Zhen Xu

Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient path continuity under such constrained conditions. To address these issues, this study proposes an improved A*-based path-planning framework that integrates adaptive heuristic weighting, dynamic corner correction, and Bézier-curve-based path smoothing. Rather than introducing an entirely new planning paradigm, the proposed method coordinates several existing optimization strategies within a unified framework to improve search efficiency, path regularity, and path continuity for facility agricultural scenarios. The adaptive heuristic weighting strategy dynamically adjusts the contribution of the heuristic term according to the relative distance between the current node and the target node, thereby improving global search guidance while reducing unnecessary exploration. Dynamic corner correction is introduced to suppress zigzag path structures and reduce redundant turning nodes in obstacle-dense regions, while Bézier-curve-based smoothing is employed to improve path continuity and compatibility with the kinematic characteristics of agricultural mobile robots. Simulation experiments were conducted on grid maps and greenhouse-like environments with different obstacle distributions, and comparative evaluations were performed against Dijkstra, RRT, and conventional A* algorithms. Under representative simulation scenarios, the proposed framework reduced the number of turning points by up to 53.7% and decreased computation time by approximately 19.4% compared with the conventional A* algorithm, based on the average results of repeated trials under identical conditions. In addition, physical platform experiments on a ROS2-based agricultural robot demonstrated that the planned trajectories maintained relatively stable navigation performance and smoother directional transitions in constrained greenhouse-like environments. The results indicate that the proposed framework achieves a more balanced trade-off between computational efficiency, path compactness, and path smoothness than the benchmark methods considered in this study. Nevertheless, the current validation remains limited to structured or semi-structured greenhouse environments under static obstacle conditions. Future work will focus on improving adaptability to dynamic agricultural scenarios and integrating the framework with real-time perception and motion-control systems for practical greenhouse deployment.

The sequence features of single-stranded DNA (ssDNA) adsorbed on a graphene oxide (GO) surface are important for applications of the DNA/GO functional structure in biosensors, biomedicine, and materials science. In this study, molecular dynamics (MD) simulations were used to examine the adsorption of polynucleotide ssDNAs (A₁₂, C₁₂, G₁₂, and T₁₂) and single nucleotides (A, C, G, and T) on the GO surface. For the latter case, the nucleotide–GO interaction energy followed the trend G > A > C > T, even though it was influenced by specific adsorption sites. In the case of polynucleotides, unexpectedly polythymidine (T₁₂) had the strongest interaction with the GO surface. The angle distributions of the adsorbed nucleobases indicated that T₁₂ was more likely to form a quasi-parallel structure with GO compared to A₁₂, C₁₂, or G₁₂. This was attributed to the weakest π-stacking interactions of thymine. Weaker intra-molecular base-stacking interactions made it easier to break the structures of pyrimidine bases relative to those of purine bases. Weaker inter-molecular base-stacking interactions between T₁₂ and the GO surface enabled T₁₂ to adjust its structure easily to a more stable one by slipping on the surface. This result provides a new understanding of polynucleotide ssDNA adsorption on GO surfaces, which will help in the design of functional DNA/GO structure-based platforms.

Nanomotors are of great importance when studying nanoelectromechanical systems that contain carbon nanotube (CNT) based nanomotors for controlled motion in water using a rotating electric field. In this paper, Y-type nanomotor structures based on CNTs immersed in an aqueous solution are designed, and systems with different Y-type structure angles are simulated using molecular dynamics. The simulation results suggest that when the rotating electric field speed is appropriate, changing the Y-type structure angle can adjust the hysteresis (forward and backward motion) of nanomotor rotors during rotation. Precise control over the rotation angle of the nanomotor rotor improves its working efficiency. The enclosed simulation results are an important reference when designing nanoscale propellers and complex structured nanogear systems in aqueous solutions.

In a transmission system, the rotational speed of the output can be adjusted by the system. In this study, we introduce a three-stage rotational transmission nanosystem model that uses carbon nanotubes with excellent mechanical properties to fabricate coaxially distributed nanomotors with three nanobearings. Driven by a gigahertz nanomotor at 300 K, the nanobearings are changed to adjust the output rotational frequency. In view of the differences in chirality and radius of the carbon nanotubes, 17 transmission models are established and tested by molecular dynamics simulation. The rotor’s rotational transmission ratio curves show the dynamic response of the transmission system. A better down-converting transmission system can be obtained when the radius of the rotating component is between 0.58nm and 0.88nm or the difference in radii between the rotating components is greater than 0.2 nm. From the results obtained by molecular dynamics simulation, some key points are demonstrated for future deceleration of the rotary nanomotor.