Transparency is an important aspect of human–robot interaction (HRI), as it can improve system trust and usability, leading to improved communication and performance. However, most transparency models focus only on the amount of information given to users. In this article, we propose a bidirectional transparency model, termed the transparency-based action (TBA) model, which allows the robot to take actions based on transparency information received from the human (robot-of-human and human-to-robot), in addition to providing transparency information to the human (robot-to-human). To examine the impact of a three-level (High, Medium and Low) TBA model on acceptance and HRI, we first implemented the model on a robotic system trainer in two pilot studies (with students as participants). Based on the results of these studies, the Medium TBA level was not included in the subsequent main experiment, which was conducted with older adults (aged 75–85). In that experiment, two TBA levels were compared: Low (basic information including only robot-to-human transparency) and High (including additional information relating to predicted outcomes with robot-of-human and human-to-robot transparency). The results revealed a statistically significant difference between the two TBA levels of the model in terms of perceived usefulness , ease of use , and attitude . The High TBA level was preferred by users and yielded improved user acceptance.

We investigated variable autonomy in a teleoperated robot navigation task within a simulated office environment, focusing on who controls the shift between levels of automation (LoA). Two switch control modes—human-initiated (HI) and robot-initiated (RI)—were examined. The study assessed whether these modes influence operators’ cognitive workload, situational awareness (SA), and task performance. The task involved two LoA conditions: teleoperation (low LoA) and remote monitoring (high LoA), while participants simultaneously performed a secondary task. Task performance was measured through completion time, collision rates, time spent in low LoA, and secondary task performance. Operator-related measures included workload National Aeronautics and Space Administration Task Load Index (NASA-TLX), trust, ease of use, and perceived SA. The results revealed a clear tradeoff between operational efficiency and situational awareness. The RI mode improved task efficiency and reduced workload but compromised SA, whereas HI mode enhanced SA at the cost of efficiency. These findings highlight the inherent tension between automation-driven performance gains and operator’s cognitive engagement. The study contributes to the understanding of variable autonomy in human–robot collaboration (HRC) and underscores the need to evaluate both operator experience and task performance when designing teleoperation systems for habituated environments.

With an increasing interest in human-robot collaboration, there is a need to develop robot behavior while keeping the human user’s preferences in mind. Highly skilled human users doing delicate tasks require their robot partners to behave according to their work habits and task constraints. To achieve this, we present the use of the Optometrist’s Algorithm (OA) to interactively and intuitively personalize robot-human handovers. Using this algorithm, we tune controller parameters for speed, location, and effort. We study the differences in the fluency of the handovers before and after tuning and the subjective perception of this process in a study of N = 30 non-expert users of mixed background – evaluating the OA. The users evaluate the interaction on trust, safety, and workload scales, amongst other measures. They assess our tuning process to be engaging and easy to use. Personalization leads to an increase in the fluency of the interaction. Our participants utilize the wide range of parameters ending up with their unique personalized handover.

Mobile robotic telepresence (MRP) has emerged as a possible solution for supporting health caregivers in a multitude of tasks such as monitoring, pre-diagnosis, and delivery of items. Improved interaction with the system is an important part of using such MRP systems. The current study compared two feedback types ('push' and 'pull') for controlling mobile robots via telepresence. An experimental system that represented a hospital environment was developed. A remote operator (defined as a user) teleoperated a mobile robot to deliver medication supplies to a patient and receive samples from the patient while attending to a secondary task involving medical records. The influence of the feedback types on different aspects of performance and user perception was investigated. User studies were performed with 20 participants coming from two different types of groups – users with and without technological backgrounds. Results revealed that for both user types, the 'push' feedback enhances performance, situation awareness, and satisfaction compared to the 'pull' feedback. The study highlights the potential of improving the telecare experience with MRPs through different feedback types.

This study explored the culture and gender differences among older adults interacting with polite non-humanoid robots. We have used Lakoff theory for the polite conversational maxims. A within-subjects experiment was designed with polite and non-polite, correct and erroneous behaving robots. The polite robot employs three sub-rules ("don’t impose," "give options," and "be friendly"). A user study was conducted with older adults from two cultural backgrounds: Israeli Jewish and Arab participants. The study revealed that the participants could not differentiate between the polite behaviors when the robot was correct. They were more annoyed with a polite robot making an error as compared to a non-polite erroneous robot. Whereas gender had no impact on participants’ evaluations, there were significant cultural differences between the groups: Although the Arab participants were primarily more skeptical, they enjoyed, were satisfied, and trusted the robot more than the Jewish participants. In addition, they preferred the direct non-polite behavior, whereas the Jewish participants liked the polite behavior more.

Technology domestication in old age may promote autonomy and support aging in place, but most previous research did not follow the process of domestication over time and in real life conditions. To gain deeper understanding of technology domestication in later life, we simultaneously explored uses, outcomes and constraints in real life conditions in a longitudinal study. Nineteen community-dwelling women aged 75–90 were provided with voice-controlled Intelligent Personal Assistants (Google Home) and their experiences with them were documented for three months via semi-structured interviews, observations, and weekly surveys. Analysis identified three different patterns of technology domestication: “Broad domestication” characterized by a high level of integration and ongoing experimentation, “focused domestication” in which the user mainly adopted one of the device’s functions, and “restrained domestication” wherein a short period of experimentation was followed by occasional use, if any. Demonstrating that the process of technology domestication is not homogeneous, the findings call for some theoretical updates and offer several practical implications.

Caregivers in eldercare can benefit from telepresence robots that allow them to perform a variety of tasks remotely. In order for such robots to be operated effectively and efficiently by non-technical users, it is important to examine if and how the robotic system’s level of automation (LOA) impacts their performance. The objective of this work was to develop suitable LOA modes for a mobile robotic telepresence (MRP) system for eldercare and assess their influence on users’ performance, workload, awareness of the environment, and usability at two different levels of task complexity. For this purpose, two LOA modes were implemented on the MRP platform: assisted teleoperation (low LOA mode) and autonomous navigation (high LOA mode). The system was evaluated in a user study with 20 participants, who, in the role of the caregiver, navigated the robot through a home-like environment to perform control and perception tasks. Results revealed that performance improved in the high LOA when task complexity was low. However, when task complexity increased, lower LOA improved performance. This opposite trend was also observed in the results for workload and situation awareness. We discuss the results in terms of the LOAs’ impact on users’ attitude towards automation and implications on usability.

It is important to encourage older adults to remain active when interacting with assistive robots. This study proposes a schematic model for integrating levels of automation (LOAs) and transparency (LoTs) in assistive robots to match the preferences and expectations of older adults. Metrics to evaluate LOA and LoT design combinations are defined. We develop two distinctive test cases to examine interaction design considerations for robots working for this population in everyday tasks: a person-following task with a mobile robot and a table-setting task with a robot manipulator. Evaluations in user studies with older adults reveal that LOA and LoT combinations influence interaction elements. Low LOA and high LoT encouraged activity engagement while receiving adequate information regarding the robot's behavior. The variety of objective and subjective metrics is essential to provide a holistic framework for evaluating the interaction.

This paper presents the use of dynamic sensing strategies to improve detection results for a pepper harvesting robot. The algorithm decides if an additional viewpoint is needed and selects the best-fit viewpoint location from a predefined set of locations based on the predicted profitability of such an action. The suggestion of a possible additional viewpoint is based on image analysis for fruit and occlusion level detection, prediction of the expected number of additional targets sensed from that viewpoint, and final decision if choosing the additional viewpoint is beneficial. The developed heuristic was applied on 96 greenhouse images of 30 sweet peppers and resulted in up to 19% improved detection. The harvesting utility cost function decreased by up to 10% compared to the conventional single viewpoint strategy.

This paper presents a device for accurate pesticide spraying capable of dealing with amorphous shapes and variable-sized targets. The device includes a single spray nozzle with an automatically adjustable spraying angle, color camera, and distance sensors, all mounted on a pan tilt unit. The site-specific spraying device aims to spray specific targets while reducing the use of pesticides. The spraying diameter is set as the minimum closing circle diameter according to the shape and size of the target. Two preliminary experiments were conducted in order to evaluate the spray nozzle flow rate in relation to the spray diameter and the spray diameter in relation to the nozzle's angular position. A main outdoor experiment was conducted to evaluate the complete spraying device using artificial targets of varying sizes. The results indicated that the spraying device is capable of reducing the amount of pesticides applied. An economic analysis estimates that up to 45% of pesticide reduction is possible when using the suggested spraying method. Actual savings depend on the spraying durations, target size, and distribution.

Robots designed to support people in different tasks at home and in public areas need to be able to recognize user intentions and operate accordingly. To date, research has focused on developing the technological capabilities of the robot and the mechanism of recognition. Little is known about navigational commands that could be intuitively communicated by people in order to control a robot's movement. A two-part exploratory study was conducted to evaluate how people naturally guide the motion of a robot and whether an existing gesture vocabulary used for human-human communication can be applied to human-robot interaction. Fourteen participants were first asked to demonstrate ten different navigational commands while interacting with a Pioneer robot using a WoZ technique. In the second part of the study participants were asked to identify eight predefined commands from the U.S. Army vocabulary. The results of this study could serve as a baseline for future development of command vocabulary promoting a more natural and intuitive human-robot interaction style.

Robots that operate alongside people need to be able to move in socially acceptable ways. As a step toward this goal, we study how and under which circumstances the angle at which a robot follows a person may affect the human experience and robot tracking performance. In this paper, we aimed to assess three following angles (0° angle, 30° angle, and 60° angle) under two conditions: when the robot was carrying a valuable personal item or not. Objective and subjective indicators of the quality of following and participants' perceptions and preferences were collected. Results indicated that the personal item manipulation increased awareness to the quality of the following and the following angles. Without the manipulation, participants were indifferent to the behavior of the robot. Our following algorithm was successful for tracking at a 0° and 30° angle, yet it must be improved for wider angles. Further research is required to obtain better understanding of following angle preferences for varying environment and task conditions.

Three advanced natural interaction modalities for mobile robot guidance in an indoor environment were developed and compared using two tasks and quantitative metrics to measure performance and workload. The first interaction modality is based on direct physical interaction requiring the human user to push the robot in order to displace it. The second and third interaction modalities exploit a 3-D vision-based human-skeleton tracking allowing the user to guide the robot by either walking in front of it or by pointing toward a desired location. In the first task, the participants were asked to guide the robot between different rooms in a simulated physical apartment requiring rough movement of the robot through designated areas. The second task evaluated robot guidance in the same environment through a set of waypoints, which required accurate movements. The three interaction modalities were implemented on a generic differential drive mobile platform equipped with a pan-tilt system and a Kinect camera. Task completion time and accuracy were used as metrics to assess the users' performance, while the NASA-TLX questionnaire was used to evaluate the users' workload. A study with 24 participants indicated that choice of interaction modality had significant effect on completion time (F (2, 61) = 84.874, p <; 0.001), accuracy (F (2, 29) = 4.937, p = 0.016), and workload (F (2, 68) = 11.948, p <; 0.001). The direct physical interaction required less time, provided more accuracy and less workload than the two contactless interaction modalities. Between the two contactless interaction modalities, the person-following interaction modality was systematically better than the pointing-control one: The participants completed the tasks faster with less workload.

The video shows the interaction with a customized Kompa\"{i} robot. The robot consists of the Robosoft's robuLAB10 platform, tablet PC, and a Microsoft Kinect camera mounted on a pan-tilt system. A visual control algorithm provides continuous person tracking. The newly developed robot features include gesture recognition, person following, navigation with pointing, and force control, which were integrated with the Robosoft's robuBOX SDK and the Karto SLAM algorithms. The video demonstrates all the features and puts the robot in use in an everyday home scenario.

There is limited knowledge on how to design effective interfaces to enable nonexpert users to interact with robot learning algorithms. This paper focuses on an interface design challenge: How to provide the user with sufficient information about the learned behavior. A simulated robotic task where the robot has online learning capabilities was developed. This platform was used to study the impact of the variability of the environmental conditions, the information provided on the relation between the learned robot behavior and the conditions in the environment, and the presence of a preview of the learned robot behavior on the use of the system and on task performance. The results show significant effects of the type and the quantity of displayed information. Forty-two participants made the best use of brief and contextualized notifications about changes in the environment: their presence improved the overall performance and the usage of the automation and reduced the workload. In contrast, adding previews of the learned behavior surprisingly impaired performance and reduced the use of automation.

In this paper, we propose a Modified Distributed Bees Algorithm (MDBA) for multi-sensor task allocation in a supply chain security scenario. The MDBA assigns sensors to the upcoming tasks using a decentralized, probabilistic approach to maximize information gain while minimizing costs. Tasks are allocated based on sensors' performance, tasks' priorities and the mutual sensor-task distances. Simulation analysis compared different algorithms and indicated improved performance of 15% by using MDBA with respect to the second-best algorithm.

This paper presents a generalized method for the design of a gesture vocabulary (GV) for intuitive and natural two-way human-robot dialog. Two GV design methodologies are proposed; one for a robot GV (RGV) and a second for a human GV (HGV). The design is based on motion gestures exerted from a cohort of subjects in response to a set of tasks needed to execute several robot waiter (RW)-customer dialogs. Using a RW setting as a case study, preliminary experimental results indicate the unique nature of the HGV obtained.

We explore the problem where a group of robots with different velocities search for a target in an unbounded unknown environment. The target position is un known, hence, an online search algorithm is developed. The H-MRSTM algorithm (Heterogeneous Multi-Robot Search Time Multiplication), launches a group of n robots from a common starting location to search for the target. The robots are assigned to search inside a series of concentric discs with increasing radii. Each robot is assigned to search inside a disc and when completing the search inside this disc without finding the target, the robot is assigned to search in the next unoccupied disc. We prove that every algorithm that solves this search problem must have at least a quadratic time competitive complexity and prove that the H-MRSTM algorithm's complexity is also quadratic. Hence, we obtain both an upper and lower bound on the time competitive complexity of the search problem. Consequently, H-MRSTM is proved to be optimal. Simulations in various environments show that the average case performance of H-MRSTM is superior to that of homogeneous multi-robot and single robot algorithms. In depth simulation analyses evaluated the effect of several other parameters such as the initial disc search time, the distribution of the velocities, the number of robots and the position of the target.

This paper presents a set of algorithms that are developed for real-time dynamic switching between collaboration levels in a human-robot target recognition system. The algorithms were developed for a closed-loop controller to maximize system performance, despite deviations in the parameter values. These developments enable smooth real-time adaptation of the combined human-robot system to many possible changes of the environment, human operator, and robot performance. System performance was analyzed in simulations for a variety of target probability distributions. Two hundred independent simulations for each target probability distribution were conducted to calculate algorithm performance for a variety of conditions. Values for human operations were taken from a target recognition experiment dealing with detecting melons for a robotic melon harvester. The numerical analysis results indicated that the developed dynamic switching algorithms resulted in improved system performance that, in some cases, was increased by more than 90%.

Human-robot collaboration is essential when the robot operates in unstructured environments which change dynamically and require high perception capabilities. The design of such collaborative systems requires the system to be predictable, i.e., the system's response should be repeatable and as close as possible to the response expected by its users. This should enable users to comprehend and learn how the system operates and foresee the system's responses to their commands and actions. We present a study of remote controlling a robot. This study investigated the expectations, perception, behavior and preferences of users while issuing a “forward” movement command. The study aimed to determine if users expect and prefer a system response that is identical in the distance of movement (repeated quantity), or an adaptive movement whose size depends on the environment (i.e., repeated rule, such as movement until another command, until junctions and/or obstacles). Speech was the only modality of interaction in the two experiments performed. The results show that the movement manner adaptable to the environment is preferred. Although the manner of movement (set step size vs. continuous movement, stopping at junctions vs. not stopping) may affect the overall performance, especially in the learning stage, these differences are not always perceived by the users. Results indicate that a robot's response could be qualitatively similar rather than identical in quantity or quality (the direction is constant, but the movement and feedback manners may vary). Furthermore, the overall gained user experience compensates for minor variations in the system's response.

This paper presents a physical model developed to find the directions of forces and moments required to open a plastic bag - which forces will contribute toward opening the knot and which forces will lock it further. The analysis is part of the implementation of aQ(¿)-learning algorithm on a robot system. The learning task is to let a fixed-arm robot observe the position of a plastic bag located on a platform, grasp it, and learn how to shake out its contents in minimum time. The physical model proves that the learned optimal bag shaking policy is consistent with the physical model and shows that there were no subjective influences. Experimental results show that the learned policy actually converged to the best policy.

Robotic systems in unstructured environments must cope with unknown, unpredictable, and dynamic situations. Inherent uncertainty, and limited sensor accuracy and reliability impede target recognition performance. Introducing a human operator into the system can help improve performance and simplify the robotic system. In this paper, four basic levels of collaboration were defined for human-robot collaboration in target recognition tasks. An objective function that includes operational and time costs was developed to quantify performance and determine the best collaboration level. Signal detection theory was applied to evaluate system performance. The optimal collaboration level for different cases was determined by using numerical analyses of the objective function. The findings indicate that the best system performance, the optimal values of performance measures, and the best collaboration level depend on the task, the environment, human and robot parameters, and the system characteristics. For the tested cases, the manual level was never the best collaboration level for achieving the optimal solution. The autonomous level was the best collaboration level when robot sensitivity was higher than human sensitivity. In general, collaboration of human and robot in target recognition tasks will improve upon the optimal performance of a single human detector.

The use of doctor-computer interaction devices in the operation room (OR) requires new modalities that support medical imaging manipulation while allowing doctors' hands to remain sterile, supporting their focus of attention, and providing fast response times. This paper presents "Gestix," a vision-based hand gesture capture and recognition system that interprets in real-time the user's gestures for navigation and manipulation of images in an electronic medical record (EMR) database. Navigation and other gestures are translated to commands based on their temporal trajectories, through video capture. "Gestix" was tested during a brain biopsy procedure. In the in vivo experiment, this interface prevented the surgeon's focus shift and change of location while achieving a rapid intuitive reaction and easy interaction. Data from two usability tests provide insights and implications regarding human-computer interaction based on nonverbal conversational modalities.

Robots operating in unstructured environments must cope with uncertain, unpredictable and dynamic situations, resulting in lack of information. Introducing a human-operator into the system can help simplify the robotic system and improve performance. In previous work we presented a methodology to determine the best collaboration level for an integrated human-robot target recognition system in unstructured environments. The methodology is based on an objective function for target recognition in human-robot systems. In this paper the objective function was expanded to include operational and time costs that are both important for evaluating and optimizing system performance. Numerical analyses of the developed objective function, combined with signal detection theory, were applied for the predefined collaboration levels. Results indicate that optimal collaboration of human and robot in target recognition tasks will always improve the optimal performance of a single human detector.

This paper presents a thorough evaluation of grid map based sensor fusion algorithms for mapping the environment of a mobile robot. Three physical sensors were used for creating the grid maps: a CCD camera, a set of ultrasonic sensors and a laser rangefinder. An adaptive fuzzy logic algorithm was compared to three logical sensor fusion algorithms. Results indicate the superiority of the adaptive fuzzy logic algorithm with a 0.075 confidence level.

This paper presents a methodology to determine the best collaboration level for an integrated human-robot target recognition system in unstructured environments. Four human-robot collaboration levels for target recognition tasks were defined, tested and evaluated. The collaboration levels were adjusted to an extensive range of automation, from manual to fully autonomous. An objective function for target recognition in human-robot systems was developed to allow computation of the expected value of system performance given the human, robot, environmental and task parameters. The objective function includes operational and time costs that are both important in evaluation and optimization of system performance. It quantifies the multitude of influencing parameters through a weighted sum of performance measures, and enables to predict system performance and the desirable level of collaborations and to help design optimal systems for specific tasks. Numerical analysis of the developed objective function combined with signal detection theory was applied for the defined collaboration levels. Based on the numerical analysis, the human and robot optimal values were determined for different task and environment parameters. Results indicate that collaboration of human and robot in target recognition tasks will always improve the optimal performance of a single human detector. When robot sensitivity is higher than human sensitivity, and, the overall system sensitivity never decreases beneath the robot sensitivity the best collaboration level is autonomous robot operation.

This paper presents a collaborative reinforcement learning algorithm, CQ(lambda), designed to accelerate learning by integrating a human operator into the learning process. The CQ(lambda) -learning algorithm enables collaboration of knowledge between the robot and a human; the human, responsible for remotely monitoring the robot, suggests solutions when intervention is required. Based on its learning performance, the robot switches between fully autonomous operation, and the integration of human commands. The CQ(lambda) -learning algorithm was tested on a Motoman UP-6 fixed-arm robot required to empty the contents of a suspicious bag. Experimental results of comparing the CQ(lambda) with the standard Q(lambda), indicated the superiority of the CQ(lambda) while achieving an improvement of 21.25% in the average reward.

A global approach to hand gesture vocabulary design is proposed which includes human as well as technical design factors. The method of selecting gestures for preconceived command vocabularies has not been addressed in a systematic manner. Present methods are ad hoc. In an analytical approach technological factors of gesture recognition accuracy are easily obtained and well studied. Conversely, it is difficult to obtain measures of human centered desires (intuitiveness, comfort). These factors, being subjective, are costly and time consuming to obtain, and hence we have developed automated methods for acquisition of these data through specially designed applications. Results of the intuitiveness experiments showed when commands are presented as stimuli the gestural responses vary widely over a population of subjects. This result refutes the hypothesis that there exist universal common gestures to express user intentions or commands.

This paper presents a sensor fusion framework for mapping unknown environments for mobile robots. The proposed framework enables on-line selection of the most reliable logical sensors and the most suitable fusion algorithm. The framework is rule-based, employing the “simplest sensor fusion algorithm with the most reliable sensors” concept. This goal is achieved through measures that were developed to quantify on-line the performance of the sensors. The framework was evaluated in an experiment consisting of a mobile robot equipped with five logical sensors. The framework was compared to four other algorithms. The advantages of this new framework are presented using statistical, histogram, time series and graphical analyses.

In this work, we address the issue of reconfigurability of a hand-gesture recognition system. The calibration or setup of the operational parameters of such a system is a time-consuming effort, usually performed by trial and error, and often causing system performance to suffer because of designer impatience. In this work, we suggest a methodology using a neighborhood-search algorithm for tuning system parameters. Thus, the design of hand-gesture recognition systems is transformed into an optimization problem. To test the methodology, we address the difficult problem of simultaneous calibration of the parameters of the image processing/fuzzy C-means (FCM) components of a hand-gesture recognition system. In addition, we proffer a method for supervising the FCM algorithm using linear programming and heuristic labeling. Resulting solutions exhibited fast convergence (in the order of ten iterations) to reach recognition accuracies within several percent of the optimal. Comparative performance testing using three gesture databases (BGU, American Sign Language and Gripsee), and a real-time implementation (Tele-Gest) are reported on.

This work describes a hand gesture recognition system using an optimized image processing-fuzzy C-means (FCM) algorithm. The parameters of the image processing and clustering algorithm were simultaneously found using a neighborhood parameter search routine, resulting in solutions within 1-2% of optimal. Comparison of user dependent and user independent systems, when tested with their own trainers, resulted in recognition accuracies of 98.9% and 98.2%, respectively. For experienced users, the opposite was true, testing recognition accuracies where better for user independent than user dependent systems (98.2% over 96.0%). These results are statistically significant at the .007 levels.

This paper presents a navigation methodology for decentralized autonomous automated guided vehicles used for material handling. The navigation methodology is based on behavior-based control augmented with multirobot coordination behaviors and a priori waypoint determination. Results indicate that the developed methodology fuses well between the desires for optimal vehicle routes on the one hand and decentralized reactive operation on the other.

Intelligent sensing, planning, and control of a prototype robotic melon harvester is described. The robot consists of a Cartesian manipulator mounted on a mobile platform pulled by a tractor. Black and white image processing is used to detect and locate the melons. Incorporation of knowledge-based rules adapted to the specific melon variety reduces false detections. Task, motion and trajectory planning algorithms and their integration are described. The intelligent control system consists of a distributed blackboard system with autonomous modules for sensing, planning and control. Procedures for evaluating performance of the robot performing in an unstructured and changing environment are described. The robot was tested in the field on two different melon cultivars during two different seasons. Over 85% of the fruit were successfully detected and picked.

Continues studies of adaptive coordinate-parameter control, as described in previous papers (Slutski, 1997, 1998, and Slutski and Coiffet, 1996) to perform precise and fast teleoperations. In this research, the systems' realization conditions were studied by performing a series of system simulations. The adaptive algorithm was also implemented for industrial robot control using the two different ergonomic tools: mouse and joystick. The results obtained suggest approaches of improved design for adaptive system hand controllers.

When designing new equipment for telecontrol, many experimental investigations are required. This increases time and cost of elaboration. The development of new design methods for telerobotic systems can help reduce the number of experimental investigations. For preliminary design of remote control systems these investigations may be replaced by modeling and simulation procedures. One promising method is based on a behavior study of the human operator (HO). A great number of studies use the speed-accuracy trade-off known as Fitts's law. This estimate and its modifications as applied to teleoperator design makes it possible to evaluate and predict the teleoperator performance. However, these results do not always permit sufficient evaluation of system quality; such evaluations are not quite precise and the resultant criterion dependences do not always correspond to the design problem of a concrete remote control system. Hence, profound studies of HO behavior can help simplify these procedures. The paper proposes a new methodology to evaluate and predict telerobot quality by means of a refined model of human behavior. The method was applied to investigate a new adaptive algorithm for telerobot operations.

This paper presents a systems engineering method to evaluate the performance of an agricultural robot by simulating and comparing different types of robots, number of arms, multiple arm configurations, workspace design and dynamic characteristics. Numerical simulation tools were developed to quantify measures of machine performance such as cycle time and percentage of successful cycles based on an extensive statistical analysis using measured fruit locations and simulated crop parameters. The methodology developed was applied to determine design parameters for a robotic melon harvester. Simulation results indicated that the Cartesian robot was faster than the cylindrical robot for the melon harvesting task. Activating two arms in tandem was the fastest configuration evaluated.>

A near-minimum-time task-planning algorithm for fruit-harvesting robots having to pick fruits at N given locations is presented. For the given kinematic and inertial parameters of the manipulator, the algorithm determines the near-optimal sequence of fruit locations through which the arm should pass and finds the near-minimum-time path between these points. The sequence of motions was obtained by solving the traveling salesman problem (TSP) using the distance along the geodesics in the manipulator's inertia space, between every two fruit locations, as the cost to be minimized. The proposed algorithm was applied to define the motions of a citrus-picking robot and was tested for a cylindrical robot on fruit position data collected from 20 trees. Significant reduction in the required computing time was achieved by dividing the volume containing the fruits into subvolumes and estimating the geodesic distance rather than calculating it.>