Stepping up after spinal cord injury:negotiating an obstacle during walking

Every day walking consists of frequent voluntary modifications in the gait pattern to negotiate obstacles.After spinal cord injury,stepping over an obstacle becomes challenging.Stepping over an obstacle requires sensorimotor transformations in several structures of the brain,including the parietal cortex,premotor cortex,and motor cortex.Sensory information and planning are transformed into motor commands,which are sent from the motor cortex to spinal neuronal circuits to alter limb trajectory,coordinate the limbs,and maintain balance.After spinal cord injury,bidirectional communication between the brain and spinal cord is disrupted and animals,including humans,fail to voluntarily modify limb trajectory to step over an obstacle.Therefore,in this review,we discuss the neuromechanical control of stepping over an obstacle,why it fails after spinal cord injury,and how it recovers to a certain extent.

Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells

This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.

Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.

Effects of retained dry material on the impact,overflow and landing dynamics

During long-term operation,the performance of obstacles would be changed due to the material accumulating upslope the obstacle.However,the effects of retained material on impact,overflow and landing dynamics of granular flow have not yet been elucidated.To address this gap,physical flume tests and discrete element simulations are conducted considering a range of normalized deposition height h0/H from 0 to 1,where h0 and H represent the deposition height and obstacle height,respectively.An analytical model is modified to evaluate the flow velocity and flow depth after interacting with the retained materials,which further serve to calculate the peak impact force on the obstacle.Notably,the computed impact forces successfully predict the experimental results when a≥25°.In addition,the results indicate that a higher h0/H leads to a lower dynamic impact force,a greater landing distance L,and a larger landing coefficient Cr,where Cr is the ratio of slope-parallel component of landing velocity to flow velocity just before landing.Compared to the existing overflow model,the measured landing distance L is underestimated by up to 30%,and therefore it is insufficient for obstacle design when there is retained material.Moreover,the recommended Cr in current design practice is found to be nonconservative for estimating the landing velocity of geophysical flow.This study provides insightful scientific basis for designing obstacles with deposition.

Capture behavior of self-propelled particles into a hexatic ordering obstacle

Computer simulations are utilized to investigate the dynamic behavior of self-propelled particles(SPPs)within a complex obstacle environment.The findings reveal that SPPs exhibit three distinct aggregation states within the obstacle,each contingent on specific conditions.A phase diagram outlining the aggregation states concerning self-propulsion conditions is presented.The results illustrate a transition of SPPs from a dispersion state to a transition state as persistence time increases within the obstacle.Conversely,as the driving strength increases,self-propelled particles shift towards a cluster state.A systematic exploration of the interplay between driving strength,persistence time,and matching degree on the dynamic behavior of self-propelled particles is conducted.Furthermore,an analysis is performed on the spatial distribution of SPPs along the y-axis,capture rate,maximum capture probability,and mean-square displacement.The insights gained from this research make valuable contributions to understanding the capture and collection of active particles.

Linear-quadratic and norm-bounded combined differential game guidance scheme with obstacle avoidance for attacking defended aircraft in three-player engagement

A differential game guidance scheme with obstacle avoidance,based on the formulation of a combined linear quadratic and norm-bounded differential game,is designed for a three-player engagement scenario,which includes a pursuer,an interceptor,and an evader.The confrontation between the players is divided into four phases(P1-P4)by introducing the switching time,and proposing different guidance strategies according to the phase where the static obstacle is located:the linear quadratic game method is employed to devise the guidance scheme for the energy optimization when the obstacle is located in the P1 and P3 stages;the norm-bounded differential game guidance strategy is presented to satisfy the acceleration constraint under the circumstance that the obstacle is located in the P2 and P4 phases.Furthermore,the radii of the static obstacle and the interceptor are taken as the design parameters to derive the combined guidance strategy through the dead-zone function,which guarantees that the pursuer avoids the static obstacle,and the interceptor,and attacks the evader.Finally,the nonlinear numerical simulations verify the performance of the game guidance strategy.

A Lightweight UAV Visual Obstacle Avoidance Algorithm Based on Improved YOLOv8

The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and property.We propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance.The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)module.Additionally,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection head.Based on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different environments.In particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance capability.To verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was created.The experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.

Finite Element Simulation Analysis of a Novel 3D-FRSPA for Crawling Locomotion

A novel three-dimensional-fiber reinforced soft pneumatic actuator(3D-FRSPA)inspired by crab claw and human hand structure that can bend and deform independently in each segment is proposed.It has an omni-directional bending configuration,and the fibers twined symmetrically on both sides to improve the bending performance of FRSPA.In this paper,the static and kinematic analysis of 3D-FRSPA are carried out in detail.The effects of fiber,pneumatic chamber and segment length,and circular air chamber radius of 3D-FRSPA on the mechanical performance of the actuator are discussed,respectively.The soft mobile robot composed of 3D-FRSPA has the ability to crawl.Finally,the crawling processes of the soft mobile robot on different road conditions are studied,respectively,and the motion mechanism of the mobile actuator is shown.The numerical results show that the soft mobile robots have a good comprehensive performance,which verifies the correctness of the proposedmodel.This work shows that the proposed structures have great potential in complex road conditions,unknown space detection and other operations.

Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance:A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model errors.Furthermore,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon safety.In this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon control.To effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV.This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner.Additionally,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain disturbances.We also provide theoretical proof of the string stability under this control framework.Finally,extensive simulations are conducted to validate our approach.The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower bound.Moreover,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Facilitating and hindering factors of community nurses'emergency and critical care treatment abilities:A qualitative study

BACKGROUND This study adopts a descriptive phenomenological approach to investigate the facilitators and barriers of community nurses'abilities in managing critical and emergency conditions.With the transition of healthcare systems to the community,the evolution of nursing practices,and the attention from policies and practices,community nurses play a crucial role in the management of critical and emergency conditions.However,there is still a lack of comprehensive understanding regarding the factors that promote or hinder their capabilities in this area.AIM To understand the facilitators and barriers of community nurses in managing critical and emergency conditions,exploring the fundamental reasons and driving forces influencing their treatment capabilities.METHODS This study utilized the destination sampling method between May 2023 and July 2023.It employed a descriptive phenomenological approach within qualitative research methodologies.Through objective sampling,17 community nurses from 7 communities in Changning District,Shanghai,were selected as the study subjects.Semi-structured interviews were conducted to gather data,which were subsequently organized and analyzed using Colaizzi's seven-step analysis method,leading to the extraction of final themes.RESULTS The barrier factors identified from the interviews encompassed three topics:resource allocation,professional factors,and personal literacy.The facilitators comprised three themes:professionalism,management attention,and training and continuing education.We identified that the root causes of the barriers included the lack of practical treatment experience among community nurses,insufficient awareness of self-directed learning,and limited knowledge and technical proficiency.The professional quality of community nurses and management attention serve as motivation for them to enhance their treatment abilities.CONCLUSION To enhance the capability of community nurses in treating acute and critical patients,it is recommended to bolster training specifically tailored to acute and critical care,raise awareness of first aid practices,and elevate knowledge and skill levels.

Research Progress on Effects of Continuous Cropping on Soil Microbial Florae and Its Restoration

Continuous cropping has become a common form of agricultural production at present, but with the increase of continuous cropping years, continuous cropping obstacles such as soil-borne diseases and plant growth potential decline are becoming more and more common. At present, the causes of continuous cropping obstacles and continuous cropping restoration have become a hot issue in agricultural research. This paper summarized the effects of continuous cropping obstacles on soil microbial community structure and main technical methods to repair continuous cropping obstacles, such as agricultural measure management, microbial balance adjustment and soil improvement, aiming to provide theoretical reference for protecting the sustainable utilization of soil ecosystem and ensuring the stability of crop production.

Effects of Continuous Cropping on Soil Microbial Flora and Research Progress of Continuous Cropping Obstacle Reduction Techniques

Continuous cropping can bring economic benefits in a short time and meet the growing demand of agricultural products such as grain,but long-term continuous cropping will accelerate soil degradation,lead to the reduction of crop yield and the increase of disease rate,and destroy the balance of soil microbial structure.Therefore,it is not conducive to the sustainable development of soil ecosystem.In this paper,the problems caused by continuous cropping,such as imbalance of soil microbial flora,decrease of biodiversity,accumulation of root exudates and their effects on soil fertility and crop growth,were summarized,and some measures were suggested to alleviate the obstacles of continuous cropping,such as reasonable rotation,adjustment of intercropping planting mode and application of biological fertilizers.Moreover,the paper also looked forward to the development trend of continuous cropping obstacle reduction techniques,including the integration and application of biological techniques,the promotion of green ecological techniques and the application of intelligent management system.This study provides theoretical basis and technical support for the research of continuous cropping obstacle reduction techniques and promote the healthy and sustainable development of modern agriculture.

Impact of Continuous Cropping on Soil Phenolic Acid Substances and Research Progress on Continuous Cropping Obstacle Reduction Techniques

At present,long-term continuous cropping in agricultural production has formed a relatively common development trend.With the increase of continuous cropping years,soil phenolic acids are also affected to varying degrees.This paper summarized the effects of continuous cropping on soil phenolic acids and the research progress of continuous cropping obstacle reduction techniques,aiming at providing theoretical basis and technical support for the research of continuous cropping obstacle reduction techniques and promoting the healthy and sustainable development of modern agriculture.

Magneto-Hydro-Convective Nanofluid Flow in Porous Square Enclosure

In this work,a steady mixed convection in a two-dimensional enclosure filled viananoliquid Cu/H2O through a porous medium was numerically analyzed.The nanoliquid flow is designated utilizing the Brinkman-Forchheimer model.The upper and the bottom horizontal walls are considered to be hot(Th)and cold temperature(Tc),respectively,whereas the other walls are thermally insulated.The impact of various dimensionless terms such as the Grashof number(Gr)in the ranges(0.01–20),the Reynolds number(Re)in the ranges(50–500),the Hartman number(Ha)in the ranges(0–20),and three different location cases(0.25,0.5,and 0.75)are carefully analyzed.The obtained outcomes are established in the form of isotherms,streamlines,and the average Nusselt number.It has been found that heat transport increases significantly through rising Reynolds number(Re).For the location cases L=0.25,Re=50,and Gr=105,the heat transfer is maximum.

Potential Benefits and Obstacles of the Use of Internet of Things in Saudi Universities: Empirical Study

Internet of Things (IoT) among of all the technology revolutions has been considered the next evolution of the internet. IoT has become a far more popular area in the computing world. IoT combined a huge number of things (devices) that can be connected through the internet. The purpose: this paper aims to explore the concept of the Internet of Things (IoT) generally and outline the main definitions of IoT. The paper also aims to examine and discuss the obstacles and potential benefits of IoT in Saudi universities. Methodology: the researchers reviewed the previous literature and focused on several databases to use the recent studies and research related to the IoT. Then, the researchers also used quantitative methodology to examine the factors affecting the obstacles and potential benefits of IoT. The data were collected by using a questionnaire distributed online among academic staff and a total of 150 participants completed the survey. Finding: the result of this study reveals there are twelve factors that affect the potential benefits of using IoT such as reducing human errors, increasing business income and worker’s productivity. It also shows the eighteen factors which affect obstacles the IoT use, for example sensors’ cost, data privacy, and data security. These factors have the most influence on using IoT in Saudi universities.

Cultivation Techniques of Panax notoginseng F.H. Chen under Different Forests

To overcome the issues of high cost and continuous cropping obstacles in facility cultivation of Panax notoginseng_ F. H. Chen, satisfy the market demand, save the production cost, improve the utilization rate of forest land, increase the in-come of forest farmers and protect the ecological environment, the cultivation tech-niques of high-quality P. notoginseng seedlings from Wenshan, Yunnan under four kinds of forests （walnut forest, China fir forest, grape forest and kiwi forest） were in-vestigated in this study. The results showed that the height growth, crown diameter, survival rate and 3-year-old tuber weight of P. notoginseng_under walnut forest were higher than those under the other three kinds of forests; the height growth, crown diameter, survival rate and 3-year-old tuber weight of P. notoginseng under China fir forest were higher than those under grape forest and kiwi forest; and the crown di-ameter and survival rate under grape forest were higher, and the height growth and tuber weight under grape forest were lower than those under kiwi forest. Walnut is a broad-leaved deciduous tree species, so large-scale cultivation of P. notoginseng_should be conducted under broadleaf deciduous forest with canopy density around 0.8, taking advantage of the cool environment and rich humus layer under forest. This cultivation technology could save labor, shade, fertilizer and other costs, and accord with the ecological habit and the growth rules of P. notoginseng, thus im-proving yield and achieving high economic benefit.

ASSESSMENT OF MILITARY AIRFIELD OBSTACLE FREE SPACE BASED ON GIS

A mathematical model of obstacle limit surfaces for military airfield obstacle free space is established through airfield obstacle free space analysis.Based on the model,triangle mesh elevation model of military airfield obstacle free space is built by using the software-ArcGIS,and the 3-D display result is obtained.It is convenient to evaluate military airfield obstacle for superimposing digital elevation model（DEM）with military airfield topographic map.Thus it improves the efficiency greatly.It lays the foundation for the application of geographic information systems（GIS）in the management of military airfield obstacle free space.

Design of intelligent controller for mobile robot based on fuzzy logic

In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mobile robot to safely navigate in an indoor environment. First, the designs of two behaviors for a robot＇s autonomous navigation are described, including path tracking and obstacle avoidance, which emulate human driving behaviors and reduce the complexity of the robot＇s navigation problems in unknown environments. Secondly, the two behaviors are combined by using a finite state machine （FSM）, which ensures that the robot can safely track a predefined path in an unknown indoor environment. The inputs to this controller are the readings from the sensors. The corresponding output is the desired direction of the robot. Finally, both the simulation and experimental results verify the effectiveness of the proposed method.