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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Analysis of the Causes of Continuous Cropping Obstacles for Atractylodes macrocephala Koidz in Pingjiang County and Its Control Methods

First at all, it introduced the concept and the damages of continuous cropping obstacle. Then, it analyzed the causes of continuous cropping obstacles for Atractylodes macrocephala Koidz. In the end, in order to provide guidance for pro- moting sustainable development of Atractylodes macrocephala Koidz industry in Pingjiang County, it put forward some control methods for eliminating continuous cropping obstacles of Atractylodes macrocephala Koidz, including breeding varieties with high resistance; applying rotation cropping and intercropping reasonable; rational fertilization and soil disinfection; introducing antagonistic bacterial and eliminating au- tointoxication.

MODELING OF ROBOT MANIPULATOR AND OBSTACLE AVOIDANCE

A robot intelligent path planning system RIPPS is developed, which can be utilized for a robot off line programming tool. The system consists of three parts: geometric modeler, kinematic modeler and path planer. The geometric modeler is used to construct the robot working environment cluttered with obstacles and the robot kinematic modeler to define robot manipulators by the input parameters. Giving robot start and the goal configurations, the path planer can produce a quasi optimal path. By transforming obstacles into the C space to form C obstacles, the path searching is performed in C space. The planning simulations are performed on a SGI workstation, the future research is to implement the planer on a commercial robot manipulators.

The Obstacles and Solutions to Improving Non English-major Students' Listening Comprehension

Listening has been playing an essential role in daily communication.However,listening is the most difficult part and has been an obstacle in the process of learning English for most of learners for a variety of reasons.The paper mainly discusses the linguistic and non—linguistic obstacles in the process of listening and aims to help English learners find out their own shortcomings and put forward some corresponding solutions to improve learners' listening ability.

A Research on Middle School Students'English Reading Obstacles and Strategies

English reading,a key procedure of English learning and teaching in middle school,is an essential way of getting information from the globe and communicating with others.Therefore,efficient comprehension on reading materials will directly serve for accepting the information.And it is extremely important for middle school students to acquire certain reading ability.At present,great attention has been paid to the course of reading in the instruction of English by the Chinese Education Department.But middle school students'read ing ability is still much lower than the target they should reach because of a set of existing obstacles that prevent them from being effective English readers.What are these obstacles? How to break them down? This paper will first point out and then analyze the obstacles that af fect middle school students'reading ability from two aspects:linguistic obstacles(obstacles of lexicon,grammar and background knowl edge) and non-linguistic obstacles(obstacles of psychology,interest,skill,and habit).After demonstrating different obstacles,the paper also come up with some corresponding strategies in the hope of helping middle school students to be effective English readers.

LOCAL REGULARITY RESULT FOR SOLUTIONS OF OBSTACLE PROBLEMS

This paper gives the local regularity result for solutions to obstacle problems of A-harmonic equation divA(x, ξu(x)) = 0, |A.(x,ξ)|≈|?|p-1, when 1 < p < n and the obstacle function (?)≥0.

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

Regional ecological security anddiagnosisof obstaclefactors in underdevelopedregions：acasestudy in Yunnan Province,China

In order toclarify regional ecological security status and formation mechanism of regional ecological security barriers in underdeveloped regions of China,we took Yunnan province as a case to evaluate its regional ecological security by using entropy matter-element model,comprehensive index and GIS spatial method,and we diagnosed itsobstacle factors through obstacle degree model. We found a low overall level of regional ecological security in Yunnan. Only Kunmingfell into the good level, 68% of the regions were below the critical safe level. For the vast majority of regions in Yunnan, their regional ecological security was unstable. The indexes related to per capita resources, geological and topographyenvironment, economic, and technologywere at the unsafe or dangerous level.The indexes related to urban expansion, level of income, cultivated land quality were at the level of critical safety. The indexes concerning urban management capacity, airqualityand waterenvironment were at the good or ideallevel. Yunnan's regional ecological security was not good due to natural obstructive environment itself, simultaneously lower backward economic and social level restricted the ability of ecological security response to manage ragile ecological environment. The results of the composite index wereroughly consistent with those of the entropy weight matterelement model. The mean values of the classification index,from high to low, were: the state index>the response index>the pressure index. The state index and the response index had a significant mutual promotion to each other.The regions with good composite index, state index and response index mainly distributed in the central regions of Yunnan Province. Spatial autocorrelation of regional ecological security level in Yunnan was not obvious. Water resources, economic and social development were main obstacle factors of the regional ecological security.When distinguishing with obstacle type, Kunming belonged to natural ecological environment barrier type, while other regions belonged to economic and social barrier type.