An Advanced Quantum Optimization Algorithm for Robot Path Planning

2019 ◽  
Vol 29 (08) ◽  
pp. 2050122
Author(s):  
Liming Gao ◽  
Rong Liu ◽  
Fei Wang ◽  
Weizong Wu ◽  
Baohua Bai ◽  
...  
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Optimization Problems ◽  
Dynamic Environment ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Computationally Efficient ◽  
Planning Algorithm ◽  
Scale Optimization ◽  
Robot Path

In this paper, a new robot path planning algorithm based on Quantum-inspired Evolutionary Algorithm (QEA) is proposed. QEA is an advanced evolutionary computing scheme with the quantum computing features such as qubits and superposition. It is suitable for solving large scale optimization problems. The proposed QEA algorithm works in the discretized environment, and approximates the optimal robot planing path in a highly computationally efficient fashion. The simulation results indicate that the proposed QEA algorithm is suitable for both complex static and dynamic environment and considerably outperforms the conventional genetic algorithm (GA) for solving the robot path planning problem. Our algorithm runs in only about 2[Formula: see text]s, which demonstrates that it can well tackle the optimization problem in robot path planning.

A Multi-Objective Path Planning Algorithm for Mobile Robots Based on Cellular Automata

2015 ◽  
Author(s):  
Yousef Naranjani ◽  
Jian-Qiao Sun
Keyword(s):  
Path Planning ◽  
Optimization Problems ◽  
Optimal Path ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Maximum Slope ◽  
Multi Objective ◽  
Planning Algorithm ◽  
Elevation Changes ◽  
Robot Path

Many real-world applications of robot path planning involves not only finding the shortest path, but also achieving some other objectives such as minimizing fuel consumption or avoiding danger areas. This paper introduces a 2D path planning scheme that solves a multi-objective path planning problem on a 3D terrain. This allows the controller to pick the most suitable path among a set of optimal paths. The algorithm generates a cellular automaton for the terrain based on the objectives by applying various weighting factors via an evolutionary algorithm and finds the optimal path between the start point and the goal for each set of parameters considering static obstacles and maximum slope constraints. All the final trajectories share the same characteristic that they are non-dominated with respect to the rest of the set in the Multi-Objective Optimization Problems (MOP) context. The objectives considered in this study includes the path length, the elevation changes and avoiding the radars. Testing the algorithm on several problems showed that the method is very promising for mobile robot path planning applications.

Robot path planning algorithm based on symbolic tags in dynamic environment

2017 ◽  
Author(s):  
Aleksandr Vokhmintcev ◽  
Andrey Melnikov ◽  
Aleksandr Kozko ◽  
Michael Timchenko ◽  
Artem Makovetskii
Keyword(s):  
Path Planning ◽  
Dynamic Environment ◽  
Robot Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

scholarly journals Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983957 ◽  
Author(s):  
Seyedhadi Hosseininejad ◽  
Chitra Dadkhah
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Free Path ◽  
Optimization Algorithm ◽  
Dynamic Environment ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Cuckoo Optimization Algorithm ◽  
Starting Point ◽  
Robot Path

Nowadays, the usage of autonomous mobile robots that fulfill various activities in enormous number of applications without human’s interference in a dynamic environment are thriving. A dynamic environment is the robot’s environment which is comprised of some static obstacles as well as several movable obstacles that their quantity and location change randomly through the time. Efficient path planning is one the significant necessities of these kind of robots to do their tasks effectively. Mobile robot path planning in a dynamic environment is finding a shortest possible path from an arbitrary starting point toward a desired goal point which needs to be safe (obstacle avoidance) and smooth as well as possible. To achieve this target, simultaneously satisfying a collection of certain constraints including the shortest, smooth, and collision free path is required. Therefore, this issue can be considered as an optimization problem, consequently solved via optimization algorithms. In this article, a new method based on cuckoo optimization algorithm is proposed for solving the mobile robot path planning problem in a dynamic environment. Furthermore, to diminish the computational complexity, the feature vector is also optimized (i.e. reduced in dimension) via a new proposed technique. The simulation results show the performance of proposed algorithm in finding a short, safe, smooth, and collision free path in different environment conditions.

Comparison between Normal Waveform and Modified Wavefront Path Planning Algorithm for Mobile Robot

2014 ◽  
Vol 607 ◽  
pp. 778-781 ◽  
Author(s):  
Swee Ho Tang ◽  
Che Fai Yeong ◽  
Eileen Lee Ming Su
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Large Scale ◽  
Simulation Software ◽  
Robot Path Planning ◽  
Full Wave ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Grid Based ◽  
Robot Path

Mobile robot path planning is about finding a movement from one position to another without collision. The wavefront is typically used for path planning jobs and appreciated for its efficiency, but it needs full wave expansion which takes significant amount of time and process in large scale environment. This study compared wavefront algorithm and modified wavefront algorithm for mobile robots to move efficiently in a collision free grid based static environment. The algorithms are compared in regards to parameters such as execution time of the algorithm and planned path length which is carried out using Player/Stage simulation software. Results revealed that modified wavefront algorithm is a much better path planning algorithm compared to normal wavefront algorithm in static environment.

A fast robot path planning algorithm based on bidirectional associative learning

2021 ◽  
Vol 155 ◽  
pp. 107173
Author(s):  
Meng Zhao ◽  
Hui Lu ◽  
Siyi Yang ◽  
Yinan Guo ◽  
Fengjuan Guo
Keyword(s):  
Path Planning ◽  
Associative Learning ◽  
Robot Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

Three Dimensional Robot Path Planning With Workspace Considerations

1992 ◽  
Author(s):  
C. Y. Liu ◽  
R. W. Mayne
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Recursive Quadratic Programming ◽  
Six Degree Of Freedom ◽  
Nonlinear Programming Method ◽  
Path Planning Problem ◽  
Robot Path

Abstract This paper considers the problem of robot path planning by optimization methods. It focuses on the use of recursive quadratic programming (RQP) for the optimization process and presents a formulation of the three dimensional path planning problem developed for compatibility with the RQP selling. An approach 10 distance-to-contact and interference calculations appropriate for RQP is described as well as a strategy for gradient computations which are critical to applying any efficient nonlinear programming method. Symbolic computation has been used for general six degree-of-freedom transformations of the robot links and to provide analytical derivative expressions. Example problems in path planning are presented for a simple 3-D robot. One example includes adjustments in geometry and introduces the concept of integrating 3-D path planning with geometric design.

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