Dynamic Workspace Research Articles

Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation

Real-world work environments require operators to perform multiple tasks with continual support from an automated system. Eye movement is often used as a surrogate measure of operator attention, yet conventional summary measures such as percent dwell time do not capture dynamic transitions of attention in complex visual workspace. This study analyzed eye movement data collected in a controlled a MATB-II task environment using gaze transition entropy analysis. In the study, human subjects performed a compensatory tracking task, a system monitoring task, and a communication task concurrently. The results indicate that both gaze transition entropy and stationary gaze entropy, measures of randomness in eye movements, decrease when the compensatory tracking task required more continuous monitoring. The findings imply that gaze transition entropy reflects attention allocation of operators performing dynamic operational tasks consistently.

Mapping Translation & Interpretation Courses Using Student Digital Portfolio Development

Faculty are consistently challenged by the ongoing task of establishing a chronological and coherent order to course content that students can follow without confusion while ensuring thorough coverage of learning outcomes. This article advocates for the innovative use of websites as digital tools to streamline student navigation through course material. By immersing students in fundamental translation and interpretation methodologies, these online platforms transform into dynamic workspaces, fostering practical skills essential for future professional endeavors. Website creation acts as a bridge, seamlessly integrating academic teachings on principles, techniques, and processes of translation and interpretation with practical aspects of professional readiness. Through this integration, students gain invaluable insights into market dynamics, client acquisition strategies, pricing methodologies, and ethical considerations crucial in the translation and interpretation industry. Several theoretical frameworks, including constructivism, experiential learning, social constructivism, and connectivism, support the use of website portfolio development as a teaching tool. By aligning website portfolio development with these frameworks, educators tap into the potential of digital tools to enrich student learning, increase engagement, and prepare them for the complexities of the modern world. Consequently, the utilization of websites as educational tools enhances student engagement and comprehension and promotes a strong foundation for success in the ever-evolving field of translation and interpretation. This article concludes with a sample website developed by a student to provide faculty with a framework that they can use to implement a website portfolio into their translation courses.

Development and Validation of a Screw Interlock Recognition Method based on Logistic Regression

Conventional robotic screw methods rely on position control and the use of a threshold value, normally a contact force value, to detect the contact between tool and screw, so the nut-runner can be placed correctly on the workpiece. In the case of a dynamic workspace, the variance of the workpiece position will likely cause the nut-runner to be positioned incorrectly and will significantly decrease the success rate of the screw task.This paper proposes a novel method based on a logistic regression model for flexible Human-Robot-Collaboration (HRC) screw assembly operations in highly dynamic workspaces. The proposed method is part of a screwing strategy based on impedance control and developed for HRC applications. The screwing strategy consists of a spiral movement executed by the robot while approaching the workpiece and it is used as a search procedure to find the screw. The proposed method is used to detect when the tool correctly interlocks with the screw head so the robot can proceed with the screwing process. The goal is to stop the spiral movement timely when the nut-runner has correctly interlocked with the screw head to ensure a successful screw task and avoid potential damage to the nut-runner or the workpiece.The proposed screw interlock recognition method utilizes a logistic regression model to observe the contact forces between tool and screw head. The learning model is trained using force data collected from experiments and then its feasibility is validated with further testing.

Mobile robot’s path-planning and path-tracking in static and dynamic environments: Dynamic programming approach

One of the main issues in robotics systems is planning and tracking a safe path in diverse environments. This paper addresses an optimal methodology for generating the desired path and thereafter forces the mobile robot to follow the designed reference path. The proposed technique has the potential to tackle the inherent challenges and intricacies of the environment, enabling the robot to navigate both static and dynamic workspaces. Several simulations are exploited to verify the captured theoretical results. Three cases were examined in a static environment, achieving average performance metrics for reference signals in the X- and Y-directions, and heading tracking of 99.62%, 99.64%, and 95.08%, respectively. For the dynamic environment, two cases were studied, with average performance in following the Y-direction and heading angle recorded as 97.58% and 86.79%, respectively. Simulation results show that the controller calculated the optimal signal with an average computational time of 7 ms per iteration for static environments and 21.3 ms per iteration for dynamic environments.

PiP-X: Online feedback motion planning/replanning in dynamic environments using invariant funnels

Computing kinodynamically feasible motion plans and repairing them on-the-fly as the environment changes is a challenging, yet relevant problem in robot navigation. We propose an online single-query sampling-based motion re-planning algorithm using finite-time invariant sets, commonly referred to as “ funnels”. We combine concepts from nonlinear systems analysis, sampling-based techniques, and graph-search methods to create a single framework that enables feedback motion re-planning for any general nonlinear dynamical system in dynamic workspaces. A volumetric network of funnels is constructed in the configuration space using sampling-based methods and invariant set theory; and an optimal sequencing of funnels from robot configuration to a desired goal region is then determined by computing the shortest-path subgraph (tree) in the network. Analyzing and formally quantifying the stability of trajectories using Lyapunov level-sets ensures kinodynamic feasibility and guaranteed set-invariance of the solution paths. Though not required, our method is capable of using a pre-computed library of motion primitives to speedup online computation of controllable motion plans that are volumetric in nature. We introduce a novel directed-graph data structure to represent the funnel-network and its inter-sequencibility; helping us leverage discrete graph-based incremental search to quickly rewire feasible and controllable motion plans on-the-fly in response to changes in the environment. We validate our approach on a simulated cart-pole, car-like robot, and 6DOF quadrotor platform in a variety of scenarios within a maze and a random forest environment. Using Monte Carlo methods, we evaluate the performance in terms of algorithm success, length of traversed trajectory, and runtime.

Saliency-Induced Moving Object Detection for Robust RGB-D Vision Navigation Under Complex Dynamic Environments

Localization in unknown environments is an essential requirement for vision navigation of robotic vehicles in intelligent transportation systems. However, moving objects in dynamic scenarios usually bring about great difficulty for robot localization, because motion estimation of robotic vehicles is disturbed by increasing feature outliers caused by moving objects. In order to improve the accuracy and robustness of robot localization, a novel saliency-induced moving object detection (SMOD) approach is proposed to filter out feature outliers for RGB-D-based simultaneous localization and mapping (SLAM) in complex dynamic workspaces. Firstly, three complementary motion saliency potentials, including motion energy (ME), spatiotemporal objectness (STO), and dynamic superpixels (DS), are modeled by fully analyzing spatial, temporal, appearance and depth cues in RGB-D inputs. They can be used to identify the dynamic objects effectively from diversely changing backgrounds. Then, a superpixel-level graph-based motion saliency (MS) measure is proposed to generate the MS map for reliable localization of the moving objects. The edge weights and background nodes on the graph are determined reasonably by fusing ME, STO, and DS, which is not vulnerable to background interferences. Furthermore, the SMOD approach is embedded into the front-end of ORB-SLAM3 as a pre-processing stage, in order to filter out feature outliers associated with the moving objects. Finally, the extensive experiments are performed to verify the accuracy and robustness of the proposed approach on the public dynamic datasets. The experimental results show that the SMOD method can detect the moving objects effectively in a variety of challenging dynamic environments, and separate the dynamic regions reliably from the irrelevant background. The data comparison demonstrates that the SMOD-SLAM navigation system can outperform other state-of-the-art dynamic visual SLAM (vSLAM) systems.

Dynamic human systems risk prognosis and control of lifting operations during prefabricated building construction

Prefabricated building construction (PBC) involves tedious lifting operations that require multiple cranes to work simultaneously in dynamic workspaces. Such operations involve frequent interactions among human, cyber, and physical environments, creating challenges for risk prognosis and control in dynamic contexts. Unfortunately, human errors pose challenges for achieving resilient lifting operation control. A dynamic human systems risk prognosis and control (DHS RP & C) approach is thus necessary for 1) capturing human errors and 2) controlling risks of human anomalies proactively. This study critically reviews opportunities and challenges for establishing the proposed approach. Challenges exist as 1) how to collect human/team behavior data during lifting operations, 2) how to analyze these data for comprehending the impacts of human factors, and 3) how to respond to operational contingencies with risk control measures. In the end, the authors established a research roadmap for guiding future research activities toward automated lifting operations in PBC.

Dyno-Kinematic Leg Design for High Energy Robotic Locomotion

Abstract Animal legs are capable of a tremendous breadth of distinct dynamic behaviors. As robots pursue this same degree of flexibility in their behavioral repertoire, the design of the power transition mechanism from joint to operational space (the leg) becomes increasingly significant given the limitations current actuator technology. To address the challenges of designing legs capable of meeting the competing requirements of various dynamic behaviors, this paper proposes a technique which prioritizes explicitly encoding a set of dynamics into a robot’s leg design, called dyno-kinematic leg design (DKLD). This paper also augments the design technique with a method of evaluating the suitability of an individual leg’s workspace to perform dynamic behaviors, called the effective dynamic workspace (EDW). These concepts are shown to effectively determine optimal leg designs within a set of three, increasingly complex, case studies on different robots. These new legs designs enable a 5 kg robot to climb vertical surfaces at 3 Hz, allow a 60 kg robot to efficiently perform a range of behaviors useful for navigation (including a run at 2 m/s), and endow a small quadrupedal robot with all of the necessary behaviors to produce running and climbing multimodality. This design methodology proves robust enough to determine advantageous legs for a diverse range of dynamic requirements, leg morphologies, and cost functions, therefore demonstrating its possible application to many legged robotic platforms.

The Assistant Personal Robot Project: From the APR-01 to the APR-02 Mobile Robot Prototypes

This paper describes the evolution of the Assistant Personal Robot (APR) project developed at the Robotics Laboratory of the University of Lleida, Spain. This paper describes the first APR-01 prototype developed, the basic hardware improvement, the specific anthropomorphic improvements, and the preference surveys conducted with engineering students from the same university in order to maximize the perceived affinity with the final APR-02 mobile robot prototype. The anthropomorphic improvements have covered the design of the arms, the implementation of the arm and symbolic hand, the selection of a face for the mobile robot, the selection of a neutral facial expression, the selection of an animation for the mouth, the application of proximity feedback, the application of gaze feedback, the use of arm gestures, the selection of the motion planning strategy, and the selection of the nominal translational velocity. The final conclusion is that the development of preference surveys during the implementation of the APR-02 prototype has greatly influenced its evolution and has contributed to increase the perceived affinity and social acceptability of the prototype, which is now ready to develop assistance applications in dynamic workspaces.

Innovative safety zoning for collaborative robots utilizing Kinect and LiDAR sensory approaches

Safe collaboration between a robotic and human agent is an important challenge yet to be fully overcome in a manufacturing set-up. Existing strategies, including safety zoning are sub-optimal since they seldom fully exploit the capabilities of the collaborative robot (for repetitive tasks) and highly cognitive tasks (best suited for the operator). The recently released ISO 15066 standard for collaborative robots proposes varying safeguards, including force, speed and distance limiting functions. The latter is particularly attractive as it allows the robotic agent to adapt its operating behaviour in proximity of the operator and in instances likely to lead to safety hazards. This paper discusses strategies explored for implementing dynamic zoning in shared workspaces, considering the input speed/force of the robot as dependent on the distance between human and robot. Two main strategies were modelled, for implementing zoning. The first strategy explored integrating a LiDAR sensor, and utilising LiDAR data to dynamically map the separation distances between the operator and robotic agent. The second strategy explores an experimental setup utilising the Microsoft Kinect V2 sensor for capturing 3D point clouds, and in turn, detecting objects/agents and the proximity distance. In both instances, objects/agents were detected up to a separation distance threshold, considering error sensitivity below values of 0.1 meters. Both use cases were demonstrated using a Yumi robot and form the basis of future work towards dynamic workspace zoning.

Computational design and workspace analysis of a passive motion-scaling mechanism based on pantograph for microsurgery

Abstract Robot-assisted surgery has been extensively applied in various microsurgical disciplines owing to its enhanced accuracy and dexterity based on the motion-scaling function between a hand and a surgical instrument. However, the surgical robot system developed thus far incurs high manufacturing costs and restricted compatibility due to the complicated control system, including many actuators and sensors. This paper proposes a novel passive mechanism for motion scaling based on the pantograph structure to resolve these drawbacks of robotic assistance devices. As a first step, a design configuration featuring gravity compensation and the duplication of directional motion is suggested. Subsequently, the geometric dimensions required to satisfy the surgical space and structural constraints are defined. Moreover, the mass of elements required to enable gravity compensation is calculated using moment equations. After determining the principal design parameters, the motion scaling of the proposed passive model is identified using a three-dimensional computer-aided design. In addition, for an extremely precise operation in microsurgery, the dynamic reaction force is expected to be constant in any location. Therefore, to investigate dynamic dexterity, the mathematical model is formulated based on the Lagrangian dynamic equation. Then, the dynamic workspace range is analysed from the determinant of the mass matrix. In addition, the analysis results are evaluated through comparative simulations in different workspace ranges; hence, a constant reaction force can be achieved only in the dynamic workspace range.

Toward energy-efficient online Complete Coverage Path Planning of a ship hull maintenance robot based on Glasius Bio-inspired Neural Network

Regular Ship hull maintenance is an essential for sustainability. The maintenance work of ship hulls that involve human labor suffers from many shortcomings. Maintenance robots have been introduced for drydocks to eliminate these shortcomings. An energy-efficient Complete Coverage Path Planning (CCPP) is a crucial requirement from a ship hull maintenance robot. This paper proposes a novel energy-efficient CCPP method based on Glasius Bioinspired Neural Network (GBNN) for a ship hull inspection robot. The proposed method accounts for a comprehensive energy model for path planning. This energy model reflects the energy usage of a ship hull maintenance robot due to changes in direction, distance, and vertical position. Furthermore, the proposed method is effective for dynamic workspaces since it performs online path planning. These are the major contributions made to state of the art by the work proposed in this paper. The behavior and the performance of the proposed method have been compared against state of the art through simulations considering Hornbill, a multipurpose ship hull maintenance robot. The validation confirms the ability of the proposed in realizing a complete coverage of a given dynamic workspace. According to the statistical outcomes of the comparison, the performance of the proposed method significantly surpasses that of the state-of-the-art methods in terms of energy usage. Therefore, the proposed method contributes to the development of energy-efficient CCPP methods for a ship hull maintenance robot.

Quantitative Performance Review of Wheeled Mobile Robot Path Planning Algorithms

Path planning evaluates and identifies an obstacle free path for a wheeled mobile robot (WMR) to traverse within its workspace. It emphasizes metric like, start and goal coordinate, static or dynamic workspace, static or dynamic obstacles, computational time and local minimum problem. Path planning play a significant role toward WMR effective traverse within it workspace like industrial, military, hospital, school and office. In this workspace, path planning is an optimal method to increase the productivity of WMR to achieve it specific task. Hence, in this paper, we present a review of path planning algorithms (classical algorithms, heuristics and intelligent algorithms, and machine learning algorithm) for mobile robot using statistical method. Regarding our objective, we use this statistical method to evaluate the success of these algorithms base on the following metrics: architecture (hybrid or standalone), algorithm sub-category (global or local or combine), workspace (static or dynamic), obstacle type (static or dynamic), number of obstacle (≤ 2, ≤ 5, > 5) and test workspace (virtual or real-world). Research materials are sourced from recognized databases where relevant research articles are obtained and analyzed. Result shows hybrid of machine learning approach with heuristic and intelligent algorithm has superior performance where they are applied compare to other hybrid. Also, in complex workspace Q-learning algorithm outperforms other algorithms. To conclude future research is discussed to provide reference for hybrid of Q-learning algorithm with Cuckoo Search, Shuffled Frog Leaping and Artificial Bee Colony algorithm to improve its performance in complex workspace.

Game-Theoretic Strategic Coordination and Navigation of Multiple Wheeled Robots

Multiple robots negotiating in a dynamic workspace may lead to collisions. To avoid such issues, multi-robot navigation and coordination becomes necessary but is computationally very challenging, particularly when there are many robots. This article addresses the problem of multi-robot navigation where individual robots require coordination. Although a few such attempts for modeling multi-robot coordination and navigation have been studied, this work proposes a game-theoretic coordination strategy, also referred to as strategic coordination. We make use of a genetic algorithm tuned fuzzy logic–based motion planner. The proposed strategic coordination strategy has been pitted against a basic potential field-based motion planner, also referred to as the heuristic method, for performance comparison. Results are compared through computer simulation with 8 to 17 robots at different rounds. From the obtained results, it was observed that the proposed coordination scheme’s efficacy is strong for a larger number of robots. In addition, the proposed strategic coordination scheme with the genetic-fuzzy-based motion planner was found to outperform other combinations as far as the quality of solutions and time to reach the goal positions. The computational complexity of different methods has also been compared and presented.

Self-protective motion planning for mobile manipulators in a dynamic door-closing workspace

PurposeMany work conditions require manipulators to open cabinet doors and then gain access to the desired workspace. However, after opening, the unlocked doors can easily close, interrupt a task and potentially break the operating end-effectors. This paper aims to address a manipulator's behavior planning problem for responding to a dynamic workspace released by door opening.Design/methodology/approachA dynamic model of the restricted workspace released by an unlocked door is established. As a whole system to treat, the interactions between the workspace and robot are analyzed by using a partially observable Markov decision process. A self-protective policy decision executed as a belief tree is proposed. To respond to the policy, this study has designed three types of actions: stay on guard in the workspace, using an elbow joint to defense the door and linear escape out of the workspace for self-protection by observing collision risk levels to trigger them. Finally, this study proposes self-protective motion controllers based on risk time optimization to act to the planned actions.FindingsThe elbow defense could balance robotic safety and work efficiency by interrupting the end-effector's work and using the elbow joint to prevent the door-closing in an active collision way. Compared with the stay and escape action, the advantage of the elbow defense is having a predictable performance to quick callback the interrupted work after the risk was relieved.Originality/valueThis work provides guidance for the safe operation of a class of robot operations and the upgrade of motion planning.

Towards a balancing safety against performance approach in human–robot co-manipulation for door-closing emergencies

Telemanipulation in power stations commonly require robots first to open doors and then gain access to a new workspace. However, the opened doors can easily close by disturbances, interrupt the operations, and potentially lead to collision damages. Although existing telemanipulation is a highly efficient master–slave work pattern due to human-in-the-loop control, it is not trivial for a user to specify the optimal measures to guarantee safety. This paper investigates the safety-critical motion planning and control problem to balance robotic safety against manipulation performance during work emergencies. Based on a dynamic workspace released by door-closing, the interactions between the workspace and robot are analyzed using a partially observable Markov decision process, thereby making the balance mechanism executed as belief tree planning. To act the planning, apart from telemanipulation actions, we clarify other three safety-guaranteed actions: on guard, defense and escape for self-protection by estimating collision risk levels to trigger them. Besides, our experiments show that the proposed method is capable of determining multiple solutions for balancing robotic safety and work efficiency during telemanipulation tasks.

Path Planning and Obstacles Avoidance in Dynamic Workspace Using Polygon Shape Tangents Algorithm

This paper presents the design of a path planning system in an environment that contains a set of static and dynamic polygon obstacles localized randomly. In this paper, an algorithm so-called (Polygon shape tangents algorithm) is proposed to move a mobile robot from a source point to a destination point with no collision with surrounding obstacles using the visibility binary tree algorithm. The methodology of this algorithm is based on predicting the steps of a robot trajectory from the source to the destination point. The polygon shapes tangent algorithm is compared with the virtual circles' tangents algorithm for different numbers of static and dynamic polygon obstacles for the time of arrival and the length of the path to the target. The obtained result shows that the used algorithm has better performance than the other algorithms and gets less time of arrival and shortest path with free collision.

Design and Experimental Validation of a 3-DOF Underactuated Pendulum-Like Robot

This article presents the design and the experimental validation of a novel 3-degree-of-freedom (DOF) pendulum-like cable-driven robot capable of executing point-to-point motions by leveraging partial feedback linearization control and on-line trajectory planning based on adaptive frequency oscillators (AFOs). Unlike most cable-suspended parallel robots, which rely on at least n actuated cables to control n DOF, the proposed robot is capable of performing 3-DOF point-to-point motions, from a starting pose to a goal one within its dynamic workspace, by means of two actuators only. Feedback linearization allows the dynamics of the variable-length pendulum to be decoupled from the orientation of the end effector, enabling the device to use parametric excitation to control the oscillations of the variable-length pendulum, akin to a playground swing. A pool of AFOs is introduced to enable smooth, lag free on-line estimations of the current phase of the pendulum oscillation to inform the on-line planner and the parametric excitation controller. Experimental results demonstrate feasibility of the proposed design and control approach.

Optimal Design and Force Control of a Nine-Cable-Driven Parallel Mechanism for Lunar Takeoff Simulation

Traditional simulation methods are unable to meet the requirements of lunar takeoff simulations, such as high force output precision, low cost, and repeated use. Considering that cable-driven parallel mechanisms have the advantages of high payload to weight ratio, potentially large workspace, and high-speed motion, these mechanisms have the potential to be used for lunar takeoff simulations. Thus, this paper presents a parallel mechanism driven by nine cables. The purpose of this study is to optimize the dimensions of the cable-driven parallel mechanism to meet dynamic workspace requirements under cable tension constraints. The dynamic workspace requirements are derived from the kinematical function requests of the lunar takeoff simulation equipment. Experimental design and response surface methods are adopted for building the surrogate mathematical model linking the optimal variables and the optimization indices. A set of dimensional parameters are determined by analyzing the surrogate mathematical model. The volume of the dynamic workspace increased by 46% after optimization. Besides, a force control method is proposed for calculating output vector and sinusoidal forces. A force control loop is introduced into the traditional position control loop to adjust the cable force precisely, while controlling the cable length. The effectiveness of the proposed control method is verified through experiments. A 5% vector output accuracy and 12 Hz undulation force output can be realized. This paper proposes a cable-driven parallel mechanism which can be used for lunar takeoff simulation.

Vision-based detection and visualization of dynamic workspaces

Abstract Aligning workers and groups with workspaces in advance to enable peak performance and ensure safety is the essence of workspace planning. There are plans, while there have been few methods for examining these plans. We argue that capturing and visualizing actual dynamic workspaces is fundamental to plan examination. This paper describes an initial effort on integrating the latest computer vision methods to implement automatic detection and visualization of dynamic workspaces of workers on foot. To this end, object detection, multiple object tracking, and action recognition are adopted to collect two types of action data: action classes and action locations. A density-based spatial clustering algorithm is used to reason dynamic workspaces based on the action data. We evaluated each method of the integrated system and found that they have achieved the comparable performance in our envisaged settings with the original methods in their general settings. We also presented two demonstrations of the system. The research results represent an initial step towards developing a new management capability by capturing dynamic workspaces.