Adaptive Model Prediction Control-Based Multi-Terrain Trajectory Tracking Framework for Mobile Spherical Robots

Owing to uncertainties in both kinematics and dynamics, the current trajectory tracking framework for mobile robots like spherical robots cannot function effectively on multiple terrains, especially uneven and unknown ones. Since this is a prerequisite for robots to execute tasks in the wild, we enhance our previous hierarchical trajectory tracking framework to handle this issue. First, a modified adaptive RBF neural network (RBFNN) is proposed to represent all uncertainties in kinodynamics. Then the Lyapunov function is utilized to design its adaptive law, and a variable step-size algorithm is employed in the weights update procedure to accelerate convergence and improve stability. Hence, a new adaptive model prediction control-based instruction planner (VAN-MPC) is proposed. Without modifying the bottom controllers, we finally develop the multi-terrain trajectory tracking framework by employing the new instruction planner VAN-MPC. The practical experiments demonstrate its effectiveness and robustness.Comment: 10 pages, 20 figures. This work has been submitted to the IEEE Transactions on Industrial Electronics for possible publicatio

An MPC-based Optimal Motion Control Framework for Pendulum-driven Spherical Robots

Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot's attitude and motors' current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption.Comment: This paper has been submitted to Control Engineering Practic

Droplets microfluidics platform—A tool for single cell research

Cells are the most basic structural and functional units of living organisms. Studies of cell growth, differentiation, apoptosis, and cell-cell interactions can help scientists understand the mysteries of living systems. However, there is considerable heterogeneity among cells. Great differences between individuals can be found even within the same cell cluster. Cell heterogeneity can only be clearly expressed and distinguished at the level of single cells. The development of droplet microfluidics technology opens up a new chapter for single-cell analysis. Microfluidic chips can produce many nanoscale monodisperse droplets, which can be used as small isolated micro-laboratories for various high-throughput, precise single-cell analyses. Moreover, gel droplets with good biocompatibility can be used in single-cell cultures and coupled with biomolecules for various downstream analyses of cellular metabolites. The droplets are also maneuverable; through physical and chemical forces, droplets can be divided, fused, and sorted to realize single-cell screening and other related studies. This review describes the channel design, droplet generation, and control technology of droplet microfluidics and gives a detailed overview of the application of droplet microfluidics in single-cell culture, single-cell screening, single-cell detection, and other aspects. Moreover, we provide a recent review of the application of droplet microfluidics in tumor single-cell immunoassays, describe in detail the advantages of microfluidics in tumor research, and predict the development of droplet microfluidics at the single-cell level

Enhancing the 3D printing fidelity of vat photopolymerization with machine learning-driven boundary prediction

Like many pixel-based additive manufacturing (AM) techniques, digital light processing (DLP) based vat pho-topolymerization faces the challenge that the square pixel based processing strategy can lead to zigzag edges especially when feature sizes come close to single-pixel levels. Introducing greyscale pixels has been a strategy to smoothen such edges, but it is a challenging task to understand which of the many permutations of projected pix-els would give the optimal 3D printing performance. To address this challenge, a novel data acquisition strategy based on machine learning (ML) principles is proposed, and a training routine is implemented to reproduce the smallest shape of an intended 3D printed object. Through this approach, a chessboard patterning strategy is developed along with an automated data refining and augmentation workflow, demonstrating its efficiency and effectiveness by reducing the deviation by around 30%

Properties of Cobalt- and Nickel-Doped Zif-8 Framework Materials and Their Application in Heavy-Metal Removal from Wastewater

Heterometallic zeolite imidazole framework materials (ZIF) exhibit highly attractive properties and have drawn increased attention. In this study, a petal-like zinc based ZIF-8 crystal and materials doped with cobalt and nickel ions were efficiently prepared in an aqueous solution at room temperature. It was observed that doped cobalt and nickel had obviously different effects on the morphology of ZIF-8. Cobalt ions were beneficial for the formation of ZIF-8, while addition of nickel ions tended to destroy the original configuration. Then we compared the absorption ability for metal ions between petal-like ZIF-8 and its doped derivatives with anion dichromate ions (Cr2O72−) and cation copper ions (Cu2+) as the absorbates. Results indicated that saturated adsorption capacities of Co@ZIF-8 and Ni@ZIF-8 for Cr2O72− reach 43.00 and 51.60 mg/g, while they are 1191.67 and 1066.67 mg/g for Cu2+, respectively, which are much higher than the original ZIF-8 materials. Furthermore, both the heterometallic ZIF-8 materials show fast adsorption kinetics to reach adsorption equilibrium. Therefore, petal-like ZIF-8 with doped ions can be produced through a facile method and can be an excellent candidate for further applications in heavy-metal treatment

An Electrochemical Molecularly Imprinted Polymer Sensor for Rapid β-Lactoglobulin Detection

Facile detection of β-lactoglobulin is extraordinarily important for the management of the allergenic safety of cow’s milk and its dairy products. A sensitive electrochemical sensor based on a molecularly imprinted polymer-modified carbon electrode for the detection of β-lactoglobulin was successfully synthesized. This molecularly imprinted polymer was prepared using a hydrothermal method with choline chloride as a functional monomer, β-lactoglobulin as template molecule and ethylene glycol dimethacrylate as crosslinking agent. Then, the molecularly imprinted polymer was immobilized on polyethyleneimine (PEI)-reduced graphene oxide (rGO)-gold nanoclusters (Au-NCs) to improve the sensor’s selectivity for β-lactoglobulin. Under optimal experimental conditions, the designed sensor showed a good response to β-lactoglobulin, with a linear detection range between 10−9 and 10−4 mg/mL, and a detection limit of 10−9 mg/mL (S/N = 3). The developed electrochemical sensor showed a high correlation in the detection of β-lactoglobulin in four different milk samples from the market, indicating that the sensor can be used with actual sample

S-doped graphene-regional nucleation mechanism for dendrite-free lithium metal anodes

Lithium metal is the most promising anode material for next-generation batteries, owing to its high theoretical specific capacity and low electrochemical potential. However, the practical application of lithium metal batteries (LMBs) has been plagued by the issues of uncontrollable lithium deposition. The multifunctional nanostructured anode can modulate the initial nucleation process of lithium before the extension of dendrites. By combing the theoretical design and experimental validation, a novel nucleation strategy is developed by introducing sulfur (S) to graphene. Through first-principles simulations, it is found that S atom doping can improve the Li adsorption ability on a large area around the S doping positions. Consequently, S-doped graphene with five lithiophilic sites rather than a single atomic site can serve as the pristine nucleation area, reducing the uneven Li deposition and improving the electrochemical performance. Modifying Li metal anodes by S-doped graphene enables an ultralow overpotential of 5.5 mV, a high average Coulombic efficiency of 99% over more than 180 cycles at a current density of 0.5 mA cm(-2) for 1.0 mAh cm(-2), and a high areal capacity of 3 mAh cm(-2). This work sheds new light on the rational design of nucleation area materials for dendrite-free LMB.Web of Science924art. no. 180400