Search results

The authors have developed a robot called “Robovie” that has unique mechanisms designed for communication with humans. Robovie can generate human‐like behaviors by using human‐like actuators and vision and audio sensors. Software is a key element in the systems development. Two important ideas in human‐robot communication through research from the viewpoint of cognitive science have been obtained – one is importance of physical expressions using the body and the other is the effectiveness of the robot’s autonomy in the robot’s utterance recognition by humans. Based on these psychological experiments, a new architecture that generates episode chains in interactions with humans is developed. The basic structure of the architecture is a network of situated modules. Each module consists of elemental behaviors to entrain humans and a behavior for communicating with humans.

The purpose of this paper is to focus on the local minima issue encountered in motion planning by the artificial potential field (APF) method, investigate the currently existing approaches and analyze four types of previous methods. Based on the conclusions of analysis, this paper presents an improved wall‐following approach for real‐time application in mobile robots.

In the proposed method, new switching conditions among various behaviors are reasonably designed in order to guarantee the reliability and the generality of the method. In addition, path memory is incorporated in this method to enhance the robot's cognition capability to the environment. Therefore, the new method greatly weakens the blindness of decision making of robot and it is very helpful to select appropriate behaviors facing to the changeable situation. Comparing with the previous methods which are normally considering specific obstacles, the effectiveness of this proposed method for the environment with convex polygon‐shaped obstacles has been theoretically proved. The simulation and experimental results further demonstrate that the proposed method is adaptable for the environment with convex polygon‐shaped obstacles or non‐convex polygon‐shaped obstacles. It has more widely generality and adaptiveness than other existed methods in complicated unknown environment.

The proposed method can effectively realize real time motion planning with high reliability and generality. The cognition capability of mobile robot to the environment can be improved in order to adapt to the changeable situation. The proposed method can be suitable to more complex unknown environment. It is more applicable for actual environment comparing with other traditional APF methods.

This paper has widely investigated the currently existed approaches and analyzes deeply on four types of traditional APF methods adopted for real time motion planning in unknown environment with simulation works. Based on the conclusions of analysis, this paper presents an improved wall‐following approach. The proposed method can realize real time motion planning considering more complex environment with high reliability and generality. The simulation and experimental results further demonstrate that the proposed method is adaptable for the environment with convex polygon‐shaped obstacles or non‐convex polygon‐shaped obstacles. It has more widely generality and adaptiveness than other existed methods in complicated unknown environment.

This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment.

Neural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment.

Computer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments.

The proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.

This paper aims to deal with the problem of designing robot behaviors (mainly to robotic arms) to express emotions. The authors study the effects of robot behaviors from our humanoid robot NAO on the subject’s emotion expression in human–robot interaction (HRI).

A method to design robot behavior through the movement primitives is proposed. Then, a novel dimensional affective model is built. Finally, the concept of action semantics is adopted to combine the robot behaviors with emotion expression.

For the evaluation of this combination, the authors assess positive (excited and happy) and negative (frightened and sad) emotional patterns on 20 subjects which are divided into two groups (whether they were familiar with robots). The results show that the recognition of the different emotion patterns does not have differences between the two groups and the subjects could recognize the robot behaviors with emotions.

Using affective models to guide robots’ behavior or express their intentions is highly beneficial in human–robot interaction. The authors think about several applications of the emotional motion: improve efficiency in HRI, direct people during disasters, better understanding with human partners or help people perform their tasks better.

This paper presents a method to design robot behaviors with emotion expression. Meanwhile, a similar methodology can be used in other parts (leg, torso, head and so on) of humanoid robots or non-humanoid robots, such as industrial robots.

The purpose of this paper is to consider the logical specification, and automated verification, of high‐level robotic behaviours.

The paper uses temporal logic as a formal language for providing abstractions of foraging robot behaviour, and successively extends this to multiple robots, items of food for the robots to collect, and constraints on the real‐time behaviour of robots. For each of these scenarios, proofs of relevant properties are carried out in a fully automated way. In addition to automated deductive proofs in propositional temporal logic, the possibility of having arbitrary numbers of robots involved is considered, thus allowing representations of robot swarms. This leads towards the use of first‐order temporal logics (FOTLs).

The proofs of many properties are achieved using automatic deductive temporal provers for the propositional and FOTLs.

Many details of the problem, such as location of the robots, avoidance, etc. are abstracted away.

Large robot swarms are beyond the current capability of propositional temporal provers. Whilst representing and proving properties of arbitrarily large swarms using FOTLs is feasible, the representation of infinite numbers of pieces of food is outside of the decidable fragment of FOTL targeted, and practically, the provers struggle with even small numbers of pieces of food.

The work described in this paper is novel in that it applies automatic temporal theorem provers to proving properties of robotic behaviour.

The purpose of this paper is to examine the ability of high-functioning autistic (HFA) children to programme robotic behaviour and sought to elucidate how they describe and construct a robot’s behaviour using iconic programming software.

The robotic learning environment is based on the iPad, an iconic programming app (KinderBot), and EV3. Two case studies, of A. and N., both HFA children of average age 10.5, are the focus of this research.

The research revealed how the participants succeeded in programming the behaviour of an “other” at different programming complexity levels (from simple action to combinations of states of two binary sensors and rule with subroutine). A transformation from procedural to declarative description was also found.

This research on the ability of HFA children to programme robotic behaviour yielded results that can be implemented in K-12 education. Furthermore, learning to programme robots and understand how robotic technologies work may help HFA children to better understand other technology in their environment.

In this research, the authors present an innovative approach that for the first time enables HFA children to “design” the behaviour of smart artefacts to use their sensors to adapt in accordance with the environment. For most HFA children, this would be the first opportunity to “design” the behaviour of the other, as opposed to oneself, since in most of their experience they have been largely controlled by another person.

This study aims to apply the elaboration likelihood model to explore when, how and why robotic services increase customer engagement.

A field survey and two experiments were conducted to examine the proposed theoretical framework.

The robots’ proactive behavior encouraged customers to trust and engage with them. The influence of this behavior on customer engagement increased for highly interaction-oriented customers or when the reputations of companies were poor.

The findings can inform the efficient management of customer–robot interactions and thus support firms’ relationship marketing objectives.

The literature on robotic services has recognized that robots should be proactive to ensure positive customer experiences, but few studies have explored the relational outcomes of proactive robotic services. The authors’ in-depth empirical examination thus extends research into the role these services can play in fostering customer engagement.

The purpose of this research is to understand whether service robots can safeguard servicescapes from deviant consumer behaviour. Using routine activity theory, this research examines whether increasing the perceived humanness of service robots reduces customer intentions to commit deviant consumer behaviour and whether this negative relationship is mediated by perceived empathy and perceived risk of being caught.

Five hundred and fifty-three US residents responded to a hypothetical scenario that manipulated the humanness of a service agent (from self-service technology, to robot, to human employee) across seven conditions and measured the likelihood of deviant consumer behaviour, empathy towards the service robot, perceived risk of being caught and punished and negative attitudes towards robots.

The results indicate that replacing human service agents with different types of service robots does inadvertently reduce customer perceptions of capable guardianship (i.e. the human element that deters potential offenders from committing crimes) in the servicescape and creates conditions that allow customers to perpetrate more deviant consumer behaviour.

When investing in technology such as service robots, service providers need to consider the unintended cost of customer misbehaviour (specifically deviant consumer behaviour) in their return-on-investment assessments to optimise their asset investment decisions.

Moving beyond research on customer adoption and use, this research examines the unintended consequences that might arise when deploying service robots in a technology-infused service environment. Humanised service robots offer more guardianship than self-service technology but do not replace human employees in preventing deviant consumer behaviour, as they remain more capable of deterring customer misbehaviour.

In a soccer robot game, the environment is highly competitive and dynamic. In order to work in the dynamically changing environment, the decision‐making system of a soccer robot system should have the features of flexibility and real‐time adaptation. The purpose of this paper is to focus on the middle‐size soccer robot league (MSL) and present new hierarchical hybrid fuzzy methods for decision making and action selection of an MSL robot.

In this paper, new hierarchical hybrid fuzzy methods for decision making and action selection of a robot in MSL are presented. First, the behaviors of an agent are introduced, implemented and classified in two layers, the low‐level behaviors and the high‐level behaviors. In the second layer, a two‐phase mechanism for decision making is introduced. In phase one, some useful methods are implemented which check the robot's situation for performing required behaviors. In the next phase, the team strategy, team formation, robot's role and the robot's positioning system are introduced. A fuzzy logical approach is employed to recognize the team strategy and furthermore to tell the player the best position to move.

This methodology was implemented on the ADRO RoboCup Team and ADRO team performance 2008 was compared with its previous version 2007. The results showed the success of this methodology; the team performance in coordination and collaboration highly improved; in fact, the players switched their strategic area smoothly as the team strategy changed in a reasonable manner, the robots carried out the high‐level behaviors much more efficiently and the final results were enhanced significantly.

This paper is a result of the authors' original research work in the field of autonomous robot‐middle size soccer robot, supported by Islamic Azad University – Khorasgan Branch, Isfahan, Iran.

The Group of Unmanned Assistant Robots Deployed in Aggregative Navigation by Scent (GUARDIANS) multi‐robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots act alongside a firefighter and guide and accompany the firefighters on the site while indicating possible obstacles and the locations of danger and maintain communications links. The purpose of this paper is to focus on basic navigation behaviours of multi‐robot or human‐robot teams, which have to be achieved without central and on‐line control in both categories of GUARDIANS robots' tasks.

In order to fulfill the aforementioned tasks, the robots need to be able to perform certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so‐called social potential field framework, which the authors adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots.

The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus, the application has forced the concept of a formation to be re‐defined. Using the graph‐theoretic terminology, it can be said that a formation may be stretched out as a path or be compact as a star or wheel. The developed behaviours have been implemented in simulation environments as well as on real ERA‐MOBI robots commonly referred to as Erratics. Advantages and shortcomings of the model, based on the simulations as well as on the implementation with a team of Erratics are discussed.

This paper discusses the concept of a robot formation in the context of a real world application of a robot team (Swarm).