Localization and Path Planning for an Autonomous Mobile Robot Equipped with Sonar Sensor

2015 ◽  
Vol 772 ◽  
pp. 494-499 ◽  
Author(s):  
Corina Monica Pop ◽  
Gheorghe Leonte Mogan ◽  
Mihail Neagu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robotics ◽  
Planning Process ◽  
Autonomous Mobile Robot ◽  
Sonar Sensors ◽  
Sonar Sensor ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Global Trajectory

In the field of mobile robotics, the process of robot localization and global trajectory planning in robot operating scenes, that are completely or partially known, represents one of the main issues that are essential for providing the desired robot functionality. This paper introduces the basic elements of path planning for an autonomous mobile robot equipped with sonar sensors, operating in a static environment. The path planning process is initially performed by using a known map. Next, the sonar sensors are used to localize the robot, based on obstacle avoidance techniques. The effectiveness and efficiency of the algorithm proposed in this paper is demonstrated by the simulation results.

Experimental investigation of mapping and navigation based on certainty grids using sonar sensors

Robotica ◽  
1993 ◽  
Vol 11 (1) ◽  
pp. 7-17 ◽  
Author(s):  
Jong Hwan Lim ◽  
Dong Woo Cho
Keyword(s):  
Mobile Robot ◽  
Bayesian Model ◽  
Range Data ◽  
Autonomous Mobile Robot ◽  
Two Dimensional ◽  
On Certainty ◽  
Probability Map ◽  
Sonar Sensors ◽  
Sonar Sensor ◽  
Sensor Information

SUMMARYA mapping and navigation system based on certainty grids for an autonomous mobile robot operating in unknown environment is described. The system uses sonar range data to build a map of the robot's surroundings. The range data from sonar sensor are integrated into a probability map that is composed of two dimensional grids which contain the probabilities of being occupied by the objects in the environment. A Bayesian model is used to estimate the uncertainty of the sensor information and to update the existing probability map with new range data. The resulting two dimensional map is used for path planning and navigation. In this paper, the Bayesian updating model which was successfully simulated in our earlier work is implemented on a mobile robot and is shown to be valid in the real world by experiment.

scholarly journals Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

2020 ◽  
Vol 89 ◽  
pp. 106076 ◽  
Author(s):  
Fatin H. Ajeil ◽  
Ibraheem Kasim Ibraheem ◽  
Mouayad A. Sahib ◽  
Amjad J. Humaidi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Autonomous Mobile Robot ◽  
Multi Objective

scholarly journals Minimum control effort–based path planning and nonlinear guidance for autonomous mobile robots

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881263 ◽  
Author(s):  
Paul Quillen ◽  
Kamesh Subbarao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Predictive Control ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Control Effort ◽  
Control Approach ◽  
Navigation Function ◽  
Guidance Law ◽  
High Level

This article puts forth a framework using model-based techniques for path planning and guidance for an autonomous mobile robot in a constrained environment. The path plan is synthesized using a numerical navigation function algorithm that will form its potential contour levels based on the “minimum control effort.” Then, an improved nonlinear model predictive control approach is employed to generate high-level guidance commands for the mobile robot to track a trajectory fitted along the planned path leading to the goal. A backstepping-like nonlinear guidance law is also implemented for comparison with the NMPC formulation. Finally, the performance of the resulting framework using both nonlinear guidance techniques is verified in simulation where the environment is constrained by the presence of static obstacles.

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