scholarly journals Dynamic Pathfinding for Non-Player Character Follower on Game

2021 ◽  
Vol 13 (2) ◽  
pp. 55-63
Author(s):  
Paulus Harsadi ◽  
Siti Asmiatun ◽  
Astrid Novita Putri
Keyword(s):  
Video Game ◽  
Potential Field ◽  
Computation Time ◽  
Attractive Potential ◽  
Artificial Potential Field ◽  
Field Function ◽  
Particle Chain ◽  
Comparison Algorithms ◽  
Test Result ◽  
Flocking Behavior

Artificial Intellegences in video game are important things that can challenge game player. One of them is creating character or NPC Follower (Non-player character Follower) inside the video game, such as real human/animal attitude. Artificial Intelligences have some techniques in which pathfinding is one of Artificial Intellegence techniques that is more popular in research than other techniques. The ability to do dynamic pathfinding is Dynamic Particle Chain (DPC) algorithm. This algorithm has the ability of flocking behavior called boid to explore the environment. But, the algoritm method moves from one boid’s point to another according to the nearest radius, then it will be able to increase computation time or needed time toward the target. To finish higher computation problem in dynamic pathfinding, the researcher suggests an algorithm that is able to handle dynamic pathfinding process through attractive potential field function of Artificial Potential Field to start pathfinding toward the target and flocking behavior technique to avoid the obstacle. Based on the test result by simulation of moving environment and complex, the computation time of algorithm is faster than comparison algorithms, DPC and Astar. It concludes that the suggested method can be used to decrease computation level in dynamic pathfinding.

scholarly journals UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus

2021 ◽  
Author(s):  
Ning Wang ◽  
Jiyang Dai ◽  
Jin Ying
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Control Algorithm ◽  
Influence Factor ◽  
Order System ◽  
Attractive Potential ◽  
Artificial Potential Field ◽  
Particle Model ◽  
Field Function ◽  
Avoidance Control

AbstractAiming at the problem of UAV formation's obstacle avoidance and the consensus of position and velocity in a 3D obstacle environment, a novel distributed obstacle avoidance control algorithm for cooperative formation based on the improved artificial potential field (IAPF) and consensus theory is proposed in this paper. First, the particle model of the UAV and the dynamic model of the second-order system are established, and the topological structure of the communication network of the system is described with the knowledge of graph theory. Second, the attractive potential field function containing the coordination gains factor, the repulsive potential field function containing the influence factor of the repulsive force and the planning angle, and the potential field function between the UAVs containing the communication weight are defined. Then, the variables of position and velocity in the consensus protocol are improved by the reference vector of the formation center and the expected velocity, respectively, and a new formation obstacle avoidance control protocol is designed by combining the IAPF and the theory of consensus. Finally, the Lyapunov function is used to prove the stable convergence of the algorithm. The simulation results show that this method can not only prevent the UAV from colliding with each other while avoiding static and dynamic obstacles but also enable the UAV to quickly restore the expected formation and achieve the consensus of the relative distance, relative height, and velocity.

Multi-Robots Path Planning Based on Improved Artificial Potential Field Method

2012 ◽  
Vol 562-564 ◽  
pp. 937-940 ◽  
Author(s):  
Yu Lan Hu ◽  
Qi Song Zhang
Keyword(s):  
Gravitational Field ◽  
Path Planning ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Target Point ◽  
Field Function ◽  
Dynamic Movement ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Mobile path planning is a focus area and the key to intelligent technologies in robot. As one of the most basic and important topics the problem of mobile robot path planning solve the trouble that the robot avoid obstacles in the environment and how to successfully reach the destination. On the emergence of case that is the robot can not reach the target point and easy to fall into local minimum .This will be optimized by improving the way repulsive field function, When the robot close to the target point, not only the gravity of the gravitational field continue to reduce but also the repulsion of the repulsive force field has also been decreasing. This would solve the problem that when the robot reach the target point but easy to fall into local minimal solution. In traditional artificial potential field method, the target is static, but due to prey (i.e. target) is dynamic in this article, the traditional artificial potential field of gravitational field function is not suitable for the situation discussed. Therefore this paper puts forward a dynamic movement is based on the goal of the gravitational field of new functions.

scholarly journals 3D Path Planning Method for UAV Based on Improved Artificial Potential Field

2020 ◽  
Vol 38 (5) ◽  
pp. 977-986
Author(s):  
Yicong Guo ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Yue Yang
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Potential Field ◽  
Local Minimum ◽  
Field Method ◽  
Artificial Potential Field ◽  
Field Calculation ◽  
Field Function ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Path planning is the key technology for UAV to achieve autonomous flight. Considering the shortcomings of path planning based on the conventional potential field method, this paper proposes an improved optimization algorithm based on the artificial potential field method and extends it to three-dimensional space to better achieve flight constrained 3D online path planning for UAVs. The algorithm is improved and optimized aiming at the three problems of goal nonreachable with obstacle nearby (GNWON), easy to fall into local minimum, and path oscillation in traditional artificial potential field method. First, an improved potential field function with relative distance is used to solve the GNWON, and an optimized repulsive potential field calculation method based on different obstacles or threat models is proposed to optimize the planned path. Secondly, in order to make the drone jump out of the local minimum trap, a method of setting heuristic sub-target points is proposed. For local path oscillation, a method using memory sum force was proposed to improve the oscillation. The simulation results show that the improved optimization algorithm in this paper effectively makes up for the shortcomings of the traditional artificial potential field method, and the designed 3D online path planning algorithm for the UAV is practical and feasible.

Multi-robot path planning based on improved artificial potential field and fuzzy inference system1

2020 ◽  
Vol 39 (5) ◽  
pp. 7621-7637 ◽  
Author(s):  
Tao Zhao ◽  
Haodong Li ◽  
Songyi Dian
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Fuzzy Inference ◽  
Target Position ◽  
Assessment Method ◽  
Artificial Potential Field ◽  
Collision Risk ◽  
Inference System ◽  
Field Function ◽  
Multi Robot

In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems.

Grid Based Path Planning Using CNN & Artificial Potential Field Method

2013 ◽  
Vol 392 ◽  
pp. 830-836 ◽  
Author(s):  
Shamina Akter ◽  
Deok Jin Lee ◽  
Shin Taek Lim ◽  
Kil To Chong
Keyword(s):  
Image Processing ◽  
Path Planning ◽  
Potential Field ◽  
Planning Process ◽  
Vision System ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Field Function ◽  
Potential Value

This proposed path planning method combines cellular neural network (CNN) with artificial potential field approach. The fundamental operation based on CNN gray scale image processing and artificial potential is the additional approach for global path-planning. Every point of the environment has a potential value with respect to start and destination position. In the trajectory planning process, a minimum search of potential value of every surrounding neighbor points around a point is done and the neighbor point with the least minimum value is selected as the next location. This procedure is repeated until the goal point is reached. The advantage of using CNN based image processing with artificial potential field function in a vision system is its effectiveness in robot localization while the use of minimum potential value gives a simple yet efficient path planning method. Their feedback criterion is similar to a procedure in filtering the image and it frequently updates the information about obstacles and free path. The parallel processing properties of CNN makes the proposed method robust for real time application.

scholarly journals Improved Artificial Potential Field Method Applied for AUV Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaojing Fan ◽  
Yinjing Guo ◽  
Hui Liu ◽  
Bowen Wei ◽  
Wenhong Lyu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Field ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Field Function ◽  
Time Path

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

scholarly journals An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141879956 ◽  
Author(s):  
Wenrui Wang ◽  
Mingchao Zhu ◽  
Xiaoming Wang ◽  
Shuai He ◽  
Junpei He ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Potential Field ◽  
Lyapunov Stability Theory ◽  
Field Method ◽  
Artificial Potential Field ◽  
Redundant Manipulators ◽  
Field Function ◽  
Potential Field Method ◽  
Artificial Potential Field Method

In this article, we present an improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators. Specifically, we not only focused on the position for the manipulator end-effectors but also considered their posture in the course of trajectory planning and obstacle avoidance. We introduced boundaries for Cartesian space components to optimize the attractive field function. Moreover, the manipulator achieved a reasonable speed to move to the target pose, regardless of the difference between the initial pose and target pose. We proved the stability using Lyapunov stability theory by introducing velocity feedforward, when the manipulator moved along a continuous trajectory. Considering the shape of the manipulator joints and obstacles, we set up the collision detection model by projecting the obstacles to link coordinates. In this case, establishing the repulsive field between the nearest points on every joint and obstacles with the closest distance was sufficient for achieving obstacle avoidance for redundant manipulators. The simulation results based on a nine-degree-of-freedom hyper-redundant manipulator, which was designed and made in our laboratory, fully substantiated the efficacy and superiority of the proposed method.

scholarly journals Formation control of UAVs based on artificial potential field

2018 ◽  
Vol 189 ◽  
pp. 10018
Author(s):  
Yongshen Lv ◽  
Xuerong Yang ◽  
Yajun Yang ◽  
Shengdong Pan ◽  
Chaojun Xin
Keyword(s):  
Kinetic Model ◽  
Potential Field ◽  
Formation Control ◽  
Control Algorithm ◽  
Simulation Experiment ◽  
Control Method ◽  
Artificial Potential Field ◽  
Field Function ◽  
Time Performance ◽  
Matching Rules

The problem of UAVs’ formation control in the process of motion is investigated in this paper. A formation control method based on artificial potential field of UAVs is proposed, established on the collision avoidance, aggregation and speed matching rules of UAVs. First establish the UAVs’ kinetic model in accordance to the motion rules, then design the formation control algorithm based on artificial potential field function, which is used to control the formation during the movement of UAVs. Finally, the results of simulation experiment show that the proposed formation control method in this paper is effective and has the advantages of easy realization, good real-time performance and excellent robustness.

Research on Obstacles Avoidance for UAV SLAM

2014 ◽  
Vol 635-637 ◽  
pp. 1329-1334 ◽  
Author(s):  
Li Wang ◽  
Xu Liu ◽  
Heng Xin Wang ◽  
Xi Bin Wang
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Simultaneous Localization And Mapping ◽  
Plane Motion ◽  
Artificial Potential Field ◽  
Motion Model ◽  
Field Function ◽  
Moving Obstacles ◽  
Localization And Mapping

Abstract: When UAV is implementing the simultaneous localization and mapping (SLAM) problem, the environment where UAV is flying exists unavoidable solid or moving obstacles because of its unknown character, which threatens the flying safety and the completeness of SLAM mission. To conquer this problem, an improved artificial potential field algorithm is proposed to simultaneously accomplish obstacle avoidance of UAV and SLAM mission based on a potential field function containing the distance from UAV to the goal and from UAV to the obstacles and the covariance of features. This algorithm is simulated and tested based on the built UAV plane motion model. The result shows that the proposed algorithm is effective to avoid the obstacles while implementing SLAM for UAV.

scholarly journals MGRO Recognition Algorithm-Based Artificial Potential Field for Mobile Robot Navigation

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Ming Pang ◽  
Zhankai Meng ◽  
Wenbo Zhang ◽  
Changhai Ru
Keyword(s):  
Mobile Robot ◽  
Potential Field ◽  
Visual Recognition ◽  
Recognition Algorithm ◽  
Mobile Robot Navigation ◽  
Artificial Potential Field ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Field Function ◽  
Enhancement Method

This paper describes a novel recognition algorithm which includes mean filter, Gaussian filter, Retinex enhancement method, and Ostu threshold segmentation method (MGRO) for the navigation of mobile robots with visual sensors. The approach includes obstacle visual recognition and navigation path planning. In the first part, a three-stage method for obstacle visual recognition is constructed. Stage 1 combines mean filtering and Gaussian filtering to remove random noise and Gauss noise in the environmental image. Stage 2 increases image contrast by using the Retinex enhancement method. Stage 3 uses the Ostu threshold segmentation method to achieve obstacle feature extraction. A navigation method based on the auxiliary visual information is constructed in the second part. The method is based on the artificial potential field (APF) method and is able to avoid falling into local minimum by changing the repulsion field function. Experimental results confirm that obstacle features can be extracted accurately and the mobile robot can avoid obstacles safely and arrive at target positions correctly.

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