The Role of Motion Planning in Robotics

2015 ◽  
Vol 811 ◽  
pp. 311-317
Author(s):  
Ellips Masehian
Keyword(s):  
Motion Planning ◽  
Computer Science ◽  
Robotic Systems ◽  
Robot Motion ◽  
Planning Problem ◽  
Robot Motion Planning ◽  
Wide Range ◽  
Vast Literature ◽  
High Level

As robotic systems evolve and get more sophisticated, expectations of them to accomplish high-level tasks increase gradually and their motion planning becomes more complex and difficult. The motion planning problem has been studied for more than four decades from different aspects such that presently has a vast literature. This paper investigates different components of the robot motion planning (RMP) problem and presents a new comprehensive taxonomy for a wide range of RMP problems. The taxonomy is based on a survey of the literature on RMP problems and applications in robotics and computer science.

scholarly journals Effective metrics for multi-robot motion-planning

2018 ◽  
Vol 37 (13-14) ◽  
pp. 1741-1759 ◽  
Author(s):  
Aviel Atias ◽  
Kiril Solovey ◽  
Oren Salzman ◽  
Dan Halperin
Keyword(s):  
Motion Planning ◽  
Success Rate ◽  
Shape Matching ◽  
Equivalence Classes ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
The Novel ◽  
Multi Robot

We study the effectiveness of metrics for multi-robot motion-planning (MRMP) when using rapidly-exploring random tree (RRT)-style sampling-based planners. These metrics play the crucial role of determining the nearest neighbors of configurations and in that they regulate the connectivity of the underlying roadmaps produced by the planners and other properties such as the quality of solution paths. After screening over a dozen different metrics we focus on the five most promising ones: two more traditional metrics, and three novel ones, which we propose here, adapted from the domain of shape-matching. In addition to the novel multi-robot metrics, a central contribution of this work are tools to analyze and predict the effectiveness of metrics in the MRMP context. We identify a suite of possible substructures in the configuration space, for which it is fairly easy: (i) to define a so-called natural distance that allows us to predict the performance of a metric, which is done by comparing the distribution of its values for sampled pairs of configurations to the distribution induced by the natural distance; and (ii) to define equivalence classes of configurations and test how well a metric covers the different classes. We provide experiments that attest to the ability of our tools to predict the effectiveness of metrics: those metrics that qualify in the analysis yield higher success rate of the planner with fewer vertices in the roadmap. We also show how combining several metrics together may lead to better results (success rate and size of roadmap) than using a single metric.

scholarly journals CATEGORY AND TOPOLOGICAL COMPLEXITY OF THE CONFIGURATION SPACE

2019 ◽  
Vol 100 (3) ◽  
pp. 507-517
Author(s):  
CESAR A. IPANAQUE ZAPATA
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Rigid Bodies ◽  
Robot Motion ◽  
Planning Problem ◽  
Topological Complexity ◽  
Robot Motion Planning ◽  
Spatial Motion ◽  
Lusternik Schnirelmann ◽  
Homotopy Invariants

The Lusternik–Schnirelmann category cat and topological complexity TC are related homotopy invariants. The topological complexity TC has applications to the robot motion planning problem. We calculate the Lusternik–Schnirelmann category and topological complexity of the ordered configuration space of two distinct points in the product $G\times \mathbb{R}^{n}$ and apply the results to the planar and spatial motion of two rigid bodies in $\mathbb{R}^{2}$ and $\mathbb{R}^{3}$ respectively.

Extending RRT for Robot Motion Planning with SLAM

2012 ◽  
Vol 151 ◽  
pp. 493-497 ◽  
Author(s):  
Hai Zhu Pan ◽  
Jin Xue Zhang
Keyword(s):  
Genetic Algorithms ◽  
Mobile Robot ◽  
Motion Planning ◽  
Cell Decomposition ◽  
Potential Fields ◽  
Robot Motion ◽  
Planning Problem ◽  
Robot Motion Planning ◽  
The Past ◽  
Road Maps

In this paper,the motion planning problem for mobile robot is addressed. Motion planning (MP) has diversified over the past few decades to include many different approaches such as cell decomposition, road maps, potential fields, and genetic algorithms. Often the goal of motion planning is not just obstacle avoidance but optimization of certain parameters as well. A motion planning algorithms based on Rapidly-exploring random Tree(RRT) is present in the paper. Then the RRT algorithm has been extended which combines the SLAM algorithm.The Extend-RRT-SLAM has been simulated in MobileSim.Simulation results show Extend-RRT-SLAM to be very effective for robot motion planning.

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