一种基于人工场势的FDM薄壁结构打印路径规划方法

针对熔融堆积式(FDM)3D打印机在打印薄壁结构时存在路径规划方法和成型质量不理想等问题,文章提出了一种基于人工场势(Artificial Potential Field,APF)的FDM薄壁结构打印路径规划方法,通过原理论述和试验分析证明其优势。

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.

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.

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.

Trajectory tracking and obstacle avoidance method for robots based on fast terminal sliding mode

For accurate trajectory tracking and obstacle avoidance in finite time of a nonholonomic mobile robot,a trajectory tracking controller based on global fast terminal sliding mode method is proposed,which has the advantages of chattering-free and adjustable convergence time.First of all,the kinematics model of the robot is established in mobile carrier coordinates.Secondly,the global structure including terminal attractor and exponential convergence of the fast terminal sliding mode trajectory tracking controller is proved by Lyapunov stability theory,ensuring that the trajectory and heading angle tracking error converges to a smaller zero range in finite time.Finally,the artificial potential field obstacle avoidance method is introduced to make the robot not only track the reference trajectory strictly,but also avoid the obstacles.The simulation results show that the proposed method can achieve a stable tracking control in finite time for a given reference trajectory.

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.

Using Successive Multi-Goals Method in the Individual-Based Model to Simulate Crowd Behavior during Evacuation Processes

The fact of proportional population growth in many countries drags the attention of researchers in the field of crowd dynamics to the need for developing reliable models to predict the behavior of human crowds in emergency situations such as evacuation processes. Computer based models that simulate human crowd dynamics prove to offer the optimum way to predict the crowd realistic behavior especially in emergency situations. This paper presents a vital extension of my previous work in which an individual-based model to simulate the behavior of human crowd was developed using the artificial potential fields to describe the interaction forces between each crowd member and the environment on one side and amongst the crowd members on the other side to add realistic flavor to the predicted crowd behavior. In this paper, the successive multi-goals （SMG） method, which is a new method to represent the environment in which the crowd moves, is developed. Rather than using the traditional static potential field, the successive multi-goals method uses a dynamic potential field which is vital to solve the reactive problem that is considered as a drawback of the model when simulating the human crowd behavior during evacuation of buildings whose structures are complex such as bottlenecks and narrow corridors. Numerical results that match the real behavior of human individuals in emergency situations prove the efficiency of the new method to solve the problem on an individual basis as well as its applicability.