scholarly journals UAV Path Planning under Dynamic Threats Using an Improved PSO Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jong-Jin Shin ◽  
Hyochoong Bang
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Computation Time ◽  
Pso Algorithm ◽  
Cost Evaluation ◽  
Processing Unit ◽  
Dijkstra Algorithm ◽  
Flight Direction ◽  
Map Generation ◽  
The Cost

This paper presents the method to solve the problem of path planning for an unmanned aerial vehicle (UAV) in adversarial environments including radar-guided surface-to-air missiles (SAMs) and unknown threats. SAM lethal envelope and radar detection for SAM threats and line-of-sight (LOS) calculation for unknown threats are considered to compute the cost for path planning. In particular, dynamic SAM lethal envelope is taken into account for path planning in that SAM lethal envelope does change its direction according to the flight direction of UAV. In addition, terrain masking, nonisotropic radar cross section (RCS), and dynamic constraints of UAV are considered to determine the cost of the path. An improved particle swarm optimization (PSO) algorithm is proposed for finding an optimal path. The proposed algorithm is composed of preprocessing steps, multi-swarm PSO algorithm, and postprocessing steps. The Voronoi diagram and Dijkstra algorithm as preprocessing steps provide the initial path for the multi-swarm PSO algorithm which uses multiple swarms with sub-swarms for the balance between exploration and exploitation. Postprocessing steps include waypoint insertion and 3D path smoothing. The computation time is reduced by using the map generation, the coordinate transformation, and the graphic processing unit (GPU) implementation of the algorithm. Various simulations are carried out to compare the performance of the proposed method according to the number of iterations, the number of swarms, and the number of cost evaluation points. The t -test results show that the suggested method is statistically better than existing methods.

Path Planning for Rolling Locomotion of Polyhedral Tensegrity Robots Based on Dijkstra Algorithm

2019 ◽  
Vol 60 (4) ◽  
pp. 273-286 ◽  
Author(s):  
Yipeng Lu ◽  
Xian Xu ◽  
Yaozhi Luo
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Experimental Result ◽  
Dijkstra Algorithm ◽  
Long Distance ◽  
Numerical Result ◽  
Planning Approach ◽  
The Cost ◽  
Rolling Locomotion ◽  
Terrain Characteristics

Tensegrity-based locomotive robots have attracted more and more interests from multidisciplinary engineering community. To realize long distance locomotion for tensegrity robots in a given land, path planning is usually needed. This paper proposes a path planning approach for rolling locomotion of polyhedral tensegrity robots. Given the start vertex, target vertex and the directed graph G which indicates the possible paths, the optimal path with lowest cost can be found by Dijkstra algorithm. Numerical and experimental examples are carried out with a six-bar tensegrity robot prototype. Both motion distance and terrain characteristics are considered within the cost. The proposed approach is generally verified by the examples. A comparison between the numerical result and the experimental result is also presented.

Optimal path planning of a disinfection mobile robot against COVID-19 in a ROS-based research platform

2021 ◽  
Vol 16 (4) ◽  
pp. 405-417
Author(s):  
L. Banjanovic-Mehmedovic ◽  
I. Karabegovic ◽  
J. Jahic ◽  
M. Omercic
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Pso Algorithm ◽  
Optimal Path Planning ◽  
Localization And Mapping ◽  
Increasing Demand ◽  
Window Approach ◽  
Path Planner ◽  
Hardware Platforms ◽  
Reactive Collision

Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution – a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.

scholarly journals Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141666366 ◽  
Author(s):  
Imen Chaari ◽  
Anis Koubaa ◽  
Hachemi Bennaceur ◽  
Adel Ammar ◽  
Maram Alajlan ◽  
...  
Keyword(s):  
Path Planning ◽  
Execution Time ◽  
Large Scale ◽  
Optimal Path ◽  
Heuristic Methods ◽  
Solution Quality ◽  
Good Trade ◽  
And Performance ◽  
Path Planner ◽  
The Cost

This article presents the results of the 2-year iroboapp research project that aims at devising path planning algorithms for large grid maps with much faster execution times while tolerating very small slacks with respect to the optimal path. We investigated both exact and heuristic methods. We contributed with the design, analysis, evaluation, implementation and experimentation of several algorithms for grid map path planning for both exact and heuristic methods. We also designed an innovative algorithm called relaxed A-star that has linear complexity with relaxed constraints, which provides near-optimal solutions with an extremely reduced execution time as compared to A-star. We evaluated the performance of the different algorithms and concluded that relaxed A-star is the best path planner as it provides a good trade-off among all the metrics, but we noticed that heuristic methods have good features that can be exploited to improve the solution of the relaxed exact method. This led us to design new hybrid algorithms that combine our relaxed A-star with heuristic methods which improve the solution quality of relaxed A-star at the cost of slightly higher execution time, while remaining much faster than A* for large-scale problems. Finally, we demonstrate how to integrate the relaxed A-star algorithm in the robot operating system as a global path planner and show that it outperforms its default path planner with an execution time 38% faster on average.

scholarly journals An Intelligent Gain based Ant Colony Optimisation Method for Path Planning of Unmanned Ground Vehicles

2019 ◽  
Vol 69 (2) ◽  
pp. 167-172 ◽  
Author(s):  
Sangeetha Viswanathan ◽  
K. S. Ravichandran ◽  
Anand M. Tapas ◽  
Sellammal Shekhar
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Computation Time ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
Ground Vehicles ◽  
Optimal Path Planning ◽  
Military Applications ◽  
The Military ◽  
Reduction Cost

 In many of the military applications, path planning is one of the crucial decision-making strategies in an unmanned autonomous system. Many intelligent approaches to pathfinding and generation have been derived in the past decade. Energy reduction (cost and time) during pathfinding is a herculean task. Optimal path planning not only means the shortest path but also finding one in the minimised cost and time. In this paper, an intelligent gain based ant colony optimisation and gain based green-ant (GG-Ant) have been proposed with an efficient path and least computation time than the recent state-of-the-art intelligent techniques. Simulation has been done under different conditions and results outperform the existing ant colony optimisation (ACO) and green-ant techniques with respect to the computation time and path length.

UAV Path Planning Based on Particle Swarm Optimization with Global Best Path Competition

2018 ◽  
Vol 32 (06) ◽  
pp. 1859008 ◽  
Author(s):  
Chen Huang ◽  
Jiyou Fei
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Competition Strategy ◽  
Swarm Optimization ◽  
Evaluation Functions ◽  
Planning Methods

Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning.

Optimal path planning of manipulatory systems subjected to non-autonomous motion laws

Robotica ◽  
1997 ◽  
Vol 15 (3) ◽  
pp. 251-261 ◽  
Author(s):  
A. D. Jutard-Malinge ◽  
G. Bessonnet
Keyword(s):  
Path Planning ◽  
Mechanical System ◽  
Degrees Of Freedom ◽  
Optimal Path ◽  
State Equation ◽  
Dynamic Complexity ◽  
Travelling Time ◽  
Complementary Part ◽  
The Cost ◽  
Autonomous State

A path planning method is presented based on non-autonomous dynamic modeling of open-loops in articulated systems. It is assumed that one part of the mechanical system is submitted to specified motions laws, while movements of the complementary part are free. Thus, motion optimization is related to free joint movements but it is achieved on the basis of the dynamic model of the whole mechanical system. This approach introduces a non-autonomous state equation of a special type in the sense that it can not only depend on the running time but also on the unknown travelling time. The cost function to be minimized involves the travelling time and the actuating inputs. Optimization is achieved by applying the Pontryagin Maximum Principle which yields a new optimality condition concerning the travelling time dependency of the stated problem. Two simulation examples are presented. The first one shows how the developed technique makes possible both the reducing and mastering the dynamic complexity of a four degrees of freedom-vertical manipulator. Set at four degrees of freedom, the second one deals with a redundant planar manipulator characterized by a mobile base that is submitted to a specified driving motion.

scholarly journals Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147827-147838 ◽  
Author(s):  
Min Luo ◽  
Xiaorong Hou ◽  
Jing Yang
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Dijkstra Algorithm ◽  
Optimal Path Planning
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