scholarly journals Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments

2021 ◽  
Vol 11 (10) ◽  
pp. 4362
Author(s):  
Liang Gong ◽  
Xiangyu Yu ◽  
Jingchuan Wang
Keyword(s):  
Mobile Robots ◽  
Working Environment ◽  
Localization Algorithm ◽  
Complex Environments ◽  
Planning Strategy ◽  
Outdoor Scenes ◽  
New Strategy ◽  
Outdoor Environments ◽  
Planning Algorithm ◽  
Active Localization

Working environment of mobile robots has gradually expanded from indoor structured scenes to outdoor scenes such as wild areas in recent years. The expansion of application scene, change of sensors and the diversity of working tasks bring greater challenges and higher demands to active localization for mobile robots. The efficiency and stability of traditional localization strategies in wild environments are significantly reduced. On the basis of considering features of the environment and the robot motion curved surface, this paper proposes a curve-localizability-SVM active localization algorithm. Firstly, we present a curve-localizability-index based on 3D observation model, and then based on this index, a curve-localizability-SVM path planning strategy and an improved active localization method are proposed. Obtained by setting the constraint space and objective function of the planning algorithm, where curve-localizability is the main constraint, the path helps improve the convergence speed and stability in complex environments of the active localization algorithm. Helped by SVM, the path is smoother and safer for large robots. The algorithm was tested by comparative experiments and analysis in real environment and robot platform, which verified the improvement of efficiency and stability of the new strategy.

scholarly journals Development and Implementation of Spline-based Path Planning Algorithm in ROS/Gazebo Environment

2019 ◽  
Vol 18 (1) ◽  
pp. 57-84 ◽  
Author(s):  
Lavrenov Lavrenov ◽  
Evgeni Magid ◽  
Matsuno Fumitoshi ◽  
Mikhail Svinin ◽  
Jackrit Suthakorn
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Cost Function ◽  
Working Environment ◽  
Autonomous Mobile Robots ◽  
Dynamic Tuning ◽  
Starting Point ◽  
Geometric Point ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment. In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria. This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point. To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms. The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.

scholarly journals Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*

2020 ◽  
Vol 10 (21) ◽  
pp. 7846
Author(s):  
Hyejeong Ryu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Search Algorithm ◽  
Random Tree ◽  
Graph Search ◽  
Complex Environments ◽  
Sampling Process ◽  
Planning Algorithm ◽  
Local Grid ◽  
Path Planning Algorithm

An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.

Motion Planning for Deformable Linear Objects Under Multiple Constraints

Robotica ◽  
2019 ◽  
Vol 38 (5) ◽  
pp. 819-830
Author(s):  
Jiangtao Ma ◽  
Jianhua Liu ◽  
Xiaoyu Ding ◽  
Naijing Lv
Keyword(s):  
Motion Planning ◽  
Random Tree ◽  
The Other ◽  
Elastic Rods ◽  
Multiple Constraints ◽  
Complex Environments ◽  
Tree Algorithm ◽  
Planning Strategy ◽  
New Strategy

SUMMARYDeformable linear objects (DLOs) have a wide variety of applications in a range of fields. Their key characteristic is that they extend much further in one of their dimensions than in the other two. Accurate motion planning is particularly important in the case of DLOs used in robotics applications. In this paper, a new strategy for planning the motions of DLOs under multiple constraints is proposed. The DLO was modeled as Cosserat elastic rods so that the deformation is simulated accurately and efficiently. The control of the motion of the DLO was enhanced by supplementing one gripper installed at each end with additional supports. This allows DLOs to undergo complex deformations, and thus avoid collisions during motion. The appropriate number of supports and their positions were determined, and then a rapidly exploring random tree algorithm was used to search for the best path to guide the DLO toward its target destination. The motion of the simulated DLO is described as it is controlled using two grippers and specific numbers of supports. To prove that the proposed DLO motion planning strategy can successfully guide relatively long DLOs through complex environments without colliding with obstacles, a case study of the strategy was conducted when guiding a DLO through a puzzle.

Online Modelling and Tracking Control of Mobile Robots with Slippage in Outdoor Environments

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Bo ZHOU ◽  
Xianzhong DAI ◽  
Jianda HAN
Keyword(s):  
Mobile Robots ◽  
Tracking Control ◽  
Outdoor Environments

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

scholarly journals Environment Mapping Robot Using Supersonic Sensor

1999 ◽  
Vol 11 (6) ◽  
pp. 468-472
Author(s):  
Masafumi Uchida ◽  
◽  
Tanaka Hisaya ◽  
Hideto Ide ◽  
Keyword(s):  
Computer Simulation ◽  
Path Planning ◽  
Autonomous Robot ◽  
Working Environment ◽  
Occupancy Grid ◽  
Environment Map ◽  
Planning Algorithm ◽  
And Performance ◽  
Path Planning Algorithm ◽  
Range Sensors

We studied an automapping algorithm for an autonomous robot having ultrasonic range sensors. A robot with a working environment map operates smoothly. The robot consisted of an automapping algorithm using ultrasonic range sensors and a path planning algorithm. Ultrasonic range sensors are basic, inexpensive, and compact. We proposed an automapping algorithm introducing a parameter, valid length, for a robot with ultrasonic range sensors. The map was based on an occupancy grid. Computer simulation confirmed the effectiveness of introducing valid length in mapping by an autonomous robot. We discuss proposed distinctions and performance.

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

2010 ◽  
Vol 24 (1-2) ◽  
pp. 179-206 ◽  
Author(s):  
Y. L. Ip ◽  
A. B. Rad ◽  
Y. K. Wong ◽  
Y. Liu ◽  
X. M. Ren
Keyword(s):  
Kalman Filter ◽  
Mobile Robots ◽  
Extended Kalman Filter ◽  
Autonomous Mobile Robots ◽  
Localization Algorithm
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