Game theory based finite-time formation control using artificial potentials for tethered space net robot

The Tethered Space Net Robot(TSNR)is an innovative solution for active space debris capture and removal.Its large envelope and simple capture method make it an attractive option for this task.However,capturing maneuverable debris with the flexible and elastic underactuated net poses significant challenges.To address this,a novel formation control method for the TSNR is proposed through the integration of differential game theory and robust adaptive control in this paper.Specifically,the trajectory of the TSNR is obtained through the solution of a real-time feedback pursuit-evasion game with a dynamic target,where the primary condition is to ensure the stability of the TSNR.Furthermore,to minimize tracking errors and maintain a specific configuration,a robust adaptive formation control scheme with Artificial Potential Field(APF)based on a Finite-Time Convergent Extended State Observer(FTCESO)is investigated.The proposed control method has a key advantage in suppressing complex oscillations by a new adaptive law,thus precisely maintaining the configuration.Finally,numerical simulations are performed to demonstrate the effectiveness of the proposed scheme.

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.

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

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.

Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.

Cooperative Search of UAV Swarm Based on Ant Colony Optimization with Artificial Potential Field

An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.

Collision avoidance planning in multi-robot system based on improved artificial potential field and rules

For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method,so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots,collisions between robots can be detected,thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-based simulated environment.

Solution to reinforcement learning problems with artificial potential field

A novel method was designed to solve reinforcement learning problems with artificial potential field.Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF),which was a very appropriate method to model a reinforcement learning problem.Secondly,a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept.The performance of this new method was tested by a gridworld problem named as key and door maze.The experimental results show that within 45 trials,good and deterministic policies are found in almost all simulations.In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution,the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning.Therefore,the new method is simple and effective to give an optimal solution to the reinforcement learning problem.

Local minima-free design of artificial coordinating fields

In order to overcome the drawbacks of conventional artificial potential fields (APF) based methods for the motion planning problems of mobile robots in dynamic uncertain environments, an artificial coordinating fields (ACF) based method has been proposed recently. This paper deals with the reachability problem of the ACF, that is, how to design and choose the parameters of the ACF and how the environment should be such that the robot can reach its goal without being trapped in local minima. Some sufficient conditions for these purposes are developed theoretically. Theoretical analyses show that, the ACF can effectively remove local minima in dynamic uncertain environments with V-shape or U-shape obstacles, and guide the mobile robot to reach its goal with some necessary environment constraints and based on the methods provided in this paper to properly choose the parameters of the ACF. Comparisons between the ACF and APF, and simulations are provided to illustrate the advantages of the ACF.

Mobile robot path planning method combined improved artificial potential field with optimization algorithm

To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.

NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

A novel approach for collision-free path planning of a multiple degree-of-freedom （DOF） articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point （VNP）, a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

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.

Region coverage control for multiple stratospheric airships with combined self-/event-triggered mechanism

The region coverage control problem of multiple stratospheric airships system is firstly addressed in this paper.Towards it,we propose a two-layer control framework with the artificial potential field(APF)-based region coverage control law and the adaptive tracking control law.The APF-based region coverage control law ensures the coverage task is achieved until every single stratospheric airship ends up performing station keeping where near the respective global minimum point,in which an innovative solution to the local minimum problem is put forward.The adaptive tracking control law is designed to realize motion control using tracking the desired velocity and angular velocity given by coverage control law,with the consideration of several practical control problems as unknown individual differences and external disturbances.To save resources,the combined self-/event-triggered mechanism designed therein significantly reduces the times of state information transmission and control law calculation.The effectiveness of the proposed control framework is verified through simulations.

Intelligent decision-making method for vehicles in emergency conditions based on artificial potential fields and finite state machines

This study aims to propose a decision-making method based on artificial potential fields(APFs)and finite state machines(FSMs)in emergency conditions.This study presents a decision-making method based on APFs and FSMs for emergency conditions.By modeling the longitudinal and lateral potential energy fields of the vehicle,the driving state is identified,and the trigger conditions are provided for path planning during lane changing.In addition,this study also designed the state transition rules based on the longitudinal and lateral virtual forces.It established the vehicle decision-making model based on the finite state machine to ensure driving safety in emergency situations.To illustrate the performance of the decision-making model by considering APFs and finite state machines.The version of the model in the co-simulation platform of MATLAB and CarSim shows that the developed decision model in this study accurately generates driving behaviors of the vehicle at different time intervals.The contributions of this study are two-fold.A hierarchical vehicle state machine decision model is proposed to enhance driving safety in emergency scenarios.Mathematical models for determining the transition thresholds of lateral and longitudinal vehicle states are established based on the vehicle potential field model,leading to the formulation of transition rules between different states of autonomous vehicles(AVs).

An Optimal Control Strategy for Multi-UAVs Target Tracking and Cooperative Competition

An optimal control strategy of winner-take-all(WTA)model is proposed for target tracking and cooperative competition of multi-UAVs(unmanned aerial vehicles).In this model,firstly,based on the artificial potential field method,the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets.Secondly,according to the finite-time convergence high-order differentiator,a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory.Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking,high precision,strong stability and avoiding chattering.Finally,a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy.Theoretical analysis and numerical simulation results show that the model has the fast convergence,high control accuracy,strong stability and good robustness.

Path planning for light-induced dielectrophoretic manipulation of micro-particles

To realize automatic manipulation of micro-particles by light-induced dielectrophoresis （LDEP）, a path-planning scheme based on the improved artificial potential field （APF） for micro light pattern movements is proposed. An algorithm combining guided target and point obstacle based on a new local minimum judging criterion is specially designed, which can solve the local minimum problems encountered by the traditional APF. Experiments of real-time particle manipulation based on this algorithm are implemented and the experimental results show that the proposed approach can overcome the local minimum problems of the traditional APF method, and it is validated to be highly stable for intensive particle obstacles during LDEP manipulation. Consequently, this method can realize real-time manipulation of micro-nano particles with safety, decrease the difficulty of manual manipulation, and thus improve the efficiency of manipulation of micro-particles.

Hybrid optimization of dynamic deployment for networked fire control system

With applying the information technology to the military field, the advantages and importance of the networked combat are more and more obvious. In order to make full use of limited battlefield resources and maximally destroy enemy targets from arbitrary angle in a limited time, the research on firepower nodes dynamic deployment becomes a key problem of command and control. Considering a variety of tactical indexes and actual constraints in air defense, a mathematical model is formulated to minimize the enemy target penetration probability. Based on characteristics of the mathematical model and demands of the deployment problems, an assistance-based algorithm is put forward which combines the artificial potential field （APF） method with a memetic algorithm. The APF method is employed to solve the constraint handling problem and generate feasible solutions. The constrained optimization problem transforms into an optimization problem of APF parameters adjustment, and the dimension of the problem is reduced greatly. The dynamic deployment is accomplished by generation and refinement of feasible solutions. The simulation results show that the proposed algorithm is effective and feasible in dynamic situation.

Coordinate Control,Motion Optimization and Sea Experiment of a Fleet of Petrel-Ⅱ Gliders

The formation of hybrid underwater gliders has advantages in sustained ocean observation with high resolution and more adaptation for complicated ocean tasks. However, the current work mostly focused on the traditional gliders and AUVs.The research on control strategy and energy consumption minimization for the hybrid gliders is necessary both in methodology and experiment. A multi-layer coordinate control strategy is developed for the fleet of hybrid underwater gliders to control the gliders’ motion and formation geometry with optimized energy consumption. The inner layer integrated in the onboard controller and the outer layer integrated in the ground control center or the deck controller are designed. A coordinate control model is proposed based on multibody theory through adoption of artificial potential fields. Considering the existence of ocean flow, a hybrid motion energy consumption model is constructed and an optimization method is designed to obtain the heading angle, net buoyancy, gliding angle and the rotate speed of screw propeller to minimize the motion energy with consideration of the ocean flow. The feasibility of the coordinate control system and motion optimization method has been verified both by simulation and sea trials. Simulation results show the regularity of energy consumption with the control variables. The fleet of three Petrel-Ⅱ gliders developed by Tianjin University is deployed in the South China Sea. The trajectory error of each glider is less than 2.5 km, the formation shape error between each glider is less than 2 km, and the difference between actual energy consumption and the simulated energy consumption is less than 24% actual energy. The results of simulation and the sea trial prove the feasibility of the proposed coordinate control strategy and energy optimization method. In conclusion, a coordinate control system and a motion optimization method is studied, which can be used for reference in theoretical research and practical fleet operation for both the traditional gliders and hybrid gliders.

Rollover Prevention and Motion Planning for an Intelligent Heavy Truck

It is very necessary for an intelligent heavy truck to have the ability to prevent rollover independently.However,it was rarely considered in intelligent vehicle motion planning.To improve rollover stability,a motion planning strategy with autonomous anti rollover ability for an intelligent heavy truck is put forward in this paper.Considering the influence of unsprung mass in the front axle and the rear axle and the body roll stiffness on vehicle rollover stability,a rollover dynamics model is built for the intelligent heavy truck.From the model,a novel rollover index is derived to evaluate vehicle rollover risk accurately,and a model predictive control algorithm is applicated to design the motion planning strategy for the intelligent heavy truck,which integrates the vehicle rollover stability,the artificial potential field for the obstacle avoidance,the path tracking and vehicle dynamics constrains.Then,the optimal path is obtained to meet the requirements that the intelligent heavy truck can avoid obstacles and drive stably without rollover.In addition,three typical scenarios are designed to numerically simulate the dynamic performance of the intelligent heavy truck.The results show that the proposed motion planning strategy can avoid collisions and improve vehicle rollover stability effectively even under the worst driving scenarios.