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.

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.

The Relationship between Overparenting and Adolescent Anxiety:The Mediating Role of Cognitive Avoidance

Background:Adolescent anxiety has a significant impact on physical and mental health,and overparenting is recognized as one of the major factors affecting adolescent anxiety.The objective of this study was to investigate the relationship between overparenting and adolescent anxiety,while also examining the mediating role of cognitive avoidance.Methods:Data were collected through a cross-sectional survey with 1931 valid responses using the Overparenting Scale,the Cognitive Avoidance Scale,and the Anxiety Self-Rating Scale.A structural equation modelling approach was used to test the mediating role of cognitive avoidance between overparenting and adolescent anxiety and to reveal the underlying mechanisms.The significance of the mediating effect was assessed based on maximum likelihood estimation.Differences in the mediating role of cognitive avoidance in the male and female samples were comparatively analyzed in the mediation effect analysis.Results:The study’s findings reveal a significant positive correlation between overparenting and adolescent anxiety(p<0.01),between overparenting and cognitive avoidance(p<0.01),and between cognitive avoidance and adolescent anxiety(p<0.01).Cognitive avoidance mediated the relationship between overparenting and adolescent anxiety.Overparenting can not only directly predict adolescent anxiety but also indirectly predict it through the mediating role of cognitive avoidance.Conclusion:This study validates the direct effect of overparenting on adolescent anxiety and reveals the mechanism of cognitive avoidance as a mediator.

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.

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.

An Investigation of Avoidance of Relative Clauses in the English Writings of High Vocational College Students

This study investigates the avoidance of relative clauses by high vocational college students and also explores the differences in the avoidance of the RCs by high-level students and low-level students in their English writings. The data used in this study have been collected from English writing tasks and analyzed by Statistical Package for Social Sciences(SPSS17.0). The major findings of this study are stated as follows: 1) There are significant differences in the production of the relative clauses in their English writings by Chinese higher vocational college students and English native speakers. Avoidance of relative clauses exists in the English writings of higher vocational college students. 2) High-level Chinese higher vocational college students display less of the avoidance phenomenon in their English writings than low-level students.

Research on Avoidance Behavior in the English Writing of English Majors

This thesis conducts a study of avoidance phenomenon in the English writing of English majors in Shandong University ofTechnology. Instruments used in the study are 180 English writing samples of the 2 ndgrade English majors, questionnaires and in-terviews. Based on the data collected, different kinds of avoidance are analyzed. Further, reasons behind avoidance are exploredand corresponding implications are given.

A Novel Multi-agent Formation Control Law With Collision Avoidance

In this paper a stable formation control law that simultaneously ensures collision avoidance has been proposed.It is assumed that the communication graph is undirected and connected.The proposed formation control law is a combination of the consensus term and the collision avoidance term(CAT).The first order consensus term is derived for the proposed model,while ensuring the Lyapunov stability.The consensus term creates and maintains the desired formation shape,while the CAT avoids the collision.During the collision avoidance,the potential function based CAT makes the agents repel from each other.This unrestricted repelling magnitude cannot ensure the graph connectivity at the time of collision avoidance.Hence we have proposed a formation control law,which ensures this connectivity even during the collision avoidance.This is achieved by the proposed novel adaptive potential function.The potential function adapts itself,with the online tuning of the critical variable associated with it.The tuning has been done based on the lower bound of the critical variable,which is derived from the proposed connectivity property.The efficacy of the proposed scheme has been validated using simulations done based on formations of six and thirty-two agents respectively.

Distributed formation control for a multi-agent system with dynamic and static obstacle avoidances

Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms （formation control term, velocity matching term, and obstacle avoidance term） is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.

Optimal deadlock avoidance Petri net supervisors for automated manufacturing systems

Deadlock avoidance problems are investigated for automated manufacturing systems with flexible routings. Based on the Petri net models of the systems, this paper proposes, for the first time, the concept of perfect maximal resourcetransition circuits and their saturated states. The concept facilitates the development of system liveness characterization and deadlock avoidance Petri net supervisors. Deadlock is characterized as some perfect maximal resource-transition circuits reaching their saturated states. For a large class of manufacturing systems, which do not contain center resources, the optimal deadlock avoidance Petri net supervisors are presented. For a general manufacturing system, a method is proposed for reducing the system Petri net model so that the reduced model does not contain center resources and, hence, has optimal deadlock avoidance Petri net supervisor. The controlled reduced Petri net model can then be used as the liveness supervisor of the system.

Reinforcement Learning Based Obstacle Avoidance for Autonomous Underwater Vehicle

Obstacle avoidance becomes a very challenging task for an autonomous underwater vehicle(AUV)in an unknown underwater environment during exploration process.Successful control in such case may be achieved using the model-based classical control techniques like PID and MPC but it required an accurate mathematical model of AUV and may fail due to parametric uncertainties,disturbance,or plant model mismatch.On the other hand,model-free reinforcement learning(RL)algorithm can be designed using actual behavior of AUV plant in an unknown environment and the learned control may not get affected by model uncertainties like a classical control approach.Unlike model-based control model-free RL based controller does not require to manually tune controller with the changing environment.A standard RL based one-step Q-learning based control can be utilized for obstacle avoidance but it has tendency to explore all possible actions at given state which may increase number of collision.Hence a modified Q-learning based control approach is proposed to deal with these problems in unknown environment.Furthermore,function approximation is utilized using neural network(NN)to overcome the continuous states and large statespace problems which arise in RL-based controller design.The proposed modified Q-learning algorithm is validated using MATLAB simulations by comparing it with standard Q-learning algorithm for single obstacle avoidance.Also,the same algorithm is utilized to deal with multiple obstacle avoidance problems.

Collision Avoidance of Cooperative Dual Redundant Manipulators

Dual redundant manipulators are extremely useful for tasks in dangerous or space environments, but efficient and real time coordinated control is hard to achieve. Collision avoidance between two cooperative manipulators is vital to the successful applications of dual redundant manipulators. Although methods based on the distance function have been demonstrated simple and efficient, different collision avoidance points can usually produce completely different results and even failure. The paper discussed the choices of collision avoidance points and proposed a novel method for the choosing of those points. The method is testified by simulation results of two redundant planar manipulators.

The molecular mechanism of shade avoidance in crops--How data from Arabidopsis can help to identify targets for increasing yield and biomass production

In order to prevent or counteract shading,plants enact a complex set of growth and developmental adaptations when they sense a change in light quality caused by other plants in their vicinity.This shade avoidance response（SAR）typically includes increased stem elongation at the expense of plant fitness and yield,making it an undesirable trait in an agricultural context.Manipulating the molecular factors involved in SAR can potentially improve productivity by increasing tolerance to higher planting density.However,most of the investigations of the molecular mechanism of SAR have been carried out in Arabidopsis thaliana,and it is presently unclear in how far results of these investigations apply to crop plants.In this review,current data on SAR in crop plants,especially from members of the Solanaceae and Poaceae families,are integrated with data from Arabidopsis,in order to identify the most promising targets for biotechnological approaches.Phytochromes,which detect the change in light caused by neighboring plants,and early signaling components can be targeted to increase plant productivity.However,they control various photomorphogenic processes not necessarily related to shade avoidance.Transcription factors involved in SAR signaling could be better targets to specifically enhance or suppress SAR.Knowledge integration from Arabidopsis and crop plants also indicates factors that could facilitate the control of specific aspects of SAR.Candidates are provided for the regulation of plant architecture,flowering induction and carbohydrate allocation.Yet to-be-elucidated factors that control SAR-dependent changes in biotic resistance and cell wall composition are pointed out.This review also includes an analysis of publicly available gene expression data for maize to augment the sparse molecular data available for this important species.

Local Path Planning and Tracking Control of Vehicle Collision Avoidance System

Automotive collision avoidance technology can effectively avoid the accidents caused by dangerous traffic conditions or driver's manipulation errors.Moreover,it can promote the development of autonomous driving for intelligent vehicle in intelligent transportation.We present a collision avoidance system,which is composed of an evasive trajectory planner and a path following controller.Considering the stability of the vehicle in the conflict-free process,the evasive trajectory planner is designed by polynomial parametric method and optimized by genetic algorithm.The path following controller is proposed to make the car drive along the designed path by controlling the vehicle's lateral movement.Simulation results show that the vehicle with the proposed controller has good stability in the collision process,and it can ensure the vehicle driving in accordance with the planned trajectory at different speeds.The research results can provide a certain basis for the research and development of automotive collision avoidance technology.

Saturation attack based route planning and threat avoidance algorithm for cruise missiles

According to the characteristic of cruise missiles,navigation point setting is simplified,and the principle of route planning for saturation attack and a concept of reference route are put forward.With the help of the shortest-tangent idea in route-planning and the algorithm of back reasoning from targets,a reference route algorithm is built on the shortest range and threat avoidance.Then a route-flight-time algorithm is built on navigation points.Based on the conditions of multi-direction saturation attack,a route planning algorithm of multi-direction saturation attack is built on reference route,route-flight-time,and impact azimuth.Simulation results show that the algorithm can realize missiles fired in a salvo launch reaching the target simultaneously from different directions while avoiding threat.

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.

Potential-based obstacle avoidance in formation control

Based on the double integrator mathematic model, a new kind of potential function is presented in this paper by referring to the concepts of the electric field; then a new formation control method is proposed, in which the potential functions are used between agent-agent and between agent-obstacle, while state feedback control is applied for the agent and its goal. This strategy makes the whole potential field simpler and helps avoid some local minima. The stability of this combination of potential functions and state feedback control is proven. Some simulations are presented to show the rationality of this control method.

Radar-Based Collision Avoidance for Unmanned Surface Vehicles'

Unmanned surface vehicles （USVs） have become a focus of research because of their extensive applications. To ensure safety and reliability and to perform complex tasks autonomously, USVs are required to possess accurate perception of the environment and effective collision avoidance capabilities. To achieve these, investigation into real- time marine radar target detection and autonomous collision avoidance technologies is required, aiming at solving the problems of noise jamming, uneven brightness, target loss, and blind areas in marine radar images. These technologies should also satisfy the requirements of real-time and reliability related to high navigation speeds of USVs. Therefore, this study developed an embedded collision avoidance system based on the marine radar, investigated a highly real-time target detection method which contains adaptive smoothing algorithm and robust segmentation algorithm, developed a stable and reliable dynamic local environment model to ensure the safety of USV navigation, and constructed a collision avoidance algorithm based on velocity obstacle （V-obstacle） which adjusts the USV＇s heading and speed in real-time. Sea trials results in multi-obstacle avoidance firstly demonstrate the effectiveness and efficiency of the proposed avoidance system, and then verify its great adaptability and relative stability when a USV sailing in a real and complex marine environment. The obtained results will improve the intelligent level of USV and guarantee the safety of USV independent sailing.

Nonlinear control of spacecraft formation flying with disturbance rejection and collision avoidance

A nonlinear controller for disturbances rejection and collision avoidance is proposed for spacecraft formation flying.The formation flying is described by a nonlinear model with the J2 perturbation and atmospheric drag. Based on the theory of the state-dependent Riccati equation（SDRE）, a finite time nonlinear control law is developed for the nonlinear dynamics involved in formation flying. Then, a compensative internal mode（IM） control law is added to eliminate disturbances.These two control laws compose a finite time nonlinear tracking controller with disturbances rejection. Moreover, taking safety requirements into account, the repulsive control law is incorporated in the composite controller to perform collision avoidance manoeuvres. A numerical simulation is presented to demonstrate the effectiveness of the proposed method.Compared to the conventional control method, the proposed method provides better performance in the presence of the obstacles and external disturbances.

Target Tracking and Obstacle Avoidance for Multi-agent Systems

This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems. To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic environment, a novel control algorithm based on potential function and behavior rules is proposed. Meanwhile, the interactions among agents are also considered. According to the state whether an agent is within the area of its neighbors＇ influence, two kinds of potential functions are presented. Meanwhile, the distributed control input of each agent is determined by relative velocities as well as relative positions among agents, target and obstacle. The maximum linear speed of the agents is also discussed. Finally, simulation studies are given to demonstrate the performance of the proposed algorithm.