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.

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.

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.

Improved RRT^(∗)Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments

A new and improved RRT∗algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm.This algorithm enables the robot to avoid obstacles,find the optimal path,and complete automatic charging docking.It maintains the global completeness and path optimality of the RRT algorithmwhile also improving the iteration speed and quality of generated paths in both 2D and 3D path planning.After finding the optimal path,the B-sample curve is used to optimize the rough path to create a smoother and more optimal path.In comparison experiments,the new algorithmyielded reductions of 35.5%,29.2%,and 11.7%in search time and 22.8%,19.2%,and 9%in path length for the 3D environment.Finally,experimental validation of the automatic charging of electric vehicles was conducted to further verify the effectiveness of the algorithm.The simulation experimental validation was carried out by kinematic modeling and building an experimental platform.The error between the experimental results and the simulation results is within 10%.The experimental results show the effectiveness and practicality of the algorithm.

Co-Frequency Interference Analysis and Avoidance Between NGSO Constellations: Challenges, Techniques, and Trends

In recent years,as giant satellite constellations grow rapidly worldwide,the co-existence between constellations has been widely concerned.In this paper,we overview the co-frequency interference(CFI)among the giant non-geostationary orbit(NGSO)constellations.Specifically,we first summarize the CFI scenario and evaluation index among different NGSO constellations.Based on statistics about NGSO constellation plans,we analyse the challenges in mitigation and analysis of CFI.Next,the CFI calculation methods and research progress are systematically sorted out from the aspects of interference risk analysis framework,numerical calculation and link construction.Then,the feasibility of interference mitigation technologies based on space,frequency domain isolation,power control,and interference alignment mitigation in the NGSO mega-constellation CFI scenario are further sorted out.Finally,we present promising directions for future research in CFI analysis and CFI avoidance.

FSE2R:An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocolin USN

The 3D Underwater Sensor Network(USNs)has become the most optimistic medium for tracking and monitoring underwater environment.Energy and collision are two most critical factors in USNs for both sparse and dense regions.Due to harsh ocean environment,it is a challenge to design a reliable energy efficient with collision free protocol.Diversity in link qualities may cause collision and frequent communication lead to energy loss;that effects the network performance.To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing(FSE2R)is proposed.Our proposal’s key idea is based on computation of node distance from the sink,Residual Energy(RE)of each node and Signal to Interference Noise Ratio(SINR).The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision.The novel proposal compares with existing protocols like H2AB,DEEP,and E2LR to achieve Quality of Service(QoS)in terms of through-put,packet delivery ratio and energy consumption.The comparative analysis shows that FSE2R gives on an average 30%less energy consumption,24.62%better PDR and 48.31%less end-to-end delay compared to other protocols.

Circular Formation Control with Collision Avoidance Based on Probabilistic Position

In this paper,we study the circular formation problem for the second-order multi-agent systems in a plane,in which the agents maintain a circular formation based on a probabilistic position.A distributed hybrid control protocol based on a probabilistic position is designed to achieve circular formation stabilization and consensus.In the current framework,the mobile agents follow the following rules:1)the agent must follow a circular trajectory;2)all the agents in the same circular trajectory must have the same direction.The formation control objective includes two parts:1)drive all the agents to the circular formation;2)avoid a collision.Based on Lyapunov methods,convergence and stability of the proposed circular formation protocol are provided.Due to limitations in collision avoidance,we extend the results to LaSalle’s invariance principle.Some theoretical examples and numerical simulations show the effectiveness of the proposed scheme.

Tax avoidance and earnings management:a neural network approach for the largest European economies

In this study,we investigate the relationship between tax avoidance and earnings management in the largest five European Union economies by using artificial neural network regressions.This methodology allows us to deal with nonlinearities detected in the data,which is the principal contribution to the previous literature.We ana-lyzed Compustat data for Germany,the United Kingdom,France,Italy,and Spain for the 2006–2015 period,focusing on discretionary accruals.We considered three tax avoidance measures,two based on the effective tax rate(ETR)and one on book-tax differences(BTD).Our results indicate the presence of nonlinear patterns and a posi-tive,statistically significant relationship between discretionary accruals and both ETR indicators implying that when companies resort to earnings management,a larger tax-able income—and thus higher ETR and lesser tax avoidance–would ensue.Hence,as also highlighted by the fact that discretionary accruals do not appear to affect BTD,our evidence does not suggest that companies are exploiting tax manipulation to reduce their tax payments;thus,the gap between accounting and taxation seems largely unaf-fected by earnings management.

Research on Obstacle Avoidance Method of Intelligent Car Based on Optimized Fuzzy Control Algorithm

In order to realize the accurate obstacle avoidance function of intelligent car, we propose an intelligent car obstacle avoidance system based on optimized fuzzy control algorithm. Firstly, the kinematics model of intelligent car obstacle avoidance is established, and an efficient environment information collection system composed of multiple sensors is designed to realize the comprehensive collection of obstacle information. Then, the optimized fuzzy control system is adopted to improve the position control accuracy and obstacle avoidance ability. Through the physical debugging and joint simulation of the intelligent car fuzzy controller in the MATLAB and Simulink environment, the simulation results show that the control method can make the collision-free path planned by the intelligent car from the initial state to the obstacle avoidance smoother, and at the same time, the obstacle avoidance of the intelligent car. The actual running distance is reduced by about 16%, which can ensure the practicability of the obstacle avoidance system, provide a new guarantee for the safe operation of the car, and also provide a new idea for the development of the unmanned car.

Research on Obstacle Avoidance Method of Intelligent Car Based on Optimized Fuzzy Control Algorithm

In order to realize the accurate obstacle avoidance function of intelligent car, we propose an intelligent car obstacle avoidance system based on optimized fuzzy control algorithm. Firstly, the kinematics model of intelligent car obstacle avoidance is established, and an efficient environment information collection system composed of multiple sensors is designed to realize the comprehensive collection of obstacle information. Then, the optimized fuzzy control system is adopted to improve the position control accuracy and obstacle avoidance ability. Through the physical debugging and joint simulation of the intelligent car fuzzy controller in the MATLAB and Simulink environment, the simulation results show that the control method can make the collision-free path planned by the intelligent car from the initial state to the obstacle avoidance smoother, and at the same time, the obstacle avoidance of the intelligent car. The actual running distance is reduced by about 16%, which can ensure the practicability of the obstacle avoidance system, provide a new guarantee for the safe operation of the car, and also provide a new idea for the development of the unmanned car.

Driver Intent Prediction and Collision Avoidance With Barrier Functions

For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.

Cost-effective distributed FTFC for uncertain nonholonomic mobile robot fleet with collision avoidance and connectivity preservation

In this paper,the fault-tolerant formation control(FTFC)problem is investigated for a group of uncertain nonholonomic mobile robots with limited communication ranges and unpredicted actuator faults,where the communication between the robots is in a directed one-to-one way.In order to guarantee the connectivity preservation and collision avoidance among the robots,some properly chosen performance functions are incorporated into the controller to per-assign the asymmetrical bounds for relative distance and bearing angle between each pair of adjacent mobile robots.Particularly,the resultant control scheme remains at a costeffective level because its design does not use any velocity information from neighbors,any prior knowledge of system nonlinearities or any nonlinear approximator to account for them despite the presence of modeling uncertainties,unknown external disturbances,and unexpected actuator faults.Meanwhile,each follower is derived to track the leader with the tracking errors regarding relative distance and bearing angle subject to prescribed transient and steady-state performance guarantees,respectively.Moreover,all the closed-loop signals are ensured to be ultimately uniformly bounded.Finally,a numerical example is simulated to verify the effectiveness of this methodology.

A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy(IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy.The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.

Summarizing vehicle driving decision-making methods on vulnerable road user collision avoidance

With the development of intelligent vehicles and autonomous driving technology,the safety of vulnerable road user(VRU)in traffic has been more guaranteed,and many research achievements have been made in the key area of collision avoidance decision-making methods.In this paper,the knowledge mapping method is used to mine the available literature in depth,and it is found that the research focus has shifted from the traditional accident cause analysis to emerging deep learning and virtual reality technology.This paper summarizes research on the three core dimensions of environmental perception,behavior cognition and collision avoidance decision-making in intelligent vehicle systems.In terms of perception,accurate identification of pedestrians and cyclists in complex environments is a major demand for VRU perception;in terms of behavior cognition,the coupling of VRU intention identification and motion trajectory prediction and other multiple factors needs further research;in terms of decision-making,the intention identification and trajectory prediction of collision objects are not included in the risk assessment model,and there is a lack of exploration specifically for cyclists'collision risk.On this basis,this paper provides guidance for the improvement of traffic safety of contemporary VRU under the conditions of intelligent and connected transportation.

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.

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.