scholarly journals An Approach to Improve Multi objective Path Planning for Mobile Robot Navigation using the Novel Quadrant Selection Method

2021 ◽  
Vol 71 (6) ◽  
pp. 748-761
Author(s):  
K. Rajchandar ◽  
R. Baskaran ◽  
K. Padmanabhan Panchu ◽  
M. Rajmohan
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Target Position ◽  
Computational Effort ◽  
Mobile Robot Navigation ◽  
Multi Objective ◽  
Starting Position ◽  
Path Distance ◽  
Number Of Cells

Currently, automated and semi-automated industries need multiple objective path planning algorithms for mobile robot applications. The multi-objective optimisation algorithm takes more computational effort to provide optimal solutions. The proposed grid-based multi-objective global path planning algorithm [Quadrant selection algorithm (QSA)] plans the path by considering the direction of movements from starting position to the target position with minimum computational effort. Primarily, in this algorithm, the direction of movements is classified into quadrants. Based on the selection of the quadrant, the optimal paths are identified. In obstacle avoidance, the generated feasible paths are evaluated by the cumulative path distance travelled, and the cumulative angle turned to attain an optimal path. Finally, to ease the robot’s navigation, the obtained optimal path is further smoothed to avoid sharp turns and reduce the distance. The proposed QSA in total reduces the unnecessary search for paths in other quadrants. The developed algorithm is tested in different environments and compared with the existing algorithms based on the number of cells examined to obtain the optimal path. Unlike other algorithms, the proposed QSA provides an optimal path by dramatically reducing the number of cells examined. The experimental verification of the proposed QSA shows that the solution is practically implementable.

A Hybrid Approach Based on ACO and Ga for Multi Objective Mobile Robot Path Planning

2014 ◽  
Vol 527 ◽  
pp. 203-212 ◽  
Author(s):  
Bashra Kadhim Oleiwi ◽  
Hubert Roth ◽  
Bahaa I. Kazem
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Free Path ◽  
Optimal Path ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Mobile Robot Navigation ◽  
Initial Population ◽  
Multi Objective ◽  
Robot Path

In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for efficient and fast selection in generating the Multi objective optimal path for mobile robot navigation in static environment. First we used the proposed approach to evaluate its ability to solve single objective problem in length term as well as we compared it with traditional ACO and simple GA then we extended to solve Pareto optimality ideas based on three criteria: length, smoothness and security, and making it Multi objective Hybrid approach. The proposed approach is tested to generate the single and multi objective optimal collision free path. The simulation results show that the mobile robot travels successfully from one location to another and reaches its goal after avoiding all obstacles that are located in its way in all tested environment and indicate that the proposed approach is accurate and can find a set Pareto optimal solution efficiently in a single run.

scholarly journals A New Online Random Particles Optimization Algorithm for Mobile Robot Path Planning in Dynamic Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Behrang Mohajer ◽  
Kourosh Kiani ◽  
Ehsan Samiei ◽  
Mostafa Sharifi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Potential Field ◽  
Optimal Path ◽  
Target Position ◽  
Path Selection ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Robot Path

A new algorithm named random particle optimization algorithm (RPOA) for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot’s sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles, and finally a low level decision control provides a control signal for control of considered holonomic mobile robot. This process is implemented without requirement to tuning algorithm or complex calculation, and furthermore, it is independent from gradient base methods such as heuristic (artificial potential field) methods. Therefore, in this paper, the problem of local mobile path planning is free from getting stuck in local minima and is easy computed. To evaluate the proposed algorithm, some simulations in three various scenarios are performed and results are compared by the artificial potential field.

scholarly journals A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

2021 ◽  
Vol 9 (11) ◽  
pp. 1243
Author(s):  
Charis Ntakolia ◽  
Dimitrios V. Lyridis
Keyword(s):  
Fuzzy Logic ◽  
Path Planning ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Computation Time ◽  
Computational Effort ◽  
Unmanned Vehicles ◽  
Maritime Transportation ◽  
Solution Quality ◽  
Multi Objective

Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs in maritime transportation. However, most of the traditional path planning approaches include single-objective approaches that mainly find the shortest path. Dynamic and complex environments impose the need for multi-objective path planning where an optimal path should be found to satisfy contradicting objective terms. To this end, a swarm intelligence graph-based pathfinding algorithm (SIGPA) has been proposed in the recent literature. This study aims to enhance the performance of SIGPA algorithm by integrating fuzzy logic in order to cope with the multiple objectives and generate quality solutions. A comparative evaluation is conducted among SIGPA and the two most popular fuzzy inference systems, Mamdani (SIGPAF-M) and Takagi–Sugeno–Kang (SIGPAF-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. SIGPA remains a reliable approach for real-time applications due to low computational effort; SIGPAF-M generates better paths; and SIGPAF-TSK reaches a better trade-off among solution quality and computation time.

A New Real Time Path Planning for Mobile Robot Navigation Using Invasive Weed Optimization Algorithm

2014 ◽  
Author(s):  
Prases K. Mohanty ◽  
Dayal R. Parhi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Fitness Function ◽  
Optimal Path ◽  
Mobile Robot Navigation ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Planning Algorithm ◽  
Path Planner

In this article a new optimal path planner for mobile robot navigation based on invasive weed optimization (IWO) algorithm has been addressed. This ecologically inspired algorithm is based on the colonizing property of weeds and distribution. A new fitness function has been formed between robot to goal and obstacles, which satisfied the conditions of both obstacle avoidance and target seeking behavior in robot present in the environment. Depending on the fitness function value of each weed in the colony the robot that avoids obstacles and navigating towards goal. The optimal path is generated with this developed algorithm when the robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed navigational algorithm has been performed through a series of simulation and experimental results. The results obtained from the proposed algorithm has been also compared with other intelligent algorithms (Bacteria foraging algorithm and Genetic algorithm) to show the adaptability of the developed navigational method. Finally, it has been concluded that the proposed path planning algorithm can be effectively implemented in any kind of complex environments.

scholarly journals Type-2 fuzzy logic controller optimized by wavelet networks for mobile robot navigation

2020 ◽  
Vol 18 (1) ◽  
pp. 326
Author(s):  
Fatma Affane ◽  
Kadda Zemalache Meguenni ◽  
Abdelhafid Omari
Keyword(s):  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Robot Navigation ◽  
Target Position ◽  
Fuzzy Reasoning ◽  
Mobile Robot Navigation ◽  
Starting Position ◽  
Wavelet Networks ◽  
Minimum Delay

<p>In this work, we will use a new control strategy based on the integration of a type-2 fuzzy reasoning optimized by wavelet networks as part of a navigation system of a mobile robot. The proposed approach is able to facilitate the navigation task in an autonomous manner, in order to determine which commands must be sent at each moment to the mobile robot. This operation must take into account convergence towards a goal with the shortest possible path in the minimum delay between the starting position and the target position. Once the goal is reached, the robot stops. </p><p> </p>

Global Path Planning for Mobile Robot Based on Improved Ant Colony Algorithms

2013 ◽  
Vol 418 ◽  
pp. 15-19 ◽  
Author(s):  
Min Huang ◽  
Ping Ding ◽  
Jiao Xue Huan
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Ant Colony ◽  
Mobile Robot Navigation ◽  
Local Optimum ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Global Path Planning ◽  
Global Optimal

Global optimal path planning is always an important issue in mobile robot navigation. To avoid the limitation of local optimum and accelerate the convergence of the algorithm, a new robot global optimal path planning method is proposed in the paper. It adopts a new transition probability function which combines with the angle factor function and visibility function, and at the same time, sets penalty function by a new pheromone updating model to improve the accuracy of the route searching. The results of computer emulating experiments prove that the method presented is correct and effective, and it is better than the genetic algorithm and traditional ant colony algorithm for global path planning problem.

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