scholarly journals An Efficient Path Planning Algorithm for Networked Robots using Modified Optimization Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 2738-2743
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Object Identification ◽  
Planning Problem ◽  
Networked Robots ◽  
Design Choice ◽  
Planning Algorithm ◽  
3D Cnn ◽  
Path Planning Algorithm

Path planning has played a significant role in major numerous decision-making techniques through an automatic system involved in numerous military applications. In the last century, pathfinding and generation were carried out by multiple intelligent approaches. It is very difficult in pathfinding to reduce energy. Besides suggesting the shortest path, it has been found that optimal path planning. This paper introduces an efficient path planning algorithm for networked robots using modified optimization algorithms in combination with the η3 -splines. A new method has employed a cuckoo optimization algorithm to handle the mobile robot path planning problem. At first, η3 - splines are combined so an irregular set of points can be included alongside the kinematic parameters chosen to relate with the development and the control of mobile robots. The proposed algorithm comprises of adaptive random fluctuations (ARFs), which help to deal with the very much manageable neighborhood convergence. This algorithm carries out the process of accurate object identification along with analyzing the influence of different design choice by developing a 3D CNN architecture to determine its performance. Besides offering classification in real-time applications, the proposed algorithm outperforms the performance of state of the art in different benchmarks

scholarly journals Review on path planning algorithm for unmanned aerial vehicles

2021 ◽  
Vol 24 (2) ◽  
pp. 1017
Author(s):  
Nurul Saliha Amani Ibrahim ◽  
Faiz Asraf Saparudin
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.

A Geometric Path-Planning Algorithm in Cluttered Planar Environments Using Convex Hulls

2018 ◽  
Author(s):  
Nafiseh Masoudi ◽  
Georges Fadel
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Visibility Graph ◽  
Convex Hulls ◽  
Planning Problem ◽  
Wire Routing ◽  
Visibility Algorithm ◽  
Planning Algorithm ◽  
Global Optimal ◽  
Path Planning Algorithm

The problem of finding a collision free path in an environment occupied by obstacles, known as path planning, has many applications in design of complex systems such as wire routing in automobile assemblies or motion planning for robots. Developing the visibility graph of the workspace is among the first techniques to address the path-planning problem. The visibility algorithm is efficient in finding the global optimal path. However, it is computationally expensive as it explores the entire workspace of the problem to create all non-intersecting segments of the graph. In this paper, we propose an algorithm based on the notion of convex hulls to generate the partial visibility graph from a given start point to a goal point in a 2D workspace cluttered with a number of disjoint polygonal convex or concave obstacles. The algorithm facilitates the attainment of the shortest path in a planar workspace while reducing the size of the visibility graph to explore.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

Research on pull-type multi-AGV system dynamic path optimization based on time window

2021 ◽  
pp. 095440702098271
Author(s):  
Hongying Shan ◽  
Chuang Wang ◽  
Cungang Zou ◽  
Mengyao Qin
Keyword(s):  
Path Planning ◽  
Time Window ◽  
Planning Problem ◽  
Dijkstra Algorithm ◽  
Before And After ◽  
Common Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Set Up ◽  
Path Planning Algorithm

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Robotica ◽  
2021 ◽  
pp. 1-30
Author(s):  
Ümit Yerlikaya ◽  
R.Tuna Balkan
Keyword(s):  
Path Planning ◽  
Extreme Environment ◽  
Configuration Spaces ◽  
Planning Problem ◽  
Different Types ◽  
Planning Algorithm ◽  
Line Path ◽  
Path Planning Algorithm ◽  
Path Planning Problem ◽  
System Operating

Abstract Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.

Optimal Collision-Free Path Planning for an Autonomous Multi-Wheeled Combat Vehicle

2017 ◽  
Author(s):  
Amr Mohamed ◽  
Moustafa El-Gindy ◽  
Jing Ren ◽  
Haoxiang Lang
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Vehicle Model ◽  
Optimal Path Planning ◽  
Combat Vehicle ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper presents an optimal collision-free path planning algorithm of an autonomous multi-wheeled combat vehicle using optimal control theory and artificial potential field function (APF). The optimal path of the autonomous vehicle between a given starting and goal points is generated by an optimal path planning algorithm. The cost function of the path planning is solved together with vehicle dynamics equations to satisfy the vehicle dynamics constraints and the boundary conditions. For this purpose, a simplified four-axle bicycle model of the actual vehicle considering the vehicle body lateral and yaw dynamics while neglecting roll dynamics is used. The obstacle avoidance technique is mathematically modeled based on the proposed sigmoid function as the artificial potential field method. This potential function is assigned to each obstacle as a repulsive potential field. The inclusion of these potential fields results in a new APF which controls the steering angle of the autonomous vehicle to reach the goal point. A full nonlinear multi-wheeled combat vehicle model in TruckSim software is used for validation. This is done by importing the generated optimal path data from the introduced optimal path planning MATLAB algorithm and comparing lateral acceleration, yaw rate and curvature at different speeds (9 km/h, 28 km/h) for both simplified and TruckSim vehicle model. The simulation results show that the obtained optimal path for the autonomous multi-wheeled combat vehicle satisfies all vehicle dynamics constraints and successfully validated with TruckSim vehicle model.

scholarly journals NPQ-RRT ∗ : An Improved RRT ∗ Approach to Hybrid Path Planning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zihan Yu ◽  
Linying Xiang
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Research Direction ◽  
Random Trees ◽  
Target Point ◽  
Evaluation Function ◽  
Local Planning ◽  
Planning Algorithm ◽  
Window Approach ◽  
Path Planning Algorithm

In recent years, the path planning of robot has been a hot research direction, and multirobot formation has practical application prospect in our life. This article proposes a hybrid path planning algorithm applied to robot formation. The improved Rapidly Exploring Random Trees algorithm PQ-RRT ∗ with new distance evaluation function is used as a global planning algorithm to generate the initial global path. The determined parent nodes and child nodes are used as the starting points and target points of the local planning algorithm, respectively. The dynamic window approach is used as the local planning algorithm to avoid dynamic obstacles. At the same time, the algorithm restricts the movement of robots inside the formation to avoid internal collisions. The local optimal path is selected by the evaluation function containing the possibility of formation collision. Therefore, multiple mobile robots can quickly and safely reach the global target point in a complex environment with dynamic and static obstacles through the hybrid path planning algorithm. Numerical simulations are given to verify the effectiveness and superiority of the proposed hybrid path planning algorithm.

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