Energy-Efficient Path Planning Algorithm on Three-Dimensional Large-Scale Terrain Maps for Mobile Robots

2020 ◽  
pp. 319-330
Author(s):  
Konstantin Zakharov ◽  
Anton Saveliev ◽  
Oleg Sivchenko
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Energy Efficient ◽  
Large Scale ◽  
Three Dimensional ◽  
Planning Algorithm ◽  
Path Planning Algorithm

A Path Planning Algorithm for Mobile Robots Based on DGABI-RRT

2021 ◽  
pp. 554-564
Author(s):  
Qingdang Li ◽  
Hui Zhao ◽  
Mingyue Zhang ◽  
Zhen Sun
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals An energy-efficient path planning algorithm for unmanned surface vehicles

2018 ◽  
Vol 161 ◽  
pp. 308-321 ◽  
Author(s):  
Hanlin Niu ◽  
Yu Lu ◽  
Al Savvaris ◽  
Antonios Tsourdos
Keyword(s):  
Path Planning ◽  
Energy Efficient ◽  
Unmanned Surface Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals 2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuexi Zhang ◽  
Jiajun Lai ◽  
Dongliang Xu ◽  
Huaijun Li ◽  
Minyue Fu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Basic System ◽  
Rescue Robot ◽  
Planning Algorithm ◽  
Local Path ◽  
Slam Algorithm ◽  
Path Planning Algorithm

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

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.

Kinematics of a Novel Single-Loop Under-Actuated Wrist

2012 ◽  
Author(s):  
Raffaele Di Gregorio
Keyword(s):  
Path Planning ◽  
Closed Form ◽  
Nonholonomic Constraint ◽  
Three Dimensional ◽  
Single Loop ◽  
Closed Form Solutions ◽  
Position Analysis ◽  
Tracking Tasks ◽  
Planning Algorithm ◽  
Path Planning Algorithm

A novel type of parallel wrist (PW) is proposed which, differently from previously presented PWs, features a single-loop architecture and only one nonholonomic constraint. Due to the presence of a nonholonomic constraint, the proposed PW type is under-actuated, that is, it is able to control the platform orientation in a three-dimensional workspace by employing only two actuated pairs, one prismatic (P) and the other revolute (R); and it cannot perform tracking tasks. Position analysis and path planning of this novel PW are studied. In particular, all the relevant position analysis problems are solved in closed form, and, based on these closed-form solutions, a path-planning algorithm is built.

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