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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°. 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°. 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.

The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory from the synthesis of chemical materials to the analysis of physical chemical properties, and it provides a vivid demonstration for future chemistry reshaped by AI and robotics technology.