Optimal Path Planning Using Swarm Intelligence Based Hybrid Techniques

2019 ◽  
Vol 16 (9) ◽  
pp. 3717-3727
Author(s):  
Monica Sood ◽  
Sahil Verma ◽  
Vinod Kumar Panchal ◽  
Kavita
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Research Work ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Hybrid Techniques ◽  
Different Types

The planning of optimal path is an important research domain due to vast applications of optimal path planning in the robotics, simulation and modeling, computer graphics, virtual reality estimation and animation, and bioinformatics. The optimal path planning application demands to determine the collision free shortest and optimal path. There can be numerous possibilities that to find the path with optimal length based on different types of available obstacles during the path and different types of workspace environment. This research work aims to identify the optimum path from the initial source-point to final point for the unknown workspace environment consists of static obstacles. For this experimentation, swarm intelligence based hybrid concepts are considered as the work collaboration and intelligence behavior of swarm agents provides the resourceful solution of NP hard problems. Here, the hybridization of concepts makes the solution of problem more efficient. Among swarm intelligence concepts, cuckoo search (CS) algorithm is one of the efficient algorithms due to clever behavior and brood parasitic property of cuckoo birds. In this research work, two hybrid concepts are proposed. First algorithm is the hybridized concept of cuckoo search with bat algorithm (BA) termed as CS-BAPP. Another algorithm is the hybridized concept of cuckoo search with firefly algorithm (FA) termed as CS-FAPP. Both algorithms are initially tested on the benchmarks functions and applied to the path planning problem. For path planning, a real time dataset area of Alwar region situated at Rajasthan (India) is considered. The selected region consists of urban and dense vegetation land cover features. The results for the optimal path planning on Alwar region are assessed using the evaluation metrics of minimum number of iterations, error rate, success rate, and simulation time. Moreover, the results are also compared with the individual FA, BA, and CS along with the comparison of hybrid concepts.

scholarly journals Optimal Path Planning using Hybrid Bat Algorithm and Cuckoo Search

2018 ◽  
Vol 7 (4.12) ◽  
pp. 30
Author(s):  
Monica Sood ◽  
Dr. Sahil Verma ◽  
Vinod Kumar Panchal ◽  
Dr. Kavita
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Research Work ◽  
Hybrid Approach ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Local Optima ◽  
Optimal Path Planning ◽  
Hybrid Concept ◽  
Minimum Number

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.  

scholarly journals Optimal Path Planning of Mobile Robot Using Hybrid Cuckoo Search-Bat Algorithm

2018 ◽  
Vol 133 ◽  
pp. 510-517 ◽  
Author(s):  
Mbl Saraswathi ◽  
Gunji Bala Murali ◽  
B.B.V.L. Deepak
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Optimal Path Planning

Optimal path planning of mobile robot using the hybrid cuckoo–bat algorithm in assorted environment

2019 ◽  
Vol 7 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Balamurali Gunji ◽  
Deepak B.B.V.L. ◽  
Saraswathi M.B.L. ◽  
Umamaheswara Rao Mogili
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real World ◽  
Hybrid Algorithm ◽  
Optimal Path ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Content Type ◽  
The Real ◽  
World Environment

Purpose The purpose of this paper is to obtain an optimal mobile robot path planning by the hybrid algorithm, which is developed by two nature inspired meta-heuristic algorithms, namely, cuckoo-search and bat algorithm (BA) in an unknown or partially known environment. The cuckoo-search algorithm is based on the parasitic behavior of the cuckoo, and the BA is based on the echolocation behavior of the bats. Design/methodology/approach The developed algorithm starts by sensing the obstacles in the environment using ultrasonic sensor. If there are any obstacles in the path, the authors apply the developed algorithm to find the optimal path otherwise reach the target point directly through diagonal distance. Findings The developed algorithm is implemented in MATLAB for the simulation to test the efficiency of the algorithm for different environments. The same path is considered to implement the experiment in the real-world environment. The ARDUINO microcontroller along with the ultrasonic sensor is considered to obtain the path length and time of travel of the robot to reach the goal point. Originality/value In this paper, a new hybrid algorithm has been developed to find the optimal path of the mobile robot using cuckoo search and BAs. The developed algorithm is tested with the real-world environment using the mobile robot.

Towards Design of an Efficient Sensing Data Acquisition scheme for UAVs-assisted Wireless Sensor Networks

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Path Planning ◽  
Data Acquisition ◽  
Large Scale ◽  
Cluster Formation ◽  
Optimal Path ◽  
Research Work ◽  
Planning Problem ◽  
Sensing Data ◽  
Planning Algorithm ◽  
Load Balanced

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

Optimal path planning for intelligent automated wheelchair using DDSRPSO

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kalaipriyan Thirugnanasambandam ◽  
Raghav R.S. ◽  
Jayakumar Loganathan ◽  
Ankur Dumka ◽  
Dhilipkumar V.
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Swarm Intelligence ◽  
Cooperative Behavior ◽  
Optimal Path ◽  
Particle Swarm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Optimal Path Planning

Purpose This paper aims to find the optimal path using directionally driven self-regulating particle swarm optimization (DDSRPSO) with high accuracy and minimal response time. Design/methodology/approach This paper encompasses optimal path planning for automated wheelchair design using swarm intelligence algorithm DDSRPSO. Swarm intelligence is incorporated in optimization due to the cooperative behavior in it. Findings The proposed work has been evaluated in three different regions and the comparison has been made with particle swarm optimization and self-regulating particle swarm optimization and proved that the optimal path with robustness is from the proposed algorithm. Originality/value The performance metrics used for evaluation includes computational time, success rate and distance traveled.

scholarly journals Efficient Path Planning of Secondary Additive Manufacturing Operations

2018 ◽  
Vol 249 ◽  
pp. 03011
Author(s):  
Keimargeo McQueen ◽  
Sara Darensbourg ◽  
Carl Moore ◽  
Tarik Dickens ◽  
Clement Allen
Keyword(s):  
Additive Manufacturing ◽  
Path Planning ◽  
Travelling Salesman Problem ◽  
Optimal Path ◽  
Planning Problem ◽  
Robotic Arms ◽  
Optimal Path Planning ◽  
Manufacturing Operations ◽  
3D Printed ◽  
Path Planner

We have designed a path planner for an additive manufacturing (AM) prototype that consists of two robotic arms which collaborate on a single part. Theoretically, with two nozzle equipped arms, a part can be 3D printed twice as fast. Moreover, equipping the second robot with a machining tool enables the completion of secondary operations like hole reaming or surface milling before the printing is finished. With two arms in the part space care must be taken to ensure that the arms collaborate intelligently; in particular, tasks must be planned so that the robots do not collide. This paper discusses the development of a robot path planner to efficiently print segments with two arms, while maintaining a safe distance between them. A solution to the travelling salesman problem, an optimal path planning problem, was used to successfully determine the robots path plans while a simple nozzle-to-nozzle distance calculation was added to represent avoiding robot-to-robot collisions. As a result, in simulation, the average part completion time was reduced by 45% over the single nozzle case. Importantly, the algorithm can theoretically be run on n-robots, so time reduction possibilities are large.

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