Ensuring safety of unmanned surface vehicles (USVs) during nearshore field operations is challenging as they face unknown risks of collision with surface and underwater obstacles. To address this problem, we propose a bio-inspired collision risk-assessment method for ensuring safe USVs operation in nearshore clean waters. Collision risks are typically unknown due to the imprecision of underwater measurements for the water-air boundary light refraction, and disturbances caused by wave-induced USV motion. To tackle these two causes of imprecision, our collision risk-assessment method employs one single stereo camera and an IMU. We use visual information from the stereo camera to model the light rays at the water-air interface, rectify the underwater measurements, and monitor for surface obstacles, and IMU data to model wave disturbances via time-frequency analysis. We verify the performance of this new method in both the indoor and outdoor experiments, which demonstrates that it achieves a 65.44% improvement in the depth accuracy at the water-air interface compared with raw depth measurements, and provides a range of safety levels for guiding USV operations. This new collision risk-assessment method significantly expands the USVs working areas while ensuring safety during field exploration. Additionally, the simplicity of the sensor setup and thus minimal cost of this method underscore its utility for supporting USV-based various explorations in clear waters.

Over the past few decades, wearable exoskeletons of various forms have been developed to assist human activities or for rehabilitation of movement disorders. However, sustainable exoskeletons with efficient energy harvesting devices still have not been fully explored. In this paper, we propose the design of a lightweight wearable Bowden-cable-actuated soft exoskeleton robot with energy harvesting capability. Unlike previous wearable exoskeletons, the presented exoskeleton uses an electromagnetic generator to both harvest biomechanical energy and to output mechanical torque by controlling an operation mode relay switch based on a human’s gait. Moreover, the energy-harvesting module also acts as a knee impact absorber for the human, where the effective damping level can be modulated in a controlled manner. The harvested energy is regulated and stored in super capacitors for powering wireless sensory devices when needed. The experimental results show an average of a 7.91% reduction in thigh muscle activity, with a maximum of 3.2 W of electric power being generated during movement downstairs. The proposed design offers important prospects for the realization of lightweight wearable exoskeletons with improved efficiency and long-term sustainability.

A solar tracker significantly increases the amount of energy harvested by a floating photovoltaic system by adjusting the pose of its photovoltaic (PV) panels to optimize their exposure to the solar rays. Conventional solar trackers perform such an adjustment using actuators, which is energy-consuming and involves complex structures. In this paper, a novel dual-axis wave-driven solar tracker is proposed where the photovoltaic (PV) panel is adjusted by the inertia force and gravity. Actuators are replaced by brakes to fix the pose of the PV panel. The kinematics and dynamics of the system are investigated, which are further used for the state feedback and the formulation of the control strategy. A sliding-mode observer is used to estimate the effect of winds on the system, which enables the robustness of the system to be enhanced. A motion planning strategy is also developed to track the solar position and minimize the movements of two joints. Indoor experiments are conducted to test the accuracy of the state feedback and dynamic model. The performance of the uncertainty observer is also tested by experiments. At last, field experiments on the real water surface are implemented to verify the feasibility of the proposed system. The results show that the solar tracker is able to adjust the PV panels at a velocity of 8.89 deg/s under the effect of limited base motions with amplitude less than 4 $^{\circ}$ . Note to Practitioners—Traditional solar trackers can be driven passively by chemical energy or actively using actuators. The former cannot function stably in the field environment, and the latter is complex in structure and energy-consuming. For the FPV system under the effect of waves, the solar tracker can utilize the motion of the base to align its PV panel to the solar position. Thus, we present a wave-driven solar tracker without using actuators, and brakes are applied to lock the position of joints. The proposed solar tracker is energy-efficient, has a simpler structure and hardware than the active one, and is more robust than the passive one. We model the system and propose a control scheme to drive the PV panel using inertia and gravity. We also observe the effect of winds to enhance the system robustness. The motion planning strategy is investigated to minimize the movements of the joints. The impact of base motion and control period on the energy-harvesting efficiency is analyzed by simulations. Both indoor and field experiments are implemented to verify the feasibility of the system. The experimental results show that the system performs well even when the base shacking is less than 4 $^\circ$ . Note that the control algorithm is a model-based algorithm, which means the model parameters (inertia, gravity, etc.) need to be reidentified for different PV panels and system properties. That is a challenge for large-scale deployment with different configurations. Future research will address the system identification problem and enable the model parameters to be updated automatically.

Sailboats have a wide range of applications on account of their energy-saving, environmentally benign and low-noise characteristics. This has led to the increasing popularity of research in autonomous sailing robots, with concomitant attention paid to the development of methods that ensure their safety during sailing. Sailboats are affected by obstacles and by nonlinear aerodynamic and hydrodynamic factors. In particular, sailboats are affected by the wake generated by a moving vessel, which in this letter is denoted as “moving boat”. Wakes are swift and can have severe adverse effects on a sailboat by causing it to veer off course, stall or capsize. Moreover, sailboats have low mobility and large inertia and will therefore struggle to avoid a wake. Thus, in this letter, we describe our efforts to develop a method to enable a sailboat to perform wake avoidance. We model the wake as a Kelvin wake and generate waypoints for an orientation-restricted Dubins path-based Kelvin wake-avoidance method. A V-stability-based control method has proven suitable for generating the path, after which the desired heading angle for sailboat control is obtained. The resulting wake-avoidance method is verified in simulations and experiments. It enables the sailboat to avoid alongside and toward wakes, and the qualitative results have revealed that the maximum range of roll in alongside scenario and maximum range of surge acceleration in toward scenario are reduced by 57.4% and 23.4%, respectively, relative to a situation when no wake-avoidance method was used. The maximum range of roll in toward scenario is acceptable.

Bowden-cable-actuated soft exoskeleton robots are known for their light weight and flexibility of power transmission during rehabilitation training or movement assistance for humans. However, friction-induced nonlinearity of the Bowden transmission cable and gearbox backlash pose great challenges forprecise tracking control of the exoskeleton robot. In this paper, we proposed the design of a learning-based repetitive controller which could compensate for the non-linearcable friction and gearbox backlash in an iterative manner. Unlike most of the previous control schemes, the presented controller does not require apriori knowledge or intensive modeling of the friction and backlash inside the exoskeleton transmission system. Instead, it uses the iterative learning control (ILC)to adaptively update the reference trajectory so that the output hysteresis caused by friction and backlashis minimized. In particular, a digital phase-lead compensator was designed and integrated with the ILC to address the issue of backlash delay and improve the stability and tracking performance. Experimental results showed an average of seven iterations for the convergence of learning and a 91.1% reduction in the RMS tracking error (~1.37 deg) compared with the conventional PD control. The proposed controller design offers promising options for the realization of lightweight, wearable exoskeletons with high tracking accuracies.