Visual and Tactile-Based Terrain Analysis Using a Cylindrical Mobile Robot

2005 ◽  
Vol 128 (1) ◽  
pp. 165-170 ◽  
Author(s):  
Giulio Reina ◽  
Mario M. Foglia ◽  
Annalisa Milella ◽  
Angelo Gentile
Keyword(s):  
Mobile Robot ◽  
Rolling Resistance ◽  
Tactile Sensor ◽  
Autonomous Mobile Robots ◽  
Rolling Motion ◽  
Rough Terrain ◽  
Terrain Analysis ◽  
Military Applications ◽  
Planning Methods ◽  
The University

Ground autonomous mobile robots have important applications, such as reconnaissance, patrol, planetary exploration, and military applications. In order to accomplish tasks on rough terrain, control and planning methods must consider the physical characteristics of the vehicle and of its environment. Failure to understand these characteristics could lead to vehicle endangement and consequent mission failure. This paper describes recent and current work at the Politecnico of Bari in collaboration with the University of Lecce in the area of deformable terrain mobility and sensing. A cylindrical mobile robot is presented and its rolling motion on terrain is studied from a theoretical and experimental prospect. A comprehensive model is developed taking into account the interaction of the vehicle with the terrain and the related dynamic ill effects, such as rolling resistance and slip, and it is experimentally validated. An unconventional application of the vehicle serving as a tactile sensor is discussed and experimental results are presented showing the effectiveness of the cylindrical mobile robot in estimating the properties of homogeneous, deformable terrain, which in turn can be used to assess the vehicle traversability.

Dynamic Modeling for a Cylindrical Mobile Robot on Rough-Terrain

2004 ◽  
Author(s):  
Giulio Reina ◽  
Mario Foglia ◽  
Annalisa Milella ◽  
Angelo Gentile
Keyword(s):  
Mobile Robot ◽  
Dynamic Model ◽  
Autonomous Vehicles ◽  
Mobile Robotics ◽  
Tactile Sensor ◽  
Physical Models ◽  
Rough Terrain ◽  
Potential Applications ◽  
Planning Methods ◽  
Vehicle Dynamic Model

Ground autonomous mini-mobile robots have important potential applications, such as reconnaissance, patrol, planetary exploration and military applications. To accomplish tasks on rough-terrain, control and planning methods must consider the physical characteristics of the vehicle and of its environment. Failure to understand these characteristics could lead to vehicle endangerment and mission failure. This paper describes recent and current work at Mobile Robotics Laboratory of the Politecnico of Bari in the area of rough terrain mobility and traversability of autonomous vehicles. A cylindrical shaped mobile robot is presented and its rolling motion on rough terrain is studied from both theoretical and experimental prospect. A comprehensive vehicle dynamic model is proposed based on well-established physical models of mobile robot-terrain interaction. The model is experimentally validated and it allows employing the vehicle as a tactile sensor for terrain characterization and identification. Innovative vision-based-methods are also introduced for estimating relevant kinematic parameters of the vehicle motion. It is shown that the dynamic model can describe efficiently the vehicle behavior and could enhance its mobility on rough-terrain through integration with control and planning algorithms.

scholarly journals Autonomous Robots Path Planning: An Adaptive Roadmap Approach

2013 ◽  
Vol 373-375 ◽  
pp. 246-254 ◽  
Author(s):  
Mohamed Elbanhawi ◽  
Milan Simic ◽  
Reza Jazar
Keyword(s):  
Computational Complexity ◽  
Path Planning ◽  
Autonomous Robots ◽  
Autonomous Mobile Robots ◽  
Current Position ◽  
Military Applications ◽  
Planning Methods ◽  
Planning Algorithm ◽  
Real Time Applications ◽  
Path Planning Algorithm

Developing algorithms that allow robots to independently navigate unknown environments is a widely researched area of robotics. The potential for autonomous mobile robots use, in industrial and military applications, is boundless. Path planning entails computing a collision free path from a robots current position to a desired target. The problem of path planning for these robots remains underdeveloped. Computational complexity, path optimization and robustness are some of the issues that arise. Current algorithms do not generate general solutions for different situations and require user experience and optimization. Classical algorithms are computationally extensive. This reduces the possibility of their use in real time applications. Additionally, classical algorithms do not allow for any control over attributes of the generated path. A new roadmap path planning algorithm is proposed in this paper. This method generates waypoints, through which the robot can avoid obstacles and reach its goal. At the heart of this algorithm is a method to control the distance of the waypoints from obstacles, without increasing its computational complexity. Several simulations were run to illustrate the robustness and adaptability of this approach, compared to the most commonly used path planning methods.

3-D Scan Matching for Mobile Robot Localization over Rough Terrain

2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Rough Terrain ◽  
Robot Localization ◽  
Scan Matching ◽  
Mobile Robot Localization

scholarly journals Unevenness Point Descriptor for Terrain Analysis in Mobile Robot Applications

10.5772/56240 ◽  
2013 ◽  
Vol 10 (7) ◽  
pp. 284 ◽  
Author(s):  
Mauro Bellone ◽  
Giulio Reina ◽  
Nicola I. Giannoccaro ◽  
Luigi Spedicato
Keyword(s):  
Mobile Robot ◽  
Terrain Analysis

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.

Expert Guided Autonomous Mobile Robot Learning

2011 ◽  
pp. 216-231
Author(s):  
Gintautas Narvydas ◽  
Vidas Raudonis ◽  
Rimvydas Simutis
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Control Systems ◽  
Intelligent Control ◽  
Main Idea ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Intelligent Control System ◽  
Objective Function Values

In the control of autonomous mobile robots there exist two types of control: global control and local control. The requirement to solve global and local tasks arises respectively. This chapter concentrates on local tasks and shows that robots can learn to cope with some local tasks within minutes. The main idea of the chapter is to show that, while creating intelligent control systems for autonomous mobile robots, the beginning is most important as we have to transfer as much as possible human knowledge and human expert-operator skills into the intelligent control system. Successful transfer ensures fast and good results. One of the most advanced techniques in robotics is an autonomous mobile robot on-line learning from the experts’ demonstrations. Further, the latter technique is briefly described in this chapter. As an example of local task the wall following is taken. The main goal of our experiment is to teach the autonomous mobile robot within 10 minutes to follow the wall of the maze as fast and as precisely as it is possible. This task also can be transformed to the obstacle circuit on the left or on the right. The main part of the suggested control system is a small Feed-Forward Artificial Neural Network. In some particular cases – critical situations – “If-Then” rules undertake the control, but our goal is to minimize possibility that these rules would start controlling the robot. The aim of the experiment is to implement the proposed technique on the real robot. This technique enables to reach desirable capabilities in control much faster than they would be reached using Evolutionary or Genetic Algorithms, or trying to create the control systems by hand using “If-Then” rules or Fuzzy Logic. In order to evaluate the quality of the intelligent control system to control an autonomous mobile robot we calculate objective function values and the percentage of the robot work loops when “If-Then” rules control the robot.

scholarly journals Chaotic Path Planner of Autonomous Mobile Robots Based on the Standard Map for Surveillance Missions

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Caihong Li ◽  
Yong Song ◽  
Fengying Wang ◽  
Zhenying Liang ◽  
Baoyan Zhu
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Small Region ◽  
Autonomous Mobile Robots ◽  
Large Region ◽  
Standard Map ◽  
Path Planner

This paper proposes a fusion iterations strategy based on the Standard map to generate a chaotic path planner of the mobile robot for surveillance missions. The distances of the chaotic trajectories between the adjacent iteration points which are produced by the Standard map are too large for the robot to track. So a fusion iterations strategy combined with the large region iterations and the small grids region iterations is designed to resolve the problem. The small region iterations perform the iterations of the Standard map in the divided small grids, respectively. It can reduce the adjacent distances by dividing the whole surveillance workspace into small grids. The large region iterations combine all the small grids region iterations into a whole, switch automatically among the small grids, and maintain the chaotic characteristics of the robot to guarantee the surveillance missions. Compared to simply using the Standard map in the whole workspace, the proposed strategy can decrease the adjacent distances according to the divided size of the small grids and is convenient for the robot to track.

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