Dynamic Path Planning for Robot Navigation Using Sonor Mapping and Neural Networks

1997 ◽  
Vol 119 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Won Soo Yun ◽  
Dong Woo Cho ◽  
Yoon Su Baek
Keyword(s):  
Neural Networks ◽  
Path Planning ◽  
Mobile Robot ◽  
Local Planning ◽  
Sensor Model ◽  
Planning Algorithm ◽  
Local Path ◽  
Time Varying Environment ◽  
Path Planning Algorithm ◽  
Global And Local

This paper presents a new path planning algorithm for safe navigation of a mobile robot in dynamic as well as static environments. The certainty grid concept is adopted to represent the robot’s surroundings and a simple sensor model is developed for fast acquisition of environmental information. The proposed system integrates global and local path planning and has been implemented in a partially known structured environment without loss of generality for an indoor mobile robot. The global planner finds the initial path based on Dijkstra’s algorithm, while the local planning scheme uses three neural networks to follow the initial global path and avoid colliding with static and moving obstacles. Effectiveness of these algorithms is illustrated through simulation and experiment using a real robot. The results show that the proposed algorithm can be efficiently implemented in a time varying environment.

scholarly journals Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6642
Author(s):  
Rafal Szczepanski ◽  
Artur Bereit ◽  
Tomasz Tarczewski
Keyword(s):  
Augmented Reality ◽  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Local Minimum ◽  
Artificial Potential Field ◽  
Local Path Planning ◽  
Planning Algorithm ◽  
Local Path ◽  
Path Planning Algorithm

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The Artificial Potential Field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of Artificial Potential Field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel Artificial Potential Field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

Mobile Robot Path Planning Based on Local Environment Modeling and Adaptive Window

2011 ◽  
Vol 48-49 ◽  
pp. 679-683
Author(s):  
Xun Yu Zhong ◽  
Xia Fu Peng ◽  
Jie Hua Zhou ◽  
Xiao Ci Huang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Local Environment ◽  
Environment Modeling ◽  
Movement Smoothness ◽  
Planning Algorithm ◽  
Overall Performance ◽  
Local Path ◽  
Path Planning Algorithm ◽  
Robot Path

In order to solve the problem of path planning for mobile robot in unknown environment, improving overall performance of the planned path, a multi-constrained local environment modeling method is used in which the traversability, security, movement smoothness and goal-guiding are taken into consideration. On the basis of environment modeling, improved local path planning algorithms is proposed by combining Bug algorithm and rolling path planning algorithm. That improved algorithm contains Move to Goal behavior and Wall Following behavior, for overcoming local minimum, designed for robot using adaptive dynamic window. Simulation results show that the proposed methods have a good safety obstacle avoidance capabilities and environmental adaptability.

Development and analysis of DAYANI arc contour intelligent technique for navigation of two-wheeled mobile robot

2018 ◽  
Vol 45 (5) ◽  
pp. 688-702 ◽  
Author(s):  
Dayal R. Parhi ◽  
Animesh Chhotray
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Weight Functions ◽  
Feasible Point ◽  
Content Type ◽  
Intelligent Technique ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Incremental Step

PurposeThis paper aims to generate an obstacle free real time optimal path in a cluttered environment for a two-wheeled mobile robot (TWMR).Design/methodology/approachThis TWMR resembles an inverted pendulum having an intermediate body mounted on a robotic mobile platform with two wheels driven by two DC motors separately. In this article, a novel motion planning strategy named as DAYANI arc contour intelligent technique has been proposed for navigation of the two-wheeled self-balancing robot in a global environment populated by obstacles. The developed new path planning algorithm evaluates the best next feasible point of motion considering five weight functions from an arc contour depending upon five separate navigational parameters.FindingsAuthenticity of the proposed navigational algorithm has been demonstrated by computing the path length and time taken through a series of simulations and experimental verifications and the average percentage of error is found to be about 6%.Practical implicationsThis robot dynamically stabilizes itself with taller configuration, can spin on the spot and rove along through obstacles with smaller footprints. This diversifies its areas of application to both indoor and outdoor environments especially with very narrow spaces, sharp turns and inclined surfaces where its multi-wheel counterparts feel difficult to perform.Originality/valueA new obstacle avoidance and path planning algorithm through incremental step advancement by evaluating the best next feasible point of motion has been established and verified through both experiment and simulation.

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