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.

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.

New multi-UAV formation keeping method based on improved artificial potential field

Formation keeping is important for multiple Unmanned Aerial Vehicles(multi-UAV)to fully play their roles in cooperative combats and improve their mission success rate.However,in practical applications,it is difficult to achieve formation keeping precisely and obstacle avoidance autonomously at the same time.This paper proposes a joint control method based on robust H∞ controller and improved Artificial Potential Field(APF)method.Firstly,we build a formation flight model based on the “Leader-Follower”structure and design a robust H∞ controller with three channels X,Y and Z to eliminate dynamic uncertainties,so as to realize high-precision formation keeping.Secondly,to fulfill obstacle avoidance efficiently in complex situations where UAVs fly at high speed with high inertia,this paper comes up with the improved APF method with deformation factor considered.The judgment criterion is proposed and applied to ensure flight safety.In the end,the simulation results show that the designed controller is effective with the formation keeping a high accuracy and in the meantime,it enables UAVs to avoid obstacles autonomously and recover the formation rapidly when coming close to obstacles.Therefore,the method proposed here boasts good engineering application prospect.

Simulation research for mobile robot path planning based on improved artificial potential field method recommended by the AsiaSim

Mobile robot path planning is an important research branch in the field of mobile robots.The main disadvantage of the traditional artificial potential field(APF)method is prone to local minima problems.Improved artificial potential field(IAPF)method is presented in this paper to solve the problem in the traditional APF method for robot path planning in different conditions.We introduce the distance between the robot and the target point to the function of the original repulsive force field and change the original direction of the repulsive force to avoid the trap problem caused by the local minimum point.The IAPF method is suitable for mobile robot path planning in the complicated environment.Simulation and experiment results at the robot platform illustrated the superiority of the modified IAPF method.

Ant Colony Optimization with Potential Field Based on Grid Map for Mobile Robot Path Planning

For the mobile robot path planning under the complex environment,ant colony optimization with artificial potential field based on grid map is proposed to avoid traditional ant colony algorithm's poor convergence and local optimum.Firstly,the pheromone updating mechanism of ant colony is designed by a hybrid strategy of global map updating and local grids updating.Then,some angles between the vectors of artificial potential field and the orientations of current grid are introduced to calculate the visibility of eight-neighbor cells of cellular automata,which are adopted as ant colony's inspiring factor to calculate the transition probability based on the pseudo-random transition rule cellular automata.Finally,mobile robot dynamic path planning and the simulation experiments are completed by this algorithm,and the experimental results show that the method is feasible and effective.

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.

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.

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.

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.

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.

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.

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.

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.

Flocking of multi-robot systems with connectivity maintenance on directed graphs

Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields （APF） which could guarantee the connectivity maintenance, col ision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that al robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.

An eikonal equation based path planning method using polygon decomposition and curve evolution

Path planning is a key technique of autonomous navigation for robots,and the velocity field is an important part.Constructing velocity field in a complex workspace is still challenging.In this paper,an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper.The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace,and the velocity field is obtained by utilizing the potential field of this workspace.Path optimization is implemented by curve evolution,during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field.The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness.Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.

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.

A collaborative assembly for low-voltage electrical apparatuses

Low-voltage electrical apparatuses(LVEAs)have many workpieces and intricate geometric structures,and the assembly process is rigid and labor-intensive,and has little balance.The assembly process cannot readily adapt to changes in assembly situations.To address these issues,a collaborative assembly is proposed.Based on the requirements of collaborative assembly,a colored Petri net(CPN)model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly.Also,an artificial potential field based planning algorithm(AFPA)is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs.Then an adaptive quantum genetic algorithm(AQGA)is developed to optimize the assembly process.Lastly,taking a two-pole circuit-breaker controller with leakage protection(TPCLP)as an assembly instance,comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly.The distribution of resources can also be optimized in the assembly.The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.

Research on head way control of a guided transport system based on intersection condition evaluation

ln order to ensure the safety and efficiency of the section tracking operation of a guided transport system,a safety headway control method of section tracking based on intersection conditions is proposed in this paper.Considering the difference of signal phase,the evaluation model of road conditions was established based on a fuzzy comprehensive evaluation method,FAGT.Based on the artificial potential field method,the time-varying hybrid artificial potential field(TH-APF)method was proposed,and the tracking headway control algorithm was designed to realize the dynamic control of the tracking headway of the guide transport vehicle.The simulation results verified the effectiveness and applicability of the evaluation model of intersection road conditions;the tracking headway can be maintained at about 120 s.The tracking headway control algorithm of guided transport vehicles can respond to the road conditions and avoid the local optimum of the artificial potential field method,thus improving the operating efficiency.

UAV Swarm Control Based on Hybrid Bionic Swarm Intelligence

Inspired by the pigeon behavior pattern,this paper proposes an Unmanned Aerial Vehicle(UAV)swarm control scheme based on hybrid bionic sw arm intelligence,which can realize multi-UAV obstacle avoidance d uring formation control.First,the leadership mechanism of pigeon flock is mapped to UAV swamm,and the virtual leaders are introduced to solve the unfixed relative position of level-1 leader problem.Second,the control law for UAV swarm formation is designed based on artificial potential field theory and analysis of the bionic mechanism.To avoid local minima,a guidance phase is added to the UAV formation process.By analyzing the flocking algorithm,a cooperative interaction control model of UAV swarm is established.Third,the coopentive interactive control law for UAV sw arm obstacle avoidance is proposed based on improved artifcial potential feld function.Then the two bionic swarm control models are combined to realize the formation and obstacle avoidance of UAV swarm based on mixed bionic swarm intelligence.Finally,a series of simulations are conducted to demonstrate the proposed hybrid UAV sw arm control algorithm.