Collaborative Gaze Channelling for Improved Cooperation During Robotic Assisted Surgery

The use of multiple robots for performing complex tasks is becoming a common practice for many robot applications. When different operators are involved, effective cooperation with anticipated manoeuvres is important for seamless, synergistic control of all the end-effectors. In this paper, the concept of Collaborative Gaze Channelling (CGC) is presented for improved control of surgical robots for a shared task. Through eye tracking, the fixations of each operator are monitored and presented in a shared surgical workspace. CGC permits remote or physically separated collaborators to share their intention by visualising the eye gaze of their counterparts, and thus recovers, to a certain extent, the information of mutual intent that we rely upon in a vis-à-vis working setting. In this study, the efficiency of surgical manipulation with and without CGC for controlling a pair of bimanual surgical robots is evaluated by analysing the level of coordination of two independent operators. Fitts' law is used to compare the quality of movement with or without CGC. A total of 40 subjects have been recruited for this study and the results show that the proposed CGC framework exhibits significant improvement (p<0.05) on all the motion indices used for quality assessment. This study demonstrates that visual guidance is an implicit yet effective way of communication during collaborative tasks for robotic surgery. Detailed experimental validation results demonstrate the potential clinical value of the proposed CGC framework. © 2012 Biomedical Engineering Society.link_to_subscribed_fulltex

Human-human haptic collaboration in cyclical Fitts' tasks

Understanding how humans assist each other in haptic interaction teams could lead to improved robotic aids to solo human dextrous manipulation. Inspired by experiments reported in Reed et al. (2004), which suggested two-person haptically interacting teams could achieve a lower movement time (MT) than individuals for discrete aiming movements of specified accuracy, we report that two-person teams (dyads) can also achieve lower MT for cyclical, continuous aiming movements. We propose a model, called endpoint compromise, for how the intended endpoints of both subjects' motion combine during haptic interaction; it predicts a ratio of /spl radic/2 between slopes of MT fits for individuals and dyads. This slope ratio prediction is supported by our data

Quantitative evaluation of Human-Robot options for maintenance tasks during analogue surface operations

Due to the scarcity of human labour plus the harsh conditions at any human Mars base of the foreseeable future, robots are likely to be employed in to assist with at least some assembly, deployment, transportation, inspection, servicing or repair tasks. By the first human landing, robotic technology is expected to have made possible the use of robot teams already on the surface to prepare the landing site, ensure the functioning of ISRU equipment and survey the local area for the arriving astronauts. Robots are also likely to assist them during their stay and after their departure. Today’s researchers are increasingly interested in the question of how to systematically choose the best combination of robots and/or humans for particular tasks, and how to actually demonstrate and measure teams performing these tasks in realistic simulations. This paper critically examines a quantitative method developed by Roderiguez and Weisbin of JPL for computing performance/resource scores for a range of human-machine systems on a variety of tasks. It then proposes a practical experiment, to be conducted at a future Mars Society surface operations simulation, that will apply the method to quantitatively compare human maintenance task scores with those of a hexapodal service robot that the author is currently building

Doctor of Philosophy

dissertationHumans generally have difficulty performing precision tasks with their unsupported hands. To compensate for this difficulty, people often seek to support or rest their hand and arm on a fixed surface. However, when the precision task needs to be performed over a workspace larger than what can be reached from a fixed position, a fixed support is no longer useful. This dissertation describes the development of the Active Handrest, a device that expands its user's dexterous workspace by providing ergonomic support and precise repositioning motions over a large workspace. The prototype Active Handrest is a planar computer-controlled support for the user's hand and arm. The device can be controlled through force input from the user, position input from a grasped tool, or a combination of inputs. The control algorithm of the Active Handrest converts the input(s) into device motions through admittance control where the device's desired velocity is calculated proportionally to the input force or its equivalent. A robotic 2-axis admittance device was constructed as the initial Planar Active Handrest, or PAHR, prototype. Experiments were conducted to optimize the device's control input strategies. Large workspace shape tracing experiments were used to compare the PAHR to unsupported, fixed support, and passive moveable support conditions. The Active Handrest was found to reduce task error and provide better speedaccuracy performance. Next, virtual fixture strategies were explored for the device. From the options considered, a virtual spring fixture strategy was chosen based on its effectiveness. An experiment was conducted to compare the PAHR with its virtual fixture strategy to traditional virtual fixture techniques for a grasped stylus. Virtual fixtures implemented on the Active Handrest were found to be as effective as fixtures implemented on a grasped tool. Finally, a higher degree-of-freedom Enhanced Planar Active Handrest, or E-PAHR, was constructed to provide support for large workspace precision tasks while more closely following the planar motions of the human arm. Experiments were conducted to investigate appropriate control strategies and device utility. The E-PAHR was found to provide a skill level equal to that of the PAHR with reduced user force input and lower perceived exertion

Adaptive human force scaling via admittance control for physical human-robot interaction

The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts’ reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.WOS:000731146900006Scopus - Affiliation ID: 60105072Q2ArticleUluslararası işbirliği ile yapılan - EVETOctober2021YÖK - 2021-22Eki

Considerations for Task Allocation in Human-Robot Teams

In human-robot teams where agents collaborate together, there needs to be a clear allocation of tasks to agents. Task allocation can aid in achieving the presumed benefits of human-robot teams, such as improved team performance. Many task allocation methods have been proposed that include factors such as agent capability, availability, workload, fatigue, and task and domain-specific parameters. In this paper, selected work on task allocation is reviewed. In addition, some areas for continued and further consideration in task allocation are discussed. These areas include level of collaboration, novel tasks, unknown and dynamic agent capabilities, negotiation and fairness, and ethics. Where applicable, we also mention some of our work on task allocation. Through continued efforts and considerations in task allocation, human-robot teaming can be improved.Comment: Presented at AI-HRI symposium as part of AAAI-FSS 2022 (arXiv:2209.14292

Some aspects of human performance in a Human Adaptive Mechatronics (HAM) system

An interest in developing the intelligent machine system that works in conjunction with human has been growing rapidly in recent years. A number of studies were conducted to shed light on how to design an interactive, adaptive and assistive machine system to serve a wide range of purposes including commonly seen ones like training, manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics (HAM) was proposed to resolve these issues. According to past research, the focus is predominantly on evaluation of human skill rather than human performance and that is the reason why intensive training and selection of suitable human subjects for those experiments were required. As a result, the pattern and state of control motion are of critical concern for these works. In this research, a focus on human skill is shifted to human performance instead due to its proneness to negligence and lack of reflection on actual work quality. Human performance or Human Performance Index (HPI) is defined to consist of speed and accuracy characteristics according to a well-renowned speed-accuracy trade-off or Fitts’ Law. Speed and accuracy characteristics are collectively referred to as speed and accuracy criteria with corresponding contributors referred to as speed and accuracy variables respectively. This research aims at proving a validity of the HPI concept for the systems with different architecture or the one with and without hardware elements. A direct use of system output logged from the operating field is considered the main method of HPI computation, which is referred to as a non-model approach in this thesis. To ensure the validity of these results, they are compared against a model-based approach based on System Identification theory. Its name is due to being involved with a derivation of mathematical equation for human operator and extraction of performance variables. Certain steps are required to match the processing outlined in that of non-model approach. Some human operators with complicated output patterns are inaccurately derived and explained by the ARX models

Anthropomorphic Robot Design and User Interaction Associated with Motion

Though in its original concept a robot was conceived to have some human-like shape, most robots now in use have specific industrial purposes and do not closely resemble humans. Nevertheless, robots that resemble human form in some way have continued to be introduced. They are called anthropomorphic robots. The fact that the user interface to all robots is now highly mediated means that the form of the user interface is not necessarily connected to the robots form, human or otherwise. Consequently, the unique way the design of anthropomorphic robots affects their user interaction is through their general appearance and the way they move. These robots human-like appearance acts as a kind of generalized predictor that gives its operators, and those with whom they may directly work, the expectation that they will behave to some extent like a human. This expectation is especially prominent for interactions with social robots, which are built to enhance it. Often interaction with them may be mainly cognitive because they are not necessarily kinematically intricate enough for complex physical interaction. Their body movement, for example, may be limited to simple wheeled locomotion. An anthropomorphic robot with human form, however, can be kinematically complex and designed, for example, to reproduce the details of human limb, torso, and head movement. Because of the mediated nature of robot control, there remains in general no necessary connection between the specific form of user interface and the anthropomorphic form of the robot. But their anthropomorphic kinematics and dynamics imply that the impact of their design shows up in the way the robot moves. The central finding of this report is that the control of this motion is a basic design element through which the anthropomorphic form can affect user interaction. In particular, designers of anthropomorphic robots can take advantage of the inherent human-like movement to 1) improve the users direct manual control over robot limbs and body positions, 2) improve users ability to detect anomalous robot behavior which could signal malfunction, and 3) enable users to be better able to infer the intent of robot movement. These three benefits of anthropomorphic design are inherent implications of the anthropomorphic form but they need to be recognized by designers as part of anthropomorphic design and explicitly enhanced to maximize their beneficial impact. Examples of such enhancements are provided in this report. If implemented, these benefits of anthropomorphic design can help reduce the risk of Inadequate Design of Human and Automation Robotic Integration (HARI) associated with the HARI-01 gap by providing efficient and dexterous operator control over robots and by improving operator ability to detect malfunctions and understand the intention of robot movement