This paper introduces an advanced Quaternion-based Unscented Kalman Filter (QUKF) for real-time, robust estimation of system states and external wrenches in assistive aerial payload transportation systems that engage in direct physical interaction. Unlike conventional filtering techniques, the proposed approach employs a unit-quaternion representation to inherently avoid singularities and ensure globally consistent, drift-free estimation of the platform’s pose and interaction wrenches. A rigorous quaternion-based dynamic model is formulated to capture coupled translational and rotational dynamics under interaction forces. Building on this model, a comprehensive QUKF framework is established for state prediction, measurement updates, and external wrench estimation. The proposed formulation fully preserves the nonlinear characteristics of rotational motion, enabling more accurate and numerically stable estimation during physical interaction compared to linearized filtering schemes. Extensive simulations validate the effectiveness of the QUKF, showing significant improvements over the Extended Kalman Filter (EKF). Specifically, the QUKF achieved a 79.41% reduction in Root Mean Squared Error (RMSE) for torque estimation, with average RMSE improvements of 79% and 56%, for position and angular rates, respectively. These findings demonstrate enhanced robustness to measurement noise and modeling uncertainties, providing a reliable foundation for safe, stable, and responsive human-UAV physical interaction in cooperative payload transportation tasks.

This paper presents a zero-shot adaptive elbow-assist exoskeleton controller based on deep reinforcement learning (DRL), developed to generalize robustly across variable users, tasks, and environmental conditions without per-user calibration. Trained via domain-randomized neuromusculoskeletal simulation and equipped with online latent factor estimation from surface electromyography (sEMG), the controller reduces elbow flexor effort by up to 75% in realistic lifting tasks. Human-subject experiments reveal a trade-off between muscular unloading and trajectory tracking accuracy, particularly at higher speeds and amplitudes. Despite this, the controller reliably delivers user-adaptive assistance with no subject-specific tuning. This makes it highly suited for time-sensitive, repetitive tasks in automation contexts such as warehouses, logistics, and manufacturing. In these environments, minimizing setup time and operator fatigue are critical for productivity.

Frazing is a manual task in turbine blade manufacturing companies, in which the operators must discover and repair the defects of the workpieces. Such detection is so attention-demanding and time-consuming that the operators sometimes miss the defects, resulting in poor human performance and quality. Hence, this study aims to guide the operators with a systematic visual search using augmented reality (AR). For this purpose, an AR-based assistance system was built. To evaluate the system’s usability and effectiveness, a user study was conducted on 24 skilled operators. The results showed that the performance of the augmented operators was enhanced as the task completion time and detection misses decreased using the AR tool by 11.6 and 100 percent, respectively. The mental workload declined significantly after using the AR system. The most prominent finding of this research is that the AR tool was quite effective in reducing mental fatigue and workload, leading to improved human performance in ever-changing tasks.

This article presents a novel framework called Simultaneous Registration and Machining (SRAM), a generalized method to improve workpiece registration using real-time acquired data in robotic contouring applications. The method allows for online corrections to the toolpath, while a live covariance estimate is simultaneously leveraged to adaptively tune the force controller aggressively when uncertainty is high, but conservatively otherwise to minimize chatter and instability. The SRAM framework is validated in simulation and shown to significantly reduce the path corrections required from the force controller, while correctly predicting optimal controller tuning adaptations. The SRAM method is proposed to improve force control stability, increase peripheral accuracy, smooth surface finish, and reduce cycle times in contouring applications.

This paper proposes a novel numerical inverse kinematics algorithm called theQuick Inverse KinematicsorQuIKmethod. The QuIK method is athird-orderalgorithm that uses both the first- and second-order derivative information to iteratively converge to a solution. Numerical inverse kinematics methods are readily implemented on any serial robot and do not rely on joint alignment. However, they typically are slower and less robust. The second-order derivative term allows the QuIK algorithm to converge more rapidly and more robustly than existing algorithms. A damped extension to the QuIK method is also proposed to increase reliability near singularities. The QuIK methods are tested in terms of evaluation speed, reliability, and singularity robustness against the Newton–Raphson method and several other modern algorithms. The proposed QuIK methods outperform all other tested algorithms in terms of speed and robustness, and have strong performance near singularities. The QuIK algorithms are proposed as faster and more robust “drop-in” replacements to the Newton–Raphson methods in inverse kinematics. C++ and MATLAB codebases are made available.

The current paper proposes a control methodology for ensuring safety during human-robot interaction based on a compliant sensor covering the robot links as a lightweight shell. The method can be used with existing robots without the need for mechanical redesign. To assess the behaviour of the proposed control law, the controller is analysed using a linear robot model. Stability analysis is performed and requirements on the controller parameters are derived. The effect of the controller parameters on the perceived impedance and the maximum safe operating velocity of the robot are determined via the linear model. The adverse impact of dry friction is analysed in simulation and methods are developed to mitigate the effects. The controller is implemented on a 1 DoF robotic joint and the results are compared to those of a traditional admittance control law, demonstrating comparable transient response while maintaining a simple control structure and decreased risk of instability.

In this study, a new space time block code (STBC) is proposed for long term evolution-advanced (LTE-A) system that is equipped with three time slots and two antennas. The introduced STBC has the following characteristics: i) it achieves rate one and full diversity, ii) its maximum likelihood (ML) decoding requires a joint detection of three real symbols, iii) the minimum determinant value (MDV) does not vanish by increasing signal constellation size, and iv) bit error rate (BER) results show that the proposed STBC outperforms previously presented schemes for LTE-A at high SNRs.

A novel behavior based locomotion controller (BBLC) capable of adapting to unknown disturbances is presented. The proposed controller implements a behavior based control architecture by subdividing the walking control into several task-space controllers such as swing leg control and center of gravity (COG) position control. For each task-space controller, a number of behaviors, which plan the reference task-space trajectories, are designed based on existing stabilizing controllers or strategies inspired by human walking biomechanics. A Q-learning algorithm is used to classify which behavior combinations can compensate for specific disturbances. The controller is implemented on a planar biped simulation with push type disturbances applied on flat and sloped terrain. The results show that stabilization strategies, capable of compensating for these disturbances emerge from the combination of different task level behaviors, without a priori knowledge of the nature of the disturbances.

This paper introduces a novel assistive device (AD) control strategy that provides intermittent assistance and is capable of encouraging user effort during AD use. The assistance regulation controller (ARC) presented here is designed to regulate joint torque and can be used for arbitrary joint and limb motions without requiring any a priori information about the user's intended motion. Data from eight healthy subjects who performed a forearm tracking experiment using a single degree-of-freedom powered orthosis demonstrate that the ARC is capable of encouraging user effort. On average, subjects generated 40% more joint torque when performing the tracking experiment with the ARC instead of a conventional torque-amplifying controller. The results also demonstrate that the ARC is safe to use for dynamic orthosis-assisted joint motions which require several transitions into and out of the assistance and no assistance states of the controller. As such, the results also confirm that the ARC is an effective assist-as-needed control strategy suitable for use with ADs.

A novel controller using Center of Gravity (COG) planning based on the Centroidal Moment Pivot (CMP) criterion with Ground Reaction Force (GRF) feedback is presented. High level motion planning of the robot is done by planning a reference CMP trajectory that lies within the support polygon. In order to ensure rotational stability, the controller regulates the distance between the Zero Moment Point (ZMP) and the reference CMP through COG manipulation. A reference COG trajectory is generated from the measured GRF and a simplified model of the rotational dynamics of the robot. The reference COG is then decomposed into reference joint velocities via kinematic resolution of the COG Jacobian. Planar simulations show that modifying the COG trajectory using the CMP criterion with GRF feedback increases the controller's robustness to external disturbances compared to a ZMP based controller. Additionally, the results show that the constant non-zero momentum generated by external disturbances can be regulated by minimizing the difference between the reference CMP and ZMP positions, without explicitly regulating the momentum about the COG.

This paper presents a novel approach to solve multiobjective robotic trajectory planning problems. It proposes to find the Pareto optimal set, rather than a single solution usually obtained through scalarization, e.g., weighting the objective functions. Using the trajectory planning problem for a redundant manipulator as part of a captive trajectory simulation system, the general discrete dynamic programming (DDP) approximation method presented in our previous work is shown to be a promising approach to obtain a close representation of the Pareto optimal set. When compared with the set obtained by varying the weights, the results confirm that the DDP approximation method can find approximate Pareto objective vectors, where the weighting method fails, and can generally provide a closer representation of the actual Pareto optimal set.

This paper studies the problem of generating optimal joint trajectories for redundant manipulators when multiple criteria need to be considered and proposes a novel approach based on dynamic programming and the use of the Pareto optimality condition. The drawbacks of the traditional weighting method in optimization for generating the Pareto optimal set are discussed and an alternate approach using dynamic programming is proposed. The two approaches are implemented on the model of a 7-DOF redundant manipulator with the end-effector moving along a prescribed trajectory, while the joint trajectories are required to minimize two particular criteria. The results illustrate that the dynamic programming approach provides a better approximation of the Pareto optimal set and a more flexible and predictable framework to control the objective vectors.

A new stabilizing nonlinear controller for the vertical motion of an electrically actuated hopping robot is introduced and analyzed. The approach starts by finding reference limit cycles from the "passive dynamics" of a mass-spring system. The controller then modulates the system dynamics via the leg actuator during the stance phase to force the system trajectory to converge to this reference limit cycle. The controlled system dynamics is a continuous yet nonsmooth vector field. A piecewise-continuous Lyapunov function and the general forms of Lasalle's invariance and domain of attraction theorems are used to prove global asymptotic stability of the desired limit cycles. It is also shown that the derived passive dynamic cycles are indeed the positive limit sets of the controlled system. The rate of convergence can be adjusted but is limited by actuator constraints.

This paper presents the expansion and implementation of the controlled passive dynamic running (CPDR) strategy for legged robots, previously presented by the authors. The CPDR exploits the underlying passive dynamic operation of the robot's mechanical systems to reduce the energy spent for locomotion. Meanwhile, it ensures the stability of the vertical and forward motions as the robot speed varies. An "adaptive energy controller" stabilizes the hopping height accurately over a range of operating conditions. The passive dynamic derivations for the Monopod, together with the foot-placement algorithm and model-based joint controllers, are used to control the forward speed about the passive operation trajectories. New locomotion variables are used for robust synchronization between the hip-body and the leg oscillations. ARL-Monopod II achieved a speed of 1.25 m/s with specific resistance (a measure for energy cost of locomotion) of 30% of the earlier robot ARL-Monopod I, its predecessor, due to the newer hip and leg design and application of the CPDR control strategy

A path tracking control algorithm for tracked surface drilling machines is presented. The general dynamic model of such vehicle is simulated including track-soil interactions. The controller is based on feedback linearization of the equations with feedforward friction compensation. It linearizes the complex force-slip relationship to relate the states of the system to the inputs. The controller based on simplified models is applied to the detailed simulated model of the vehicle. The result is an accurate control of the lateral offset and forward velocity error along a path.

This paper presents the experimental implementation of an energy efficient "controlled passive dynamic running" strategy on a planar one-legged running robot with hip and leg compliance. We (1997) introduced the approach previously via simulations, and tested via preliminary experiments. The improved version described here permits running speed of up to 4.5 km/h with a total mechanical power expenditure of only 48 W, which translates into a specific resistance of only 0.22. This is the highest efficiency among all actively controlled legged robots.

We present a control strategy for a simplified model of a one-legged running robot which features compliant elements in series with hip and leg actuators. For this model, proper spring selection and initial conditions result in "passive dynamic" operation close to the desired motion, without any actuation. However, this motion is not stable. Our controller is based on online calculations of the desired passive dynamic motion which is then parametrized in terms of a normalized "locomotion time". We show in simulation that the proposed controller stabilizes a wide range of velocities and is robust to modeling errors. It also tracks changes in desired robot velocity and remains largely passive despite a fixed set of springs, masses, and inertias. Comparisons of simulated runs with direct hip actuation show 95% hip actuation energy savings at 3 m/s. Such energy savings are critical for the power autonomy of electrically actuated legged robots.

To study the design, control and energetics of autonomous dynamically stable legged machines we have built a planar one-legged robot, the ARL Monopod. Its top running speed of 4.3 km/h (1.2 m/s) makes it the fastest electrically actuated legged robot to date. We adapted Raibert's control laws for the low power electric actuation necessary for autonomous locomotion and performed a detailed energetic analysis of our experiments. A comparison shows that the ARL Monopod with its 125 W average power consumption is more energy efficient than previously built robots.

We present a new approach for vertical motion feedback control of a hopping robot. The task of the feedback control is to stabilize the robotic system to a desired limit cycle under existing constraints on the controls. Unlike most previous methods, the approach presented here does not rely on the construction of the Poincare map, which simplifies the subsequent analysis, but can be difficult or impossible to obtain analytically. The method can also be applied to more complicated, possibly nonlinear models of legged robots, as well as other variable structure systems.

We present a control strategy for a simplified model of a one legged running robot which features compliant elements in series with hip and leg actuators. Proper spring selection and initial conditions result in "passive dynamic" operation close to the desired motion, without any actuation. However, this motion is not stable. The proposed controller is based on online calculations of the desired passive dynamic motion and stabilises any fixed robot speed. It also tracks large changes in desired robot velocity and remains largely passive for a wide range of velocities, despite a fixed set of springs, masses and inertias. To this end the desired motion is expressed as a function of a normalized "locomotion time" parameter. Comparisons of simulated runs with direct hip actuation show dramatic energy savings of 95% at 3m/s. Such energy savings are critical for the power autonomy of electrically actuated legged robots.