Optimal path planning for a mobile robot using cuckoo search algorithm

2014 ◽  
Vol 28 (1-2) ◽  
pp. 35-52 ◽  
Author(s):  
Prases K. Mohanty ◽  
Dayal R. Parhi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Search Algorithm ◽  
Optimal Path ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimal Path Planning

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 Redundant Cooperative Robots Under Equality and Inequality Constraints

2003 ◽  
Author(s):  
Ali Hosseini ◽  
Mehdi Keshmiri
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Minimum Principle ◽  
Optimal Path ◽  
Inequality Constraints ◽  
Optimal Path Planning ◽  
Nonlinear Inequality ◽  
Nonlinear Inequality Constraints ◽  
Cooperative Manipulators

Using kinematic resolution, the optimal path planning for two redundant cooperative manipulators carrying a solid object on a desired trajectory is studied. The optimization problem is first solved with no constraint. Consequently, the nonlinear inequality constraints, which model obstacles, are added to the problem. The formulation has been derived using Pontryagin Minimum Principle and results in a Two Point Boundary Value Problem (TPBVP). The problem is solved for a cooperative manipulator system consisting of two 3-DOF serial robots jointly carrying an object and the results are compared with those obtained from a search algorithm. Defining the obstacles in workspace as functions of joint space coordinates, the inequality constrained optimization problem is solved for the cooperative manipulators.

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.

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.

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