scholarly journals Disordered and Multiple Destinations Path Planning Methods for Mobile Robot in Dynamic Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yong-feng Dong ◽  
Hong-mei Xia ◽  
Yan-cong Zhou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Dynamic Environment ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Original Algorithm ◽  
Sequencing Rule ◽  
Planning Methods

In the smart home environment, aiming at the disordered and multiple destinations path planning, the sequencing rule is proposed to determine the order of destinations. Within each branching process, the initial feasible path set is generated according to the law of attractive destination. A sinusoidal adaptive genetic algorithm is adopted. It can calculate the crossover probability and mutation probability adaptively changing with environment at any time. According to the cultural-genetic algorithm, it introduces the concept of reducing turns by parallelogram and reducing length by triangle in the belief space, which can improve the quality of population. And the fallback strategy can help to jump out of the “U” trap effectively. The algorithm analyses the virtual collision in dynamic environment with obstacles. According to the different collision types, different strategies are executed to avoid obstacles. The experimental results show that cultural-genetic algorithm can overcome the problems of premature and convergence of original algorithm effectively. It can avoid getting into the local optimum. And it is more effective for mobile robot path planning. Even in complex environment with static and dynamic obstacles, it can avoid collision safely and plan an optimal path rapidly at the same time.

A Review of Path Planning Method for Mobile Robot

2014 ◽  
Vol 1030-1032 ◽  
pp. 1588-1591 ◽  
Author(s):  
Zong Sheng Wu ◽  
Wei Ping Fu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Grid Method ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Planning Methods ◽  
Basic Ideas ◽  
Artificial Potential Field Method

The ability of a mobile robot to plan its path is the key task in the field of robotics, which is to find a shortest, collision free, optimal path in the various scenes. In this paper, different existing path planning methods are presented, and classified as: geometric construction method, artificial intelligent path planning method, grid method, and artificial potential field method. This paper briefly introduces the basic ideas of the four methods and compares them. Some challenging topics are presented based on the reviewed papers.

Path planning of a mobile robot using genetic heuristics

Robotica ◽  
1998 ◽  
Vol 16 (5) ◽  
pp. 575-588 ◽  
Author(s):  
Andreas C. Nearchou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Hill Climbing ◽  
Planning Problem ◽  
Well Performance ◽  
Simulation Results ◽  
Task Oriented ◽  
Path Planning Problem

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

Swarm Intelligence Optimization Techniques in Mobile Path Planning - A Review

2018 ◽  
Vol 37 ◽  
pp. 62-71 ◽  
Author(s):  
Chika O. Yinka-Banjo ◽  
Ukamaka Hope Agwogie
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Dynamic Environment ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Crucial Problem ◽  
Swarm Intelligence Optimization ◽  
Present World

In the present world, mobile robot has been widely used for many functions across different areas of life. These mobile robots can be engaged in a static or dynamic environment where they are expected to accomplish a task optimally against all odds. Path planning for mobile robot is a very crucial problem in robotics that has been greatly researched upon; it is aimed at finding an optimal path in a given environment from a start point to the goal point. Several techniques have been employed in solving this crucial problem. These techniques are broadly classified as classical and heuristics. The Swarm Intelligence Techniques form a sub-class of the heuristics approach. The aim of this research is to review the swarm intelligence techniques in solving the mobile robot path planning problem. The drawbacks and merits of each of the techniques were discussed and a comparative analysis was given.

scholarly journals Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Actuators ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 4
Author(s):  
Haoting Liu ◽  
Jianyue Ge ◽  
Yuan Wang ◽  
Jiacheng Li ◽  
Kai Ding ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Artificial Bee Colony ◽  
Fitness Function ◽  
Optimal Path ◽  
Adaptive Genetic Algorithm ◽  
Transmission Tower ◽  
Bee Colony ◽  
Searching Strategy ◽  
Revenue Function

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

Path Planning of Mobile Robot Agent Using Heuristic Based Integrated Hybrid Algorithm

2014 ◽  
Vol 984-985 ◽  
pp. 1229-1234
Author(s):  
K. Sudhagar ◽  
M. Bala Subramanian ◽  
G. Rajarajeswari
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Dynamic Environment ◽  
Test Bed ◽  
Unknown Environment ◽  
Optimal Path Planning ◽  
Planning Algorithm ◽  
Simulation Results ◽  
Behavioural Aspects

Optimal path planning is considered to be the key area, which gives much attention to researchers in the field of robotic research community. In this paper, a comprehensive simulation study was made on applying heuristic based optimal path planning algorithm of a mobile robot agent in an dynamic environment. This study aims on the behavioural aspects, exploration and navigational aspects along with optimal path analysis of a mobile robot agent. The behaviour selection of a mobile robot agent is considered to be the key challenge for designing a control architecture system, in which it is highly suitable for dynamically changing environment. A mobile robot agent participating for mission critical application will explore into an known environment without any discrepancy, but exploring into an unknown environment will be a challenging criterion, considering its constraints such as time, cost, energy, exploration distance etc., This paper aims on navigational study of the mobile robot agent participating in dynamically changing environments, using heuristic approach. The System evaluation is validated using Graphical User Interface (GUI) based test-bed for Robots called RoboSim and the efficiency of the system is measured, via Simulation Results. Simulation results prove that applying A* algorithm in an unknown environment explores much faster than other path planning algorithms.

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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