A Dual-Layer Weight-Leader-Vicsek Model for Multi-AGV Path Planning in Warehouse.

Multiple automatic guided vehicles are widely involved in industrial intelligence. Path planning is crucial for their successful application. However, achieving robust and efficient path planning of multiple automatic guided vehicles for real-time implementation is challenging. In this paper, we propose a two-layer strategy for multi-vehicle path planning. The approach aims to provide fast computation and operation efficiency for implementation. The start-destination matrix groups all the vehicles, generating a dynamic virtual leader for each group. In the first layer, the hybrid A* algorithm is employed for the path planning of the virtual leaders. The second layer is named leader-follower; the proposed Weight-Leader-Vicsek model is applied to navigate the vehicles following their virtual leaders. The proposed method can reduce computational load and achieve real-time navigation by quickly updating the grouped vehicles' status. Collision and deadlock avoidance is also conducted in this model. Vehicles in different groups are treated as dynamic obstacles. We validated the method by conducted simulations through MATLAB to verify its path-planning functionality and experimented with a localization sensor.

An adaptive collision avoidance strategy for autonomous vehicle under various road friction and speed.

When the autonomous vehicle (AV) is under various road friction and speed, emergency collision avoidance is extremely difficult. In this situation, the AV may encounter severe understeering problem, so it is hard to achieve collision avoidance, even under the control of active safety system. To tackle this problem, an adaptive collision avoidance strategy (ACAS) is proposed for AV under various road friction and speed. The adaptive performance of the ACAS is realized via three aspects. (1) An adaptive reference path planning method is proposed to provide desired evasive speed and reference path for the AV according to various road friction and reduces the turning burden of AV. (2) A predictive-based fuzzy controller is designed to realize the speed control, and it improves the tracking accuracy of various desired evasive speed. (3) A novel turning enhanced method built with a direct yaw turning controller and a torque distribution method can enhance the turning capability of AV. Finally, the proposed strategy is verified on AV via simulation experiments. The code can be found online here: https://github.com/wangjinlei-hnu/ACAS.

Online 3-Dimensional Path Planning with Kinematic Constraints in Unknown Environments Using Hybrid A* with Tree Pruning.

In this paper we present an extension to the hybrid A* (HA*) path planner. This extension allows autonomous underwater vehicle (AUVs) to plan paths in 3-dimensional (3D) environments. The proposed approach enables the robot to operate in a safe manner by accounting for the vehicle's motion constraints, thus avoiding collisions and ensuring that the calculated paths are feasible. Secondly, we propose an improvement for operations in unexplored or partially known environments by endowing the planner with a tree pruning procedure, which maintains a valid and feasible search-tree during operation. When the robot senses new obstacles in the environment that invalidate its current path, the planner prunes the tree of branches which collides with the environment. The path planning algorithm is then initialised with the pruned tree, enabling it to find a solution in a lower time than replanning from scratch. We present results obtained through simulation which show that HA* performs better in known underwater environments than compared algorithms in regards to planning time, path length and success rate. For unknown environments, we show that the tree pruning procedure reduces the total planning time needed in a variety of environments compared to running the full planning algorithm during replanning.

Aging-independent and size-dependent genotoxic response induced by titanium dioxide nanoparticles in mammalian cells.

Titanium dioxide nanoparticles (TiO2 NPs) are subjected to various transformation processes (chemical, physical and biological processes) in the environment, potentially affecting their bioavailability and toxic properties. However, the size variation of TiO2 NPs during aging process and subsequent effects in mammalian cells are largely unknown. The aim of this study was to illustrate the adverse effects of TiO2 NPs in different sizes (5, 15 and <100 nm) during aging process on human-hamster hybrid (AL) cells. There was an aging-time dependent enhancement of average hydrodynamic size in TiO2 NPs stock suspensions. The cytotoxicity of fresh TiO2 NPs increased in a size-dependent manner; in contrast, their genotoxicity decreased with the increasing sizes of NPs. No significant toxicity difference was observed in cells exposed to either fresh or 60 day-aged TiO2 NPs. Both Fresh and aged TiO2 NPs efficiently induced mitochondrial dysfunction and activated Caspase-3/7 in a size-dependent manner. Using mitochondrial-DNA deficient (ρ0) AL cells, we further discovered that mitochondrial dysfunction made significant contribution to the size-dependent toxicity induced by TiO2 NPs during the aging process. Taken together, our data indicated that TiO2 NPs could significantly induced the cytotoxicity and genotoxicity in an aging time-independent and size-dependent manner, which were triggered by mitochondrial dysfunction. Our study suggested the necessity to include size as an additional parameter for the cautious monitoring of TiO2 NPs disposal before entering the environment.

Effect of temperature downshifts on a bench-scale hybrid A/O system: Process performance and microbial community dynamics.

Effect of temperature downshifts on process performance and bacterial community dynamics was investigated in a bench-scale hybrid A/O system treating real domestic wastewater. Results showed that the average COD removal in this system reached 90.5%, 89.1% and 90.3% for Run 1 (25 °C), Run 2 (15 °C) and Run 3 (10 °C), respectively, and variations in temperature barely affected the effluent COD concentration. The average removal efficiencies of NH4(+)-N were 98.4%, 97.8%, 95.7%, and that of TN were 77.1%, 61.8%, 72% at 25 °C, 15 °C and 10 °C, respectively. Although the hybrid system was subjected to low temperature, this process effectively removed NH4(+)-N and TN even at 10 °C with the average effluent concentrations of 2.4 mg/L and 14.3 mg/L, respectively. Results from high-throughput sequencing analysis revealed that when the operation temperature decreased from 25 °C to 10 °C, the richness and diversity indexes of the system decreased in the sludge samples, while underwent an increase in the biofilm samples. Furthermore, the major heterotrophic bacteria consisted of Lewinella, Lutimonas, Chitinophaga and Fluviicola at 10 °C, which could be central to effective COD removal at low temperature. Additionally, Azospira, one denitrifying-related genus increased from 0.4% to 4.45% in the biofilm samples, with a stable TN removal in response to temperature downshifts. Nitrosomonas and Nitrospira increased significantly in the biofilm samples, implying that the attached biofilm contributed to more nitrification at low temperature.

On the inclusion of post-MP2 contributions to double-hybrid density functionals.

In this study, we explore the effect of supplementing the DuT spin-component-scaled double-hybrid density functional method with post-second-order Møller-Plesset-type theory (MP2) correlation terms. We find that the inclusion of additional MP3 correlation energies has almost no effect on the performance. Further addition of correlation effects from MP4 generally leads to a small improvement in the performance. However, we find that the inclusion of the higher-order perturbative correlation effects does not rectify some major shortcomings of DuT for more challenging systems, and the use of MP4, in fact, leads to a significant deterioration in the performance in some cases. We also find that the use of correlation energies from CCSD(T) instead of those from MP3 and MP4 does not lead to a substantial improvement over the MP4-based method, both in general and in some difficult cases that we have examined. An additional observation is that, for large systems that are dominated by noncovalent interactions, DuT and the two MPn-based post-MP2 double-hybrid density functional theory (DFT) procedures all benefit from the inclusion of dispersion corrections. Overall, our investigation suggests that the current generation of MP2-based double-hybrid DFT methods may already be providing close to the optimal performance that can be achieved with the double-hybrid methodology paired with spin-component-scaling. Development of even better double hybrids is an active research field, and we hope that our study provides valuable insights. We recommend the continuing use of existing MP2-based double-hybrid methods as a bridging level between hybrid density functional procedures and high-level wave-function-based procedures.