Abstracts:We propose a hybrid approach to enhance the power generation performance of wrist-worn energy harvester. Based on a highly compact framework, the energy harvester integrates a planetary gear system to increase energy conversion capacity, an asymmetric carrier to improve motion capacity, and a magnetic spring to enhance displacement and velocity responses. These power enhancement mechanisms work together to boost the output power of the energy harvester. We develop an analytical model to theoretically investigate the effects of different power enhancement mechanisms. A miniature prototype with a total volume of 2.01 cm3 is fabricated and tested under bench-top excitations. The experiments include air gap determination of the magnetic spring and measurement of output power for various power enhancement configurations. It is found that the planetary gear system improves the power of conventional wrist-worn energy harvester by 335%–1422%. Based on this, the asymmetric carrier and magnetic spring can further increase the power by 35%–373% and 16%–278%, respectively. If hybridizing the power enhancement mechanisms, the output power can reach 8–28 times that of the conventional structure and the maximum power achieves 326.27±17.46 μW at 1.2 Hz.

Abstracts:This article presents the design, development, and implementation of walking control for the bipedal exoskeleton prototype Co-Ex. The main objective in developing this prototype is to take a successive step toward ambulatory support via an exoskeleton with self-stabilization capability. To attain this goal, Co-Ex is equipped with 8 torque-controllable active joints to provide ambulatory support while ensuring improved environmental interaction. The development of Co-Ex led to the following three contributions: first, self-stabilization capability in 3-D against external disturbances, second, a locomotion control framework that provides dynamically balanced walking behavior in 3-D despite the underactuated leg configuration, and third, a power-aware leg design in which most actuators are deployed around the waist for reduced leg inertia. To verify the self-stabilization and locomotion capabilities of Co-Ex, we conducted a series of experiments using a dummy manikin. As a result, Co-Ex showed self-stabilization behavior against disturbances and exhibited favorable locomotion characteristics that validated the proposed approach.

Abstracts:Inkjet printing has become an essential technology for manufacturing large-area, flexible organic light-emitting diode (OLED) displays because of its low cost, high efficiency, and flexible manufacturing characteristics. Printing large-area OLED displays requires thousands of nozzles to jet droplets, and the volume of droplets varies from nozzle to nozzle. However, the volume distribution of multinozzle droplets affects the accuracy and consistency of pixel film thickness. Therefore, controlling multinozzle droplet volume distribution has become a crucial challenge for printing large-area displays. This article proposes a multiagent deep reinforcement learning algorithm to extract the strategy from industrial data for the distribution control of multinozzle droplet volumes. First, we construct an offline policy experience dataset using historical state data and update it using online regulation data and new detection state data. Then, based on this dataset, we train the multiagent soft actor–critic networks offline to obtain the control policy. Finally, based on visual feedback information, the extraction strategy is used to achieve online regulation. Experiments were conducted in an industrial environment, and the mean volume of multinozzle droplets was controlled within $\pm 4\%$ of the target volume, with the standard deviation reduced to 0.07.

Abstracts:This article investigates the adaptive composite learning control problem of robots subject to uncertain dynamics and prescribed time error constraints. Existing prescribed time error constraint methods only achieve semiglobal results or guarantee system order-dependent convergence rate. In this article, by integrating a new prescribed time performance function into a tracking error-based barrier function, a novel prescribed time error constraint method is proposed with the following appealing features: 1) the constraint method is global; 2) the tracking error converges to a compact set with a proximate exponential rate, which can be preassigned by the user regardless of system order; 3) both settling time and compact set can be preassigned by the user. To handle the uncertain dynamics caused by inaccurate measurement of parameters, a novel fixed-time composite learning robot control (FTCLRC) method is developed by combining a newly designed nonsingular fixed-time integral terminal sliding mode and the Moore–Penrose pseudoinverse-based composite learning technique. In comparison with existing composite learning robot control methods that can only ensure exponential convergence, or finite-time convergence, which is dependent on the unpredictable excitation strengths and initial system states, the proposed FTCLRC can guarantee that both the tracking error and parameters estimation error converge to zero in fixed-time, under a weak IE without singularity issue. In particular, the convergence time only depends on the user-designed parameters, independent of the system's initial states, and the unpredictable excitation strengths. Experiment results are given to show the superior performance of the proposed control method.

Abstracts:Ensuring safety in physical human–robot interaction is challenging due to hardware and control architecture differences across robots, and is often implemented as system-dependent ad hoc approaches. To offer a holistic solution, we present a hardware-independent safety-focused admittance control approach, which promotes safety at the reference-generation level. This safety framework can restrict virtual dynamics through soft virtual bounds. Hard bounds are also introduced as a way to impose infinitely stiff soft bounds. As part of the overall approach, we also present a method for serial manipulator and multisegment entity collision avoidance by using partial Jacobians. In order to demonstrate the methodology's versatility across hardware platforms, we experimentally validate on two robotic systems: first, the Virtual Reality Exoskeleton (V-Rex), a nonanthropomorphic full-body haptic device composed of five robotic arms interacting with the body at the hands, feet, and pelvis; and second, the EXO-UL8, an anthropomorphic bimanual upper limb exoskeleton; which exist on opposite ends of the task/joint space control, nonredundant/redundant, off-the-shelf (industrial)/custom, nonanthropomorphic/anthropomorphic spectra. Experimental results validate virtual dynamics, soft bounds, hard bounds, and multi-arm collision avoidance on both systems. In all cases, both systems respect bound and collision constraints, supporting the approach as a safety-focused admittance control design.

Abstracts:Soft actuators are gaining increasing popularity in various fields, including marine engineering and biomedical engineering. However, due to their nonlinear properties and significant material deformation, dynamic modeling of soft actuators for motion behavior is quite a challenge, especially for underwater environments. This article ingeniously combines Euler–Bernoulli theory and Cosserat rod theory to propose an efficient and accurate dynamic model for solving the underwater motion behavior of fiber-reinforced soft actuators. Under the assumption of neglecting external forces, we first utilize Euler–Bernoulli theory to establish a mathematical model describing the relationship between water pressure and the bending deformation of the soft actuator. This model is then employed as a boundary condition when solving the dynamic model. Based on this, we use Cosserat rods theory to depict the dynamic behavior of the soft actuator's motion in an underwater environment. In particular, we analyze the impact of its own gravity, buoyancy, and water damping on the motion of the soft actuator. To validate the proposed dynamic model, we fabricated a novel fiber-reinforced soft actuator and a water-driven control system. Subsequently, we conducted a series of model validation experiments. Experimental results show the model maximum error rate is below 13%, thereby confirming the effectiveness of the model, which can predict the motion behavior of the soft actuator under different water-driven pressures.

Abstracts:This article considers the problem of target circumnavigation using multiple unmanned marine vehicles in the communication-denied environment. The vehicles are required to circumnavigate the target maintaining a desired radius and evenly spaced positions relying solely on local relative range measurements. A novel distributed control strategy with a saturation function design is proposed for cooperatively circumnavigating both static and dynamic targets. Via the Lyapunov control theory and the input-to-state property, the closed-loop stability with only the relative range information is achieved. The steady-state circumnavigation error converges to zero and is uniformly ultimately bounded for the static and dynamic targets, respectively. In addition, a second-order sliding mode observer is developed to estimate the range rate information. The observer incorporates linear and nonlinear correction terms and is proven to be global asymptotically stable. The finite time convergence property also contributes to the control performance. Finally, numerical simulations and real experiments involving three unmanned surface vehicles are conducted. Compared with the existing approach, the proposed method shows convenience in implementations and better circumnavigation control performance especially for dynamic targets.

Abstracts:Unmanned aerial vehicles (UAVs) or UAVs are increasingly applied by military and civilians in a wide range of applications, such as environmental monitoring, search and rescue, target detection, combat, precision agriculture, and three-dimensional (3-D) mapping. The failure of UAVs can cause human casualties and property damage. Hence, it is crucial to consistently monitor the UAV in order to detect any potential malfunction in vital components like motors and electronic speed controllers (ESCs). This study proposed a new UAV condition-monitoring approach that combines sensor fusion and fuzzy-based decision-making algorithms. The vibration, the motor's rotational speed, and current parameters are utilized to determine the UAV's condition, specifically the UAV's motors or ESCs. The UAV's condition is categorized into safe, partial safe, and danger. Experimental results show a clear distinction between a healthy and faulty UAV's component in terms of vibration, with a discrepancy of more than 100% in the majority of the cases. The proposed framework can successfully alert the user if there is a malfunction in the UAV's motor or ESC before or when the UAV is flying.

Abstracts:This article proposes a skeleton-guided spatiotemporal motion planner (SGS-Planner) for safe and efficient aerial robot navigation in challenging constrained space. The planner takes advantage of a spatial-temporal hierarchical framework to generate a safer and more efficient trajectory rapidly. In spatial planning, different from existing approaches that either ignore the clearance between trajectories and obstacles or suffer from local minima, path smoothing is guided by a skeleton away from obstacles. Specifically, a fast sphere inflation-based skeleton extraction method is elaborately designed for high-clearance path searching. After that, we propose a skeleton-guided path smoothing approach to generate smooth and high-clearance paths, where the smoothing is formulated as unconstrained quadratic programs. In temporal planning, the feasible trajectory is generated along the optimized path by employing an optimal time allocation method and a parallel back-up strategy to guarantee the success of planning. We validate the performance of the proposed method in extensive challenging simulations and real-world environments. Comparative results show that our approach outperforms state-of-the-art methods in terms of planning success rate, average computational efficiency (5–24 times faster), and minimum clearance (12%–177% larger) in restricted space.

Abstracts:Flapping-wing aircraft dynamics features with periodic characteristics. The widely-used averaging theory simplifies the model to some extent, but it also fails to uncover the dynamic evolution in the cycle. The accuracy of the solution is poor and the derived controller might fail. This study proposes a scheme for the stabilization controller design of a special wing-tail interaction inspired flapping-wing aircraft by considering the periodic characteristics. A periodic longitudinal dynamic model is first built by including the main periodic issues existed in the aircraft. With this model, the equilibrium status of the aircraft is trimmed as a limit cycle by using a multiple shooting method and its periodic stability is analyzed. The aircraft is unstable, and its periodic controllability is proved in ahead. Stabilization controllers based on averaged and periodic models are separately designed. It is proved that the controllers derived from root locus based on the averaged model are not always able to stabilize the aircraft in simulation, while the periodic based controller can give a more sufficient stabilization conclusion by giving a novel stability criterion. The final experiment also proves that the periodic-based controller can successfully stabilize the aircraft. This firmly supports our proposal that the periodic characteristics should be included in the dynamic analysis of flapping-wing aircraft.