scholarly journals Complete Path Planning That Simultaneously Optimizes Length and Clearance

2021 ◽  
Author(s):  
Basak Sakcak ◽  
Steven M. LaValle
Keyword(s):  
Path Planning ◽  
Complete Path

Robot path planning based on genetic algorithm with hybrid initialization method

2021 ◽  
pp. 1-16
Author(s):  
Zhaojun Zhang ◽  
Rui Lu ◽  
Minglong Zhao ◽  
Shengyang Luan ◽  
Ming Bu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Iteration Process ◽  
Initial Population ◽  
Local Optimum ◽  
Complex Environments ◽  
Random Initialization ◽  
Planning Problems ◽  
Complete Path

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

Nonholonomic path planning among obstacles subject to curvature restrictions

Robotica ◽  
2002 ◽  
Vol 20 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Wilson D. Esquivel ◽  
Luciano E. Chiang
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Autonomous Navigation ◽  
Line Segments ◽  
Circular Arcs ◽  
Straight Line ◽  
Smoothing Process ◽  
Planning Methodology ◽  
Final Orientation ◽  
Complete Path

This paper addresses the problem of finding a nonholonomic path subject to a curvature restriction, to be tracked by a wheeled autonomous navigation vehicle. This robot is able to navigate in a structured environment, with obstacles modeled as polygons, thus constituting a model based system. The path planning methodology begins with the conditioning of the polygonal environment by offsetting each polygon in order to avoid the possibility of collision with the mobile. Next, the modified polygonal environment is used to compute a preliminary shortest path (PA) between the two extreme positions of the trajectory in the plane (x, y). This preliminary path (PA) does not yet consider the restrictions on the curvature and is formed only by straight line segments. A smoothing process follows in order to obtain a path (PS) that satisfies curvature restrictions which consist basically of joining the straight line segments by circular arcs of minimum radius R (filleting). Finally, the initial and final orientation of the vehicle are accounted for. This is done using a technique we have called the Star Algorithm, because of the geometric shape of the resulting maneuvers. A final complete path (PC) is thus obtained.

scholarly journals Complete Path Planning for a Tetris-Inspired Self-Reconfigurable Robot by the Genetic Algorithm of the Traveling Salesman Problem

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 344 ◽  
Author(s):  
Anh Le ◽  
Manimuthu Arunmozhi ◽  
Prabakaran Veerajagadheswar ◽  
Ping-Cheng Ku ◽  
Tran Hoang Minh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Real World ◽  
Travelling Salesman Problem ◽  
Second Phase ◽  
Complete Coverage ◽  
Reconfigurable Robot ◽  
Four Blocks ◽  
The Cost ◽  
Complete Path

The efficiency of autonomous systems that tackle tasks such as home cleaning, agriculture harvesting, and mineral mining depends heavily on the adopted area coverage strategy. Extensive navigation strategies have been studied and developed, but few focus on scenarios with reconfigurable robot agents. This paper proposes a navigation strategy that accomplishes complete path planning for a Tetris-inspired hinge-based self-reconfigurable robot (hTetro), which consists of two main phases. In the first phase, polyomino form-based tilesets are generated to cover the predefined area based on the tiling theory, which generates a series of unsequenced waypoints that guarantee complete coverage of the entire workspace. Each waypoint specifies the position of the robot and the robot morphology on the map. In the second phase, an energy consumption evaluation model is constructed in order to determine a valid strategy to generate the sequence of the waypoints. The cost value between waypoints is formulated under the consideration of the hTetro robot platform’s kinematic design, where we calculate the minimum sum of displacement of the four blocks in the hTetro robot. With the cost function determined, the waypoint sequencing problem is then formulated as a travelling salesman problem (TSP). In this paper, a genetic algorithm (GA) is proposed as a strong candidate to solve the TSP. The GA produces a viable navigation sequence for the hTetro robot to follow and to accomplish complete coverage tasks. We performed an analysis across several complete coverage algorithms including zigzag, spiral, and greedy search to demonstrate that TSP with GA is a valid and considerably consistent waypoint sequencing strategy that can be implemented in real-world hTetro robot navigations. The scalability of the proposed framework allows the algorithm to produce reliable results while navigating within larger workspaces in the real world, and the flexibility of the framework ensures easy implementation of the algorithm on other polynomial-based shape shifting robots.

scholarly journals Autonomous path planning through application of rotated two-parameter overrelaxation 9-point Laplacian iteration technique

2021 ◽  
Vol 22 (2) ◽  
pp. 1116
Author(s):  
W. K. Ling ◽  
A'Qilah Ahmad Dahalan ◽  
Azali Saudi
Keyword(s):  
Path Planning ◽  
Harmonic Functions ◽  
Local Minima ◽  
Planning Algorithm ◽  
Path Navigation ◽  
Path Planning Algorithm ◽  
Two Parameter ◽  
Complete Path ◽  
Iteration Technique ◽  
Better Than

Autonomous path navigation is one of the important studies in robotics since a robot’s ability to navigate through an environment brings about many advancements with it. This paper suggests the iteration technique called half-sweep two parameter overrelaxation 9-point laplacian (HSTOR-9P) to be applied on autonomous path navigation and aims to investigate its effectiveness in performing computation for path planning in an indoor static environment. This iteration technique is a harmonic function that solves the Laplace’s equation where the modelling of the environment is based on. The harmonic functions are an appropriate method to be used on autonomous path planning because it satisfies the min-max principle, therefore avoiding the occurrence of local minima which traps robot’s movements, and that it offers complete path planning algorithm. Its performance is tested against its predecessor iteration technique. Results shown that HSTOR-9P iteration technique enables path construction in a lower number of iterations, thus, performs better than its predecessors.

A Biological Inspired Neural Network Approach to Robot Path Planning in Unknown Environment

2014 ◽  
Vol 602-605 ◽  
pp. 1399-1402 ◽  
Author(s):  
Bo Zhu ◽  
Zhong Min Wang ◽  
Zhao Lin Liu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Grid Method ◽  
Robot Path Planning ◽  
Simulation Experiments ◽  
Neural Network Approach ◽  
Environmental Models ◽  
Whole Space ◽  
Complete Path ◽  
Robot Path

Aiming to solve the problem of mobile robot path planning, environmental models are established by using grid method at first, each grid is treated as a neuron, and then the whole space is changed into a topology-form one with all neutral net. Secondly, biological inspired neural networks (BINN) method towards neutral net is adopted to complete path planning of mobile robot. Furthermore, non-optimal solutions potentially produced in neutral net are modified in BINN. Simulation experiments show the feasibility and effectiveness of BINN method.

scholarly journals Local Path Planning of Mobile Robot Using Critical-PointBug Algorithm Avoiding Static Obstacles

2016 ◽  
Vol 5 (3) ◽  
pp. 182
Author(s):  
SUBIR KUMAR DAS
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Limited Information ◽  
Surrounding Environment ◽  
Local Path Planning ◽  
Planning Algorithms ◽  
Local Path ◽  
Complete Path

<p align="left">Path planning is an essential task for the navigation of Autonomous Mobile Robot. This is one of the basic problems in robotics. Path planning algorithms are classified as global or local, depending on the knowledge of surrounding environment. In local path planning, the environment is unknown to the robot, and sensors are used to detect the obstacles and to avoid collision. Bug algorithms are one of the frequently used path planning algorithms where a mobile robot moves to the target by detecting the nearest obstacle and avoiding it with limited information about the environment. This proposed Critical-PointBug algorithm, is a new Bug algorithm for path planning of mobile robots. This algorithm tries to minimize traversal of obstacle border by searching few important points on the boundary of obstacle area as a rotation point to goal and end with a complete path from source to goal.</p>

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