scholarly journals Safe Path Planning of Mobile Robot in a known Dynamic Environment

2019 ◽  
Vol 9 (2) ◽  
pp. 2434-2439
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Dynamic Environment ◽  
Mobile Robot Navigation ◽  
Matlab Simulation ◽  
A Algorithm ◽  
New Approach ◽  
Probability Of Collision ◽  
Safe Path

Path planning in mobile robot navigation is an advanced method of calculating the safe and obstacle free path in static and dynamic environments are involved between source point to destination. Real time path planning method defines that how a robot can make a decision when some unknown obstacle gets encountered in the path of navigation for a dynamic situation. At the point when an obstruction comes in the way of route, the robot must choose another and safe way to advance towards the objective by evading any impact. This study is focused on exploring the algorithm that gives the safe and shortest path when an obstacle changes the environment. By using A* algorithm in MATLAB simulation the probability of collision with obstacle and robot get increased. In this simulation work a new approach of path planning has been found by placing the virtual obstacles in the environment. A new obstacle get influence in the path of navigation, using virtual obstacle boundary around the new obstacle a short and safe path get evaluated which is collision free or low risk path . The purpose for this paper is to create a dependable and smooth direction in a real time domain with impediments and to manage the robot towards the target without hitting the obstacles also considering the size of the robot

scholarly journals A Path Planning Algorithm for a Dynamic Environment Based on Proper Generalized Decomposition

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2245
Author(s):  
Antonio Falcó ◽  
Lucía Hilario ◽  
Nicolás Montés ◽  
Marta C. Mora ◽  
Enrique Nadal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Potential Field ◽  
Dynamic Environment ◽  
Artificial Potential Field ◽  
Proper Generalized Decomposition ◽  
Partial Differential ◽  
Planning Algorithm ◽  
Path Planning Algorithm

A necessity in the design of a path planning algorithm is to account for the environment. If the movement of the mobile robot is through a dynamic environment, the algorithm needs to include the main constraint: real-time collision avoidance. This kind of problem has been studied by different researchers suggesting different techniques to solve the problem of how to design a trajectory of a mobile robot avoiding collisions with dynamic obstacles. One of these algorithms is the artificial potential field (APF), proposed by O. Khatib in 1986, where a set of an artificial potential field is generated to attract the mobile robot to the goal and to repel the obstacles. This is one of the best options to obtain the trajectory of a mobile robot in real-time (RT). However, the main disadvantage is the presence of deadlocks. The mobile robot can be trapped in one of the local minima. In 1988, J.F. Canny suggested an alternative solution using harmonic functions satisfying the Laplace partial differential equation. When this article appeared, it was nearly impossible to apply this algorithm to RT applications. Years later a novel technique called proper generalized decomposition (PGD) appeared to solve partial differential equations, including parameters, the main appeal being that the solution is obtained once in life, including all the possible parameters. Our previous work, published in 2018, was the first approach to study the possibility of applying the PGD to designing a path planning alternative to the algorithms that nowadays exist. The target of this work is to improve our first approach while including dynamic obstacles as extra parameters.

scholarly journals A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot

2018 ◽  
Vol 13 (6) ◽  
pp. 1032-1046 ◽  
Author(s):  
Xiaoru Song ◽  
Song Gao ◽  
C.B. Chen ◽  
Kai Cao ◽  
Jiaoru Huang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Obstacle Avoidance ◽  
A Algorithm ◽  
Fusion Algorithm ◽  
Global Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Dynamic Obstacle

Path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot.

Evolutionary Techniques for Mobile Robot Navigation

2012 ◽  
Vol 433-440 ◽  
pp. 6646-6651
Author(s):  
Soh Chin Yun ◽  
S. Parasuraman ◽  
Velappa Ganapathy
Keyword(s):  
Genetic Algorithm ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Autonomous Systems ◽  
Dynamic Environment ◽  
Mobile Robot Navigation ◽  
Research Trend ◽  
Matlab Simulation ◽  
Starting Position ◽  
To Come

Current research trend in mobile robot is to build intelligent and autonomous systems that enables mobile robot to plan its motion in static and dynamic environment. In this paper, Genetic Algorithm (GA) is utilized to come out with an algorithm that enables the mobile robot to move from the starting position to the desired goal without colliding with any of the obstacles in the environment. The proposed navigation technique is capable of re-planning new optimum collision free path in the event of mobile robot encountering dynamic obstacles. The method is verified using MATLAB simulation and validated by Team AmigoBotTM robot. The results obtained from MATLAB simulation and real time implementation are discussed at the end of the paper.

Sign in / Sign up

Export Citation Format

Share Document