Ubiquitous robotics: Recent challenges and future trends

Ambient intelligence, ubiquitous and networked robots, and cloud robotics are new research hot topics that have started to gain popularity among the robotics community. They enable robots to acquire richer functionalities and open the way for the composition of a variety of robotic services with three functions: semantic perception, reasoning and actuation. Ubiquitous robots (ubirobots) overcome the limitations of stand-alone robots by integrating them with web services and ambient intelligence technologies. The overlap that exists now between ubirobots and ambient intelligence makes their integration worthwhile. It targets to create a hybrid physical–digital space rich with a myriad of proactive intelligent services that enhance the quality and the way of our living and working. Furthermore, the emergence of cloud computing initiates the massive use of a new generation of ubirobots that enrich their cognitive capabilities and share their knowledge by connecting themselves to cloud infrastructures. The future of ubirobots will certainly be open to an unlimited space of applications such as physical and virtual companions assisting people in their daily living, ubirobots that are able to co-work alongside people and cooperate with them in the same environment, and physical and virtual autonomic guards that are able to protect people, monitor their security and safety, and rescue them in indoor and outdoor spaces. This paper introduces the recent challenges and future trends on these topics.     

The current state and future outlook of rescue robotics

Robotic technologies, whether they are remotely operated vehicles, autonomous agents, assistive devices, or novel control interfaces, offer many promising capabilities for deployment in real‐world environments. Postdisaster scenarios are a particularly relevant target for applying such technologies, due to the challenging conditions faced by rescue workers and the possibility to increase their efficacy while decreasing the risks they face. However, field‐deployable technologies for rescue work have requirements for robustness, speed, versatility, and ease of use that may not be matched by the state of the art in robotics research. This paper aims to survey the current state of the art in ground and aerial robots, marine and amphibious systems, and human–robot control interfaces and assess the readiness of these technologies with respect to the needs of first responders and disaster recovery efforts. We have gathered expert opinions from emergency response stakeholders and researchers who conduct field deployments with them to understand these needs, and we present this assessment as a way to guide future research toward technologies that will make an impact in real‐world disaster response and recovery.     

Aspects of non-cartesian robotics

Non-Cartesian robotics, which began with the introduction of subsumption architecture by Rodney Brooks, now encompasses a wide range of robotics that do not follow traditional cartesian principles in the running of a robot. The new field is sometimes called biorobotics as it draws its guiding principles from biology, physiology, behavioural sciences, genetics and theories of evolution, brain sciences, genetics and theories of evolution, brain sciences, ethology, psychology, and other related non-engineering disciplines. The difference in principles of operation, however, has roots deeper in the philosophical underpinnings of the way we view controlling artifacts and the concept of control itself when it is contrasted against the concept of autonomy. Realization of increasingly higher levels of autonomy is routinely demanded today not only in industry where most robotic applications occur, but also in areas closer to our daily life where a gradual but steady increase in service applications of robotics is observed. This paper introduces the concept of non-Cartesian robotics as an antithesis to conventional (Cartesian) robotics and describes various aspects of this new way of running a robotic system. This work was presented, in part, at the International symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Evolutionary robotics: the Sussex approach

We give an overview of evolutionary robotics research at Sussex over the last five years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. Results are presented from research with evolved controllers for autonomous mobile robots, simulated robots, co-evolved animats, real robots with software controllers, and a real robot with a controller directly evolved in hardware.     

Further explorations in evolutionary humanoid robotics

The field of evolutionary humanoid robotics is a branch of evolutionary robotics specifically dealing with the application of evolutionary principles to humanoid robot design. Previous studies demonstrated the possible future potential of this approach by evolving walking behaviors for simulated humanoid robots with up to 20 degrees of freedom. In this paper we examine further the evolutionary process by looking at the changes in diversity over time. We then investigate the effect of the immobilization of an individual joint or joints in the robot. The latter study may be of potential future use in prosthetic design. We also explore the possibility of the evolution of humanoid robots which can cope with different environmental conditions. These include reduced ground friction (ice) and modified gravitation (moon walking). We present initial results on the implementation of our simulated humanoid robots in hardware using the Bioloid robotic platform, using a model of this robot in order to evolve the desired motion patterns, for subsequent transfer to the real robot. We finish the article with a summary and brief discussion of future work. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Soft medical robotics: clinical and biomedical applications,challenges, and future directions

Bioinspired soft robotics allow for safer clinical interactions with human patients but conventional, hard robots, which are often built with rigid materials and complex control systems, compromise tissue integrity, freedom of movement, conformability, and overall human bio-compatibility. Soft, compliant materials intrinsically reduce mechanical complexity, accommodate their usage environment, and provide great practical potential for medical device developments. Previous review papers have generally covered the topics of materials, manufacturing processes, actuator modeling and control, and current trends. Here, we focus on recent developments in soft robotic applications for the medical field including advances in cardiac devices, surgical robots, and soft rehabilitation and assistance devices. In medical applications, soft robotic devices not only expedite the evolution of minimally invasive surgery but also improve the bio-compatibility of rehabilitation and assistance devices. Here, we evaluate design requirements, mechanisms, achievements and challenges in these key areas. Of particular note, this paper concludes with a discussion on advances in 3D printing and adapting neural networks for modeling and control frameworks that have facilitated the development of faster and less expensive soft medical devices.     

Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach

In this paper, a novel heuristic algorithm is proposed to solve continuous non-linear optimization problems. The presented algorithm is a collective global search inspired by the swarm artificial intelligent of coordinated robots. Cooperative recognition and sensing by a swarm of mobile robots have been fundamental inspirations for development of Swarm Robotics Search & Rescue (SRSR). Swarm robotics is an approach with the aim of coordinating multi-robot systems which consist of numbers of mostly uniform simple physical robots. The ultimate aim is to emerge an eligible cooperative behavior either from interactions of autonomous robots with the environment or their mutual interactions between each other. In this algorithm, robots which represent initial solutions in SRSR terminology have a sense of environment to detect victim in a search & rescue mission at a disaster site. In fact, victim’s location refers to global best solution in SRSR algorithm. The individual with the highest rank in the swarm is called master and remaining robots will play role of slaves. However, this leadership and master position can be transitioned from one robot to another one during mission. Having the supervision of master robot accompanied with abilities of slave robots for sensing the environment, this collaborative search assists the swarm to rapidly find the location of victim and subsequently a successful mission. In order to validate effectiveness and optimality of proposed algorithm, it has been applied on several standard benchmark functions and a practical electric power system problem in several real size cases. Finally, simulation results have been compared with those of some well-known algorithms. Comparison of results demonstrates superiority of presented algorithm in terms of quality solutions and convergence speed.     

An information-theoretic landscape analysis of neuro-controlled embodied organisms

Recently, there has been a lot of interest in evolving controllers for both physically simulated creatures as well as for real physical robots. However, a range of different ANN architectures are used for controller evolution, and, in the majority of the work conducted, the choice of the architecture used is made arbitrarily. No fitness landscape analysis was provided for the underlying fitness landscape of the controllers search space. As such, the literature remains largely inconclusive as to which ANN architecture provides the most efficient and effective space for searching the range of possible controllers through evolutionary methods. This represents the motivation for this paper where we compare the search space for four different types of ANN architecture for controller evolution through an information-theoretic analysis of the fitness landscape associated with each type of architecture.     

Evolutionary learning of a fuzzy controller for wall-following behavior in mobile robotics

The design of fuzzy controllers for the implementation of behaviors in mobile robotics is a complex and highly time-consuming task. The use of machine learning techniques such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process. In this paper, the automated design of a fuzzy controller using genetic algorithms for the implementation of the wall-following behavior in a mobile robot is described. The algorithm is based on the iterative rule learning approach, and is characterized by three main points. First, learning has no restrictions neither in the number of membership functions, nor in their values. In the second place, the training set is composed of a set of examples uniformly distributed along the universe of discourse of the variables. This warrantees that the quality of the learned behavior does not depend on the environment, and also that the robot will be capable to face different situations. Finally, the trade off between the number of rules and the quality/accuracy of the controller can be adjusted selecting the value of a parameter. Once the knowledge base has been learned, a process for its reduction and tuning is applied, increasing the cooperation between rules and reducing its number.     

Pulse-signaling algorithm: a non-device-based robotics control and communication

A pulse-signaling algorithm (PSA) is proposed for robotics control and communication. Using the PSA, an operator can send control signals directly to control a robot or a group of robots. No extra equipment is needed in PSA because the pulse signal can be generated easily from the operator. Three aspects of the algorithm are considered in this paper: communication protocol, hardware prototype and software implementation. The PSA protocol is composed of three parts, i.e. command code, operand code and address code, in order to control the desired robot to do the desired work. A PSA prototype circuit is designed and developed, and PSA software is programmed on the designed prototype circuit to realize the algorithm. Some experiments are performed to test and evaluate the proposed algorithm.     

User-defined challenges and desiderata for robotics and autonomous systems in health and social care settings

ABSTRACT

We report the needs and challenges identified by health and social care professionals and service users for robotics and autonomous systems that are of importance to researchers and policymakers. To this end, we held eight workshops in different locations across Cornwall (UK) in which we raised awareness of the applications and opportunities of assistive robots. The 223 participants could interact physically with four robots, watched a multimedia presentation including video and use-case scenarios and then took part in 33 focus groups. Content analysis was carried out based on summaries written by facilitators during the focus groups. The focus groups produced 163 challenges that may have digital solutions including 78 suitable for robotic assistive technology, in three main areas: maintaining independence at home, social isolation, and rurality. Although further research is needed with technology and its implementation, this study shows that health and social care professionals, patients, carers, and students are willing to consider using robotics and autonomous systems in health and social care settings.     

Surgical robotics: Reviewing the past,analysing the present,imagining the future

This paper presents an overview of the surgical robotics field, highlighting significant milestones and grouping the various propositions into cohorts. The review does not aim to be exhaustive but rather to highlight how surgical robotics is acting as an enabling technology for minimally invasive surgery. As such, there is a focus on robotic surgical solutions which are commercially available; research efforts which have not gained regulatory approval or entered clinical use are mostly omitted. The practice of robotic surgery is currently largely dominated by the da Vinci system of Intuitive Surgical (Sunnyvale, CA, USA) but other commercial players have now entered the market with surgical robotic products or are appearing in the horizon with medium and long term propositions. Surgical robotics is currently a vibrant research topic and new research directions may lead to the development of very different robotic surgical devices in the future—small, special purpose, lower cost, possibly disposable robots rather than the current large, versatile and capital expensive systems. As the trend towards minimally invasive surgery (MIS) increases, surgery becomes more technically demanding for surgeons and more challenging for medical device technologists and it is clear that surgical robotics has now an established foothold in medicine as an enabling technology of MIS.     

Neural networks in robotics: A survey

The purpose of this paper is to provide an overview of the research being done in neural network approaches to robotics, outline the strengths and weaknesses of current approaches, and predict future trends in this area.This work was supported, in part, by Sandia National Laboratories under contract No. 06-1977, Albuquerque, New Mexico.     

Testbeds for ubiquitous robotics: A survey

The growing interest in ubiquitous robotics has originated in the last years the development of a high variety of testbeds. This paper presents a survey on existing ubiquitous robotics testbeds comprising networked mobile robots and networks of distributed sensors, cameras and smartphones, among others. The survey provides an insight into the testbed design, internal behavior and use, identifying trends and existing gaps and proposing guidelines for testbed developers. The level of interoperability among different ubiquitous robotics technologies is used as the main conducting criterion of the survey. Other features analyzed include testbed architectures, target experiments and usability tools.     

A probabilistic finite state machine based strategy for multi-target search using swarm robotics

As a distributed system, swarm robotics is well suited for the multi-target search task where a single robot is rather inefficient. In this paper, a model of the multi-target search problem in swarm robotics and its approximate mathematical representation are given, based on which a lower bound of the expected number of iterations is drawn. Two categories of behavior-based strategies for target search are introduced: one is inspired from swarm intelligence optimization while the other from random walk. A novel search strategy based on probabilistic finite state machine is put forward, showing the highest efficiency in all presented algorithms, which is very close to the optimal value in situations with a large number of robots. It has been demonstrated by extensive experiments that the novel strategy has excellent stability, striking a good balance between exploration and exploitation, as well as a good trade-off between parallelism and cooperative capability.     

Precise and efficient pose estimation of stacked objects for mobile manipulation in industrial robotics challenges

Object manipulation tasks such as picking up, carrying and placing should be executed based on the information of objects which are provided by the perception system. A precise and efficient pose estimation system has been developed to address the requirements and to achieve the objectives for autonomous packaging, specifically picking up of stacked non-rigid objects. For fine pose estimation, a drawing pin shaped kernel and pinhole filtering methods are used on the roughly estimated pose of objects. The system has been applied in a realistic industrial environment as a challenging scenario for the Challenge 2 – Shop Floor Logistics and Manipulation on a mobile manipulator in the context of the European Robotics Challenges (EuRoC) project.     

Cognitive robotics: a new approach to artificial intelligence

This paper describes a research program about how to achieve artificial intelligence by building robots. It is part of the behavior-oriented AI approach, but differs in some of its hypotheses and methodological approach. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Survey on frontiers of language and robotics

ABSTRACT

The understanding and acquisition of a language in a real-world environment is an important task for future robotics services. Natural language processing and cognitive robotics have both been focusing on the problem for decades using machine learning. However, many problems remain unsolved despite significant progress in machine learning (such as deep learning and probabilistic generative models) during the past decade. The remaining problems have not been systematically surveyed and organized, as most of them are highly interdisciplinary challenges for language and robotics. This study conducts a survey on the frontier of the intersection of the research fields of language and robotics, ranging from logic probabilistic programming to designing a competition to evaluate language understanding systems. We focus on cognitive developmental robots that can learn a language from interaction with their environment and unsupervised learning methods that enable robots to learn a language without hand-crafted training data.