques, que els robots siguin capaços de moure's tant amb suavitat com amb precisió, i d'aprendre a diferents nivells, com són la percepció, planificació i moviment, essent l'última el centre d'atenció d'aquest treball. El primer problema adreçat en aquesta tesi és la cinemàtica inversa, i.e.: posicionar les articulacions del robot de manera que l'efector final estigui en una certa posició i orientació. Hem estudiat el camp de les solucions iteratives, basades en la inversió del Jacobià cinemàtic d'un robot, i proposem un filtre que limita els guanys en el seu domini espectral, mentre també unifiquem tal mètode dins un esquema multi-prioritat i continu. Aquest mètode per a la cinemàtica inversa és usat a l'hora d'encapsular tota la informació sobre l'espai de treball d'un braç antropomòrfic, i les capacitats de coordinació entre dos braços són optimitzades, tot trobant la seva millor posició relativa en l'espai. Havent resolt les dificultats cinemàtiques, un robot que aprèn en un entorn humà necessita moure's amb suavitat exercint unes forces limitades per tal de no causar danys, mentre es mou amb la màxima precisió possible. Per tant, hem desenvolupat dos models dinàmics per al mateix braç robòtic redundant que havíem analitzat des del punt de vista cinemàtic: El primer basat en models locals amb projeccions de Gaussianes i el segon, caracteritzant el terme més problemàtic i difícil de representar de la dinàmica, la fricció. Aquests models ens van permetre utilitzar controladors coneguts com "feed-forward", on podem canviar activament els guanys buscant l'equilibri precisió-suavitat que més convingui. A més, hem usat aquests models per a inferir les forces externes actuant en el robot, sense la necessitat de sensors de força. Més endavant, ens hem adonat que els robots bimanuals han de coordinar els seus components (braços) i ser capaços d'adaptar-se a noves situacions amb facilitat. Al llarg de l'última dècada, diverses aplicacions per aprendre tasques motores robòtiques amb èxit han estat publicades. No obstant, degut a la complexitat d'un sistema complet que inclogui tots els elements necessaris, la majoria d'aquestes aplicacions consisteixen en robots més aviat simples amb costosos sensors d'última generació, o a resoldre tasques senzilles en un entorn molt controlat. Utilitzant el nostre treball en cinemàtica i control, ens hem basat en dos tipus de primitives de moviment per caracteritzar la motricitat robòtica. Aquestes primitives de moviment són molt adequades per usar aprenentatge per reforç. En particular, hem usat la búsqueda directa de la política, un camp de l'aprenentatge per reforç que usa la parametrització del moviment com la pròpia política. Per tal de millorar la velocitat d'aprenentatge en aplicacions amb robots reals, hem generalitzat un algoritme de búsqueda directa de política per a donar importància a les mostres amb mal resultat, i hem donat especial atenció a la reducció de dimensionalitat en la parametrització dels moviments. Hem reduït la dimensionalitat amb mètodes lineals, utilitzant les recompenses obtingudes EN executar els moviments. Aquests mètodes han estat provats en tasques bimanuals com són plegar roba, usant dos braços antropomòrfics. Els resultats mostren com la reducció de dimensionalitat pot aportar informació qualitativa d'una tasca, i al mateix temps ajuda a aprendre-la més ràpid quan les execucions amb robots reals són costoses

Causal inquiry in the social sciences: the promise of process tracing

In this thesis I investigate causal inquiry in the social sciences, drawing on examples from various disciplines and in particular from conflict studies. In a backlash against the pervasiveness of statistical methods, in the last decade certain social scientists have focused on finding the causal mechanisms behind observed correlations. To provide evidence for such mechanisms, researchers increasingly rely on ‘process tracing’, a method which attempts to give evidence for causal relations by specifying the chain of events connecting a putative cause and effect of interest. I will ask whether the causal claims process tracers make are defensible, and where they are not defensible I will ask how we can improve the method. Throughout these investigations, I show that the conclusions of process tracing (and indeed ofthe social sciences more generally) are constrained both by the causal structure ofthe social world and by social scientists’ aims and values. My central argument is this: all instances of social phenomena have causally relevant differences, which implies that any research design that requires some comparison between cases (like process tracing) is limited by how we systematize these phenomena. Moreover, such research cannot rely on stable regularities. Nevertheless, to forego causal conclusions altogether is not the right response to these limitations; by carefully outlining our epistemic assumptions we can make progress in causal inquiry. While I use philosophical theories of causation to comment on the feasibility of a social scientific method, I also do the reverse: by investigating a popular contemporary method in the social sciences, I show to what extent our philosophical theories of causation are workable in practice. Thus, this thesis is both a methodological and a philosophical work. Every chapter discusses both a fundamental philosophical position on the social sciences and a relevant case study from the social sciences

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collision-avoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collision-avoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free