Legged Robots for Object Manipulation: A Review

Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included "moving an object" as a way of doing tangible work. Legged robots can be designed to manipulate a particular type of object (e.g., a cardboard box, a soccer ball, or a larger piece of furniture), by themselves or collaboratively. The objective of this review is to collect and learn from these examples, to both organize the work done so far in the community and highlight interesting open avenues for future work. This review categorizes existing works into four main manipulation methods: object interactions without grasping, manipulation with walking legs, dedicated non-locomotive arms, and legged teams. Each method has different design and autonomy features, which are illustrated by available examples in the literature. Based on a few simplifying assumptions, we further provide quantitative comparisons for the range of possible relative sizes of the manipulated object with respect to the robot. Taken together, these examples suggest new directions for research in legged robot manipulation, such as multifunctional limbs, terrain modeling, or learning-based control, to support a number of new deployments in challenging indoor/outdoor scenarios in warehouses/construction sites, preserved natural areas, and especially for home robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical Engineerin

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Investigation of energy efficiency of hexapod robot locomotion

Disertacijoje nagrinėjamos vaikščiojančių robotų energijos sąnaudų problemos jiems judant lygiu ir nelygiu paviršiumi. Pagrindinis tyrimo objektas yra vaikščiojančio roboto valdymo, aplinkos atpažinimo bei kliūčių išvengimo žinomoje aplinkoje metodas. Energijos sąnaudų minimizavimas leistų praplėsti vaikščiojančių robotų pritaikymą ir veikimo laiką. Pagrindinis darbo tikslas – sukurti energijos sąnaudų minimizavimo metodus vaikščiojantiems robotams ir sukurti aplinkos atpažinimo ir klasifikavimo metodus bei ištirti šešiakojo roboto energijos sąnaudas jiems judant žinomoje aplinkoje. Šie metodai gali būti taikomi vaikščiojantiems daugiakojams robotams. Darbe sprendžiami šie uždaviniai: šešiakojo roboto eisenos parinkimas atsižvelgiant į energijos sąnaudas, paviršiaus kliūčių aptikimo ir perlipimo metodų sudarymas ir jų efektyvumo palyginimas. Taip pat sprendžiami uždaviniai, kurie siejasi su pėdų trajektorijos generavimo metodo kūrimu bei kliūčių dydžio ir tankio įtaka roboto energijos sąnaudoms. Disertaciją sudaro įvadas, trys skyriai, bendrosios išvados, naudotos literatūros ir autoriaus publikacijų disertacijos tema sąrašai. Įvade aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos ir pranešimai konferencijose bei disertacijos struktūra. Pirmasis skyrius skirtas literatūros apžvalgai. Jame pateikta mobiliųjų robotų energetinio efektyvumo bei energijos sąnaudų matavimo, skaičiavimo ir optimizavimo metodų analizė. Antrajame skyriuje pateiktas energetiškai efektyvaus judėjimo metodikos sudarymas vaikščiojantiems robotams. Šiame skyriuje pateiktas šešiakojo roboto matematinio ir fizinio modelių sudarymas, nelygaus paviršiaus klasifikavimo modelio sudarymas bei taktilinio kliūčių aptikimo bei perlipimo metodų sudarymas. Skyriaus gale pateikiamos išvados. Trečiajame skyriuje tiriamos energijos sąnaudų priklausomybės nuo roboto eisenos bei judėjimo parametrų, kliūčių aptikimo ir perlipimo tikslumas priklausomai nuo kliūčių skaičiaus roboto kelyje, taip pat kliūčių dydžio ir tankio įtaka roboto energijos sąnaudoms. Disertacijos tema paskelbti 9 straipsniai: keturi – Clarivate Analytics Web of Science duomenų bazės leidiniuose, turinčiuose citavimo rodiklį, trys – Clarivate Analytics Web of Science duomenų bazės „Conference Proceedings“ leidiniuose ir du – kituose recenzuojamuose mokslo leidiniuose. Disertacijos tema perskaityti 7 pranešimai konferencijose Lietuvoje bei kitose šalyse

A survey on policy search algorithms for learning robot controllers in a handful of trials

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.Comment: 21 pages, 3 figures, 4 algorithms, accepted at IEEE Transactions on Robotic

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

A survey on policy search algorithms for learning robot controllers in a handful of trials

International audienceMost policy search (PS) algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word “big-data,” we refer to this challenge as “micro-data reinforcement learning.” In this article, we show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based PS), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots, designing generic priors, and optimizing the computing time