Human-Robot Collaboration (HRC) is increasingly common in physically demanding industrial settings. However, the impact of variable human states, such as physical fatigue, on trust and the quality of this collaboration remains unclear. This is especially critical during HRC that entails co-lifting of industrial materials. This study examines the impact of operator physical fatigue on trust in a robotic partner, as well as associated perceptions of robot effort, coordination, lift quality, and objective lift performance during a co-lifting task that requires complex maneuvering. In a within-subjects experiment, 40 healthy adults (20 females) performed a collaborative lifting task through an asymmetrical complex lift trajectory under two conditions: no-fatigue and fatigue, induced via physical exertion of the dominant arm. We collected subjective ratings of trust, perceived exertion, lift quality, robot effort, and coordination, alongside objective performance metrics based on movement trajectories, such as similarity (using dynamic time warping), acceleration, and jerks. The results demonstrate that physical fatigue, as evidenced by increased perceived exertion ratings, significantly decreased participants' trust in the robot (particularly in the late phase), despite no change in the robot's actual performance. Interestingly, the impacts of fatigue on perceptions of effective coordination were dynamically impacted by the early (higher under fatigue) versus the late (lower under fatigue) phases; participants viewed the robot's effort as less consistent only in the late phase when fatigued. Surprisingly, while fatigue did not affect any of the objective performance metrics based on movement trajectories, participants perceived an improvement in transport quality; however, this was only found in the early phase. These findings indicate that physical fatigue alters not only the physical capacity of the operator but also crucial perceptual shifts of the robot partner and perceived transport quality. This highlights the need for more objective metrics to evaluate HRC that are resilient to operator fatigue. Doing so may enable the development of robust HRC systems that ensure effective and trustworthy collaborations.

This work develops a control-oriented hybrid modeling framework for concrete three-dimensional (3D) printing that couples first-principles pump dynamics with machine-learning (ML) estimators of fresh slurry density ( ρ ) and viscosity ( μ ) to dynamically predict the slurry mass flow rate ( M ̇ ). A physics-guided feature set is screened using correlation, mutual information, and permutation importance and trained under grouped cross-validation across mixes (36 nominal mixes; 97 density and 195 rheology points). In the density predictor pipeline, a non-negative least-squares (NNLS)-stacked ensemble using Ridge, Extra Trees (ExT), and Random Forest (RF) outperforms single learners (coefficient of determination ( R 2 ) ≈ 0.73 ). For viscosity, high-capacity learners dominate; the best performance is achieved by a ridge-meta stack trained over the full base set ( R 2 ≈ 0.83 ). Unlike prior screw-extrusion flow-control work, validated on a single rig without a systematic sweep of mix compositions and reporting mean absolute error (MAE) of ≈ 6.7 % , this study targets progressive-cavity pumping and learns ρ and μ from a multi-mix dataset for dynamic, control-oriented flow prediction. Embedding the learned ρ and μ maps within the pump model yields simulated flow-rate curves that closely follow laboratory measurements across water-accelerator settings and motor speeds, with MAE on the order of 1–2% of mean flow. The predictor is interpretable and low-latency (milliseconds per evaluation) and integrates with Simulink for feedforward planning, soft sensing, and closed-loop use. The workflow provides a reproducible template, from data preparation and feature screening to model training and deployment, for hybrid modeling in large-format additive construction.

Trust in human–robot collaboration (HRC) is influenced by situational factors, such as human states of fatigue and robot performance (e.g., reliability). In industrial settings, operators are often fatigued which increases attentional demands. This sustained mental demand may potentially alter the trusting behaviors of the operator. Whether this happens, and why, are currently unexplored. While most studies have relied on self-reports, recent advancements in neuroergonomics have enabled the assessment of neural signatures of trust. We hypothesize that shedding light on the neural mechanisms during HRC, under varying trust states, may provide additional insights (beyond trust perceptions) into the subconscious cognitive or affective states associated with trust under cognitive fatigue. To test this hypothesis, we conducted an HRC experiment on 16 sex-balanced participants in which trust was manipulated via robot reliability at two sessions (fatigue session and no-fatigue session). We monitored brain activity, heart rate variability (HRV), performances, and subjective experiences to investigate the effect of cognitive fatigue and robot reliability on trust metrics in HRC. A significant main effect of cognitive fatigue and reliability was observed on brain activations, functional and effective connectivity, HRV, subjective response, and performance. While most measures were sensitive to changes in cognitive fatigue and reliability, only neural responses revealed interactions between reliability, cognitive fatigue, and sex. Anterior prefrontal cortex (APFC)–left dorsolateral prefrontal cortex (LDLPFC) connection strength decreased with unreliable robot behavior in non-fatigued states but unexpectedly increased when fatigue was present. This suggests a neural strategy shift involving greater top-down influence from executive to motivational regions to exert more mental effort in challenging HRC conditions. Insights from this investigation can advance objective trust measures under cognitive fatigue states and help design better collaborations.

In this paper, we investigate the selection of time headway to ensure robust string stability in connected and autonomous vehicle platoons in the presence of signal noise in Vehicle-to-Vehicle (V2V) communication. In particular, we consider the effect of noise in communicated vehicle acceleration from the predecessor vehicle to the follower vehicle on the selection of the time headway in predecessor-follower type vehicle platooning with a Constant Time Headway Policy (CTHP). Employing a CTHP based control law for each vehicle that utilizes onboard sensors for measurement of position and velocity of the predecessor vehicle and wireless communication network for obtaining the acceleration of the predecessor vehicle, we investigate how the implementable time headway is affected by communicated signal noise. We derive constraints on the CTHP controller gains for predecessor acceleration, velocity error and spacing error and a lower bound on the time headway which will ensure robust string stability of the platoon against signal noise. We perform comparative numerical simulations on an example to illustrate the main results.

In this paper, we provide a systematic procedure for designing cooperative adaptive cruise control (CACC) systems that are robust to parasitic actuation lag in braking and propulsion and delay in the communicated acceleration from the predecessor vehicle. In particular, we derive a tight lower bound on the employable time headway in CACC systems for guaranteeing robust string stability. The lower bound on the time headway is dependent on the upper bound on the parasitic actuation lag (τ0) and the communication delay (ℓ). The main result of the paper is that if τ0exceeds ℓ, then the employable time headway is lower bounded by τ0+ℓ. Otherwise, it is better to use adaptive cruise control (ACC) where the employable time headway is lower bounded by 2τ0. The above results on CACC systems are then extended to next-generation CACC (CACC+) systems that employ information from multiple predecessor vehicles. Several comparative numerical simulations for a representative maneuver corroborate the main results.

Ensuring safety is the most important factor in connected and autonomous vehicles, especially in emergency braking situations. As such, assessing the safety benefits of one information topology over other is a necessary step towards evaluating and ensuring safety. In this paper, we compare the safety benefits of a cooperative adaptive cruise control which utilizes information from one predecessor vehicle (CACC) with the one that utilizes information from multiple predecessors (CACC+) for the maintenance of spacing under an emergency braking scenario. A constant time headway policy is employed for maintenance of spacing (that includes a desired standstill spacing distance and a velocity dependent spacing distance) between the vehicles in the platoon. The considered emergency braking scenario consists of braking of the leader vehicle of the platoon at its maximum deceleration and that of the following vehicles to maintain the spacing as per CACC or CACC+. By focusing on the standstill spacing distance and utilizing Monte Carlo simulations, we assess the safety benefits of CACC+ over CACC by utilizing the following safety metrics: (1) probability of collision, (2) expected number of collisions, and (3) severity of collision (defined as the relative velocity of the two vehicles at impact). We present and provide discussion of these results.

Anomalous robot motions caused by cyber attacks and inherent defects can lead to task failures as well as harmful accidents in collaborative human-robot workplaces. External Internet-of-Things (IoT) sensors are essential for reliably monitoring robot tool paths and detecting deviations as early as possible, especially in anomalous situations where the robot system and its internal sensors may not function as expected. However, affordable external IoT sensors may suffer from poor-quality measurements, necessitating data-driven algorithmic enhancements. In addition to external sensors providing independent monitoring, to effectively detect trajectory deviations, we need anomaly detection methods that can capture complex dynamic patterns in the nonlinear trajectory time-series data. In this work, we propose a framework to accurately estimate robot trajectories during normal robot operations as well as to detect various types of anomalous trajectory changes using an external camera. The proposed framework efficiently incorporates marker-based pose estimation, Long Short-Term Memory (LSTM), and residual control charts. The framework was evaluated in a shared human-robot assembly task. The results show that we can accurately estimate robot trajectories by enhancing camera-based measurements. Moreover, it effectively detects anomalous trajectory changes in their early stages. The motion deviation upon detection assists in determining a safe working distance. The framework is also generalizable to previously unseen trajectory deviations and allows the use of a variety of IoT sensors.

Trust in human–robot collaboration is an essential consideration that relates to operator performance, utilization, and experience. While trust’s importance is understood, the state-of-the-art methods to study trust in automation, like surveys, drastically limit the types of insights that can be made. Improvements in measuring techniques can provide a granular understanding of influencers like robot reliability and their subsequent impact on human behavior and experience. This investigation quantifies the brain–behavior relationships associated with trust manipulation in shared space human–robot collaboration to advance the scope of metrics to study trust. Thirty-eight participants, balanced by sex, were recruited to perform an assembly task with a collaborative robot under reliable and unreliable robot conditions. Brain imaging, psychological and behavioral eye-tracking, quantitative and qualitative performance, and subjective experiences were monitored. Results from this investigation identify specific information processing and cognitive strategies that result in identified trust-related behaviors that were found to be sex specific. The use of covert measurements of trust can reveal insights that humans cannot consciously report, thus shedding light on processes systematically overlooked by subjective measures. Our findings connect a trust influencer (robot reliability) to upstream cognition and downstream human behavior and are enabled by the utilization of granular metrics.

This paper presents the development and validation of a robot adaptation model to support human operators in Human-Robot Collaborative tasks when they are cognitively fatigued. A human-centered robot adaptation method for providing appropriate assistance to the operator is developed with a dual objective of task performance optimization and aiding human cognitive fatigue recovery. The problem is formulated as a Markov Decision Process (MDP) and solved using Q-learning. The implementation issues resulting from modeling the MDP and performing Q-learning for cognitive fatigue recovery, methods to mitigate those issues, and implications on the resulting optimal policies are discussed. The proposed approach is evaluated through a user study of sixteen participants performing a robotic surface polishing task under cognitive fatigue conditions. The MDP model is validated using subjective metrics (fatigue perception surveys) and objective metrics (Heart-Rate Variability (HRV), accuracy in trajectory tracking, and time efficiency of the task). Fatigue perceptions, accuracy, and time efficiency improved during the user-specific optimal adaptation policies. HRV analysis of time-domain features shows an overall improvement in fatigue conditions during the optimal adaptation policies. The results from this approach indicate that such human-centered robot adaptation can lead to efficient human-robot collaborations with robust interactions between robots and humans.

In this paper, we develop a novel path-constrained and collision-free optimal trajectory planning algorithm for robot manipulators in the presence of obstacles for the following problem: Given a desired sequence of discrete waypoints of robot configurations, a set of robot kinematic and dynamic constraints, and a set of obstacles, determine a time and jerk optimal and collision-free trajectory for the robot passing through the given waypoints. Our approach in developing the robot path through the waypoints relies on the orthogonal collocation method where the states are represented with Legendre polynomials in the Barycentric form; the transcription process efficiently converts the continuous-time formulation of the optimal control problem into a discrete non-linear program. In addition, we provide an efficient method for avoiding robot self-collisions (of joints and links) and collisions with workspace obstacles by modeling them as the union of spheres and cylinders in the workspace. The resulting collision free optimal trajectory provides smooth and constrained motion for the robot passing through all the waypoints in the given prescribed sequence with a constant speed. The proposed method is validated using numerical simulations and experiments on a six degree-of-freedom robot. Note to Practitioners—This paper is motivated by planning collision-free optimal trajectories with constant Cartesian speed (norm of translation velocity) along a given list of waypoints. The primary applications include developing constant Cartesian speed trajectories for robotic surface finishing operations, spray painting operations and robot endurance testing. Sampling-based motion planning algorithms have been widely used for their high efficiency and robustness. However, those methods in general do not take into account the joint level constraints, motion jerk, robot dynamic model and kinematic constraints together. In addition, with these algorithms, it is difficult to generate a trajectory along a list of waypoints while maintaining a constant Cartesian speed. We provide an efficient robot trajectory planning algorithm for articulated robots that is capable of achieving time and jerk optimality while avoiding obstacles and satisfying robot kinematic and dynamic constraints. The scope of this work is limited to considering only static obstacles and pre-defined Cartesian waypoints; potential extensions include consideration of dynamic obstacles and incorporating tighter bounds for objects modeled by cylinders and spheres so that a larger workspace is available for trajectory planning, etc.

In this paper, we develop a novel path-constrained and collision-free optimal trajectory planning algorithm for robot manipulators in the presence of obstacles for the following problem: Given a desired sequence of discrete waypoints of robot configurations, a set of robot kinematic and dynamic constraints, and a set of obstacles, determine a time and jerk optimal and collision-free trajectory for the robot passing through the given waypoints with constant speed. Our approach in developing the robot path through the waypoints relies on the orthogonal collocation method where the states are represented with Legendre polynomials in the Barycentric form; the transcription process efficiently converts the continuous-time formulation of the optimal control problem (both time and jerk optimal) into a discrete non-linear program. In addition, we provide an efficient method for avoiding robot self-collisions (of joints and links) and collisions with workspace obstacles by modeling them as the union of spheres and cylinders in the workspace. The resulting collision free optimal trajectory provides smooth and constrained motion for the robot passing through all the waypoints in the given prescribed sequence. The proposed method is validated using numerical simulations and experiments on a six degree-of-freedom robot.

This paper considers the problem of asynchronous H∞control for networked Markovian jump systems subject to probabilistic packet dropouts and communication delays in the measurement channel. A new dynamic output-feedback-based asynchronous controller is proposed wherein the dynamic output-feedback controller modes need not synchronize with the system modes. By utilizing results from stochastic Lyapunov-Krasovskii stability theory, sufficient conditions in terms of matrix inequalities are derived such that the closed-loop networked Markovian jump system is stochastically stable and achieves the prescribed H∞performance. Using the Schur complement technique and under the assumption that the input matrix is full rank, the sufficient condition is reduced to a linear matrix inequality and the dynamic output-feedback-based asynchronous controller is synthesized. A detailed numerical example with simulation results are presented to evaluate the proposed controller design scheme.

Surface finishing of freeform surfaces is predominately a manual operation that requires a considerable amount of operator skill; automation of this process has many benefits, including consistent surface quality, preventing hazardous exposure to particulate, etc. A novel robotic surface finishing system, consisting of a robot and an end-effector that includes a force sensor, finishing tool, and proximity laser sensor, is developed in this paper to automate the surface finishing process. The laser sensor is treated as an additional link, and based on it a novel perception system is developed for real-time scanning of the surface that provides the surface profile mesh and the corresponding normal vectors which can be used directly by the robot closed-loop control system for pose tracking. A unique feature of the perception system is that the geometry of the surface profile and normal vectors are all obtained in real-time in the robot base coordinate system, thus eliminating issues such as precise registration of the work piece in the fixture and its location with respect to the robot base coordinates. An impedance-type closed-loop control algorithm is developed for pose tracking. The proposed system and control algorithm are employed to conduct surface finishing experiments on wooden surfaces. A representative sample of the results and measurement images of surface finish are provided to illustrate the capabilities of the robotic surface finishing system. A video of the system in operation is also provided.

In this paper, we investigate the benefits of vehicle-to-vehicle (V2V) communication for autonomous vehicles and provide results on how V2V information helps reduce employable time headway in the presence of parasitic lags. For a string of vehicles adopting a constant time headway policy and availing the on-board information of predecessor's vehicle position and velocity, the minimum employable time headway (hmin) must be lower bounded by 2τ0for string stability, where τ0is the maximum parasitic actuation lag. In this paper, we quantify the benefits of using V2V communication in terms of a reduction in the employable time headway: 1) If the position and velocity information of r immediately preceding vehicles is used, then hmin can be reduced to 4τ0/(1 + r); 2) furthermore, if the acceleration of `r' immediately preceding vehicles is used, then hmincan be reduced to 2τ0/(1 + r); and 3) if the position, velocity, and acceleration of the immediate and the r-th predecessors are used, then hmin≥ 2τ0/(1 + r). Note that cases (2) and (3) provide the same lower bound on the minimum employable time headway; however, case (3) requires much less communicated information.

This paper proposes a two-channel periodic event-triggered observed-based repetitive control strategy to track a periodic reference signal subject to limited communication capacity. Under the designed two-channel periodic event-triggering framework, within any two consecutive event-triggering instants in each event-triggering channel, not only the output measurements to the state observer but also the observer states to the repetitive controller structure are kept unchanged via a zero-order hold (ZOH) which can substantially alleviate the communication burden when the output measurements and the observer states do not change significantly. By employing the input delay approach, the overall system consisting of the physical plant, the state observer, the repetitive controller, and the two-channel periodic event-triggering mechanisms is modeled as a closed-loop time-varying delay system. Sufficient conditions in terms of linear matrix inequalities are derived to ensure the closed-loop system to be asymptotically stable with a prescribed H∞attenuation performance level for an exogenous disturbance input. The controller gains, observer gains, and the event-triggering parameters are synthesized by using a matrix decomposition technique. A numerical example is provided to evaluate the proposed design approach.

Combining the benefits of robust situational awareness of human operators with the efficiency and precision of automatic control has been an important topic of human-machine shared control. The emphasis is on keeping human operators in the loop while automatic control providing assistance to improve task performance. Given a task with specific subgoals, execution of a task using blended shared control involves predicting the operator's intent of subgoal transitions and deciding the blending weights for inputs from the human operator and automatic control. In this paper we address the problem of subgoal adjustment in blended shared control which is typically initiated by the operator's intent and necessary to sustain the shared control performance for changing subgoal conditions. First, we provide a method to predict operator's intent of visiting a subgoal. Based on intent prediction, we propose a method for subgoal adjustment where the adjustment is encoded by a hyperrectangle. The volume of the hyper-rectangle is obtained by using a hyperbolic slope transition function which is based on the distance between subgoals. The adjustment actions within the hyper-rectangle are facilitated by a skill-weighted action integral that takes into consideration the skill level of the operator. The approach is tested on a scaled hydraulic excavator platform with multiple novice operators and a skilled operator. Experimental results are presented and discussed.

Optical mouse sensors have been utilized recently to measure position of mobile robots. This work provides a systematic solution to the problem of locating N optical mouse sensors on a mobile robot with the aim of increasing the quality of the measurement. The developed analysis gives insights on how the selection of a particular configuration reflects on the quality of the measurement signal, and it allows to compare the effectiveness of different configurations. The set of all the optimal configurations is parameterized into two constraints. The results are derived from the analysis of the singular values of a particular matrix obtained by solving the sensor kinematics problem. Moreover, given any mobile robot platform, an end-user procedure is provided to select the best location for N optical mouse sensors on such a platform. The procedure consists of solving a feasible constrained optimization problem.

The research investigates the design and performance capabilities of a voice-coil motor actuator (VCMA) for a disc drive with a nonlinear magnetic bias. A VCMA is designed to meet specific seek performance requirements. A nonlinear magnetic bias is independently designed to compensate for non-operational shock. A model-based adaptive controller was developed to meet the seek performance requirements and simultaneously handle effects of the nonlinear bias. The performance criteria was based on minimizing the time required for a seek maneuver without controller saturation. The proposed adaptive controller was compared to an often used linear state-feedback controller with the magnetic bias present. Simulations were conducted to verify the results.

This paper deals with friction compensation techniques for accuracy trajectory tracking control of robots. We extend the traditional model-based adaptive control algorithms that are prevalent in the robotics literature with adaptive friction compensation. Adaptive controllers that consider both static and dynamic friction effects in the robot dynamics are proposed. Stability of the proposed adaptive controller with friction compensation is shown. An extensive experimental study has been done to illustrate the importance of friction compensation in trajectory tracking. The tracking performance of the proposed adaptive controller utilizing the dynamic friction models for compensation is far superior to the conventional adaptive controller without friction compensation. Using the data collected for a series of experiments we compare the proposed adaptive controllers with static and dynamic friction compensation with two well known robot control algorithms, i.e., model-based computed-torque control and adaptive control. A representative sample of the experimental data is shown and discussed.

Provides an experimental study of planar impact of a robot manipulator on a surface. Using the data collected from a series of experiments, it investigates post-impact behavior for different pre-impact conditions such as configuration of the robot, angle and velocity of impact, etc. Understanding the post-impact behavior for various pre-impact conditions can result in the design of improved transition control strategies to stabilize the manipulator onto the surface. Potential applications of this study include robotic surface finishing operations such as polishing, chamfering, deburring, and grinding, etc.

This paper addresses the problem of contact transition from free motion to constrained motion for geometrically constrained robots. Constraint uncertainty can cause the robot to impact the surface with a non-zero velocity. To deal with contact transition. A new stable discontinuous transition controller is proposed. The control algorithm for a complete robot task that involves both free motion and constrained motion is also developed. Extensive experiments with the proposed control strategy were conducted with different levels of constraint uncertainty. Experimental results show much improved transition performance and force regulation. Details of the experimental platform and typical experimental results are given.

In this work, we consider adaptive control of time-varying mechanical systems. First, we derive the dynamics of time-varying mechanical systems under the assumption that the generalized constraints on the system do not depend on time but the system parameters such as masses and payloads are time-varying. A compact parameterization of the dynamics of the system is obtained based on the rate of change of parameters being a polynomial in time. Based on this parameterization, adaptation laws for the time-varying parameters is proposed. To test the proposed adaptive controllers, an experimental platform consisting of a two-link robot with a time-varying payload is designed. The designed experimental platform mimics pouring/filling operations in industry, where the payload is time-varying. Experimental results demonstrate the effectiveness of the proposed adaptive control designs.