Parallel computation of configuration space

Robotica ◽  
1996 ◽  
Vol 14 (2) ◽  
pp. 205-212 ◽  
Author(s):  
J. Solano González ◽  
D.I. Jonest
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Parallel Computer ◽  
Computer Language ◽  
Parallel Tasks ◽  
Planning Methods ◽  
Network Topologies ◽  
Speed Up ◽  
Computationally Intensive ◽  
Proper Account

SUMMARYMany motion planning methods use Configuration Space to represent a robot manipulator's range of motion and the obstacles which exist in its environment. The Cartesian to Configuration Space mapping is computationally intensive and this paper describes how the execution time can be decreased by using parallel processing. The natural tree structure of the algorithm is exploited to partition the computation into parallel tasks. An implementation programmed in the occam2 parallel computer language running on a network of INMOS transputers is described. The benefits of dynamically scheduling the tasks onto the processors are explained and verified by means of measured execution times on various processor network topologies. It is concluded that excellent speed-up and efficiency can be achieved provided that proper account is taken of the variable task lengths in the computation.

scholarly journals Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 395 ◽  
Author(s):  
Fusheng Zha ◽  
Yizhou Liu ◽  
Wei Guo ◽  
Pengfei Wang ◽  
Mantian Li ◽  
...  
Keyword(s):  
Motion Planning ◽  
Success Rate ◽  
Configuration Space ◽  
Fundamental Problem ◽  
Approximate Model ◽  
High Dimensional ◽  
Constrained Motion ◽  
Task Constraint ◽  
Task Constraints ◽  
Planning Methods

Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks.

ADD-RRV for motion planning in complex environments

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Peng Cai ◽  
Xiaokui Yue ◽  
Hongwen Zhang
Keyword(s):  
Principal Component Analysis ◽  
Motion Planning ◽  
Configuration Space ◽  
Principal Component ◽  
Component Analysis ◽  
Planning Method ◽  
Complex Environments ◽  
Local Configuration ◽  
Narrow Passage

Abstract In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.

COMPUTATIONS OF PULSATILE AORTIC BLOOD FLOW PROBLEMS ON PARALLEL COMPUTERS

2003 ◽  
Vol 15 (03) ◽  
pp. 109-114
Author(s):  
YANG-YAO NIU ◽  
SHOU-CHENG TCHENG
Keyword(s):  
Blood Flow ◽  
Stokes Equations ◽  
Computing Time ◽  
Time Integration ◽  
Parallel Computer ◽  
Aortic Blood Flow ◽  
Pc Cluster ◽  
Flow Problems ◽  
Aortic Blood ◽  
Speed Up

In this study, a parallel computing technology is applied on the simulation of aortic blood flow problems. A third-order upwind flux extrapolation with a dual-time integration method based on artificial compressibility solver is used to solve the Navier-Stokes equations. The original FORTRAN code is converted to the MPI code and tested on a 64-CPU IBM SP2 parallel computer and a 32-node PC Cluster. The test results show that a significant reduction of computing time in running the model and a super-linear speed up rate is achieved up to 32 CPUs at PC cluster. The speed up rate is as high as 49 for using IBM SP2 64 processors. The test shows very promising potential of parallel processing to provide prompt simulation of the current aortic flow problems.

IMPROVED FRACTAL IMAGE COMPRESSION BASED ON ROBUST FEATURE DESCRIPTORS

2011 ◽  
Vol 11 (04) ◽  
pp. 571-587 ◽  
Author(s):  
WILLIAM ROBSON SCHWARTZ ◽  
HELIO PEDRINI
Keyword(s):  
Image Compression ◽  
Computational Cost ◽  
Computational Time ◽  
Natural Scenes ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Feature Descriptors ◽  
High Compression ◽  
Speed Up ◽  
Computationally Intensive

Fractal image compression is one of the most promising techniques for image compression due to advantages such as resolution independence and fast decompression. It exploits the fact that natural scenes present self-similarity to remove redundancy and obtain high compression rates with smaller quality degradation compared to traditional compression methods. The main drawback of fractal compression is its computationally intensive encoding process, due to the need for searching regions with high similarity in the image. Several approaches have been developed to reduce the computational cost to locate similar regions. In this work, we propose a method based on robust feature descriptors to speed up the encoding time. The use of robust features provides more discriminative and representative information for regions of the image. When the regions are better represented, the search for similar parts of the image can be reduced to focus only on the most likely matching candidates, which leads to reduction on the computational time. Our experimental results show that the use of robust feature descriptors reduces the encoding time while keeping high compression rates and reconstruction quality.

scholarly journals A Fast Link Initialization Protocol for Beam-Steering based Cellular Backhaul Systems

2015 ◽  
Vol 7 (2) ◽  
pp. 113
Author(s):  
Markus Petri ◽  
Marcus Ehrig ◽  
Markus Günther
Keyword(s):  
State Of The Art ◽  
Beam Steering ◽  
Small Cell ◽  
Base Stations ◽  
Data Traffic ◽  
Mobile Data ◽  
User Expectations ◽  
Network Topologies ◽  
Speed Up ◽  
Directive Antennas

<p>To deal with the enormous increase of mobile data traffic, new cellular network topologies are necessary. The reduction of cell area and the usage of light-weighted base stations serving only a handful of users, commonly known as the small cell approach, seems to be a suitable solution addressing changes in user expectations and usage scenarios. This paper is an extended version of [1], where current challenges of small cell deployments were presented from a backhaul perspective. A mesh-type backhaul network topology based on beam-steering millimeter-wave systems was proposed as a future-proof solution. In this paper, we focus on a link initialization protocol for beam-steering with highly directive antennas. Special requirements and problems for link setup are analyzed. Based on that, a fast protocol for link initialization is presented and it is evaluated in terms of the resulting initialization speed-up compared to state-of-the-art solutions. Furthermore, a potential approach for extending the fast link initialization protocol to support point-to-multipoint connections is given.</p>

scholarly journals Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1890 ◽  
Author(s):  
Zijian Hu ◽  
Kaifang Wan ◽  
Xiaoguang Gao ◽  
Yiwei Zhai ◽  
Qianglong Wang
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Predictive Control ◽  
Experimental Testing ◽  
The Novel ◽  
Simulation Environment ◽  
Unknown Environments ◽  
Autonomous Motion ◽  
Policy Gradient ◽  
Speed Up

Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP–DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP–DDPG can stably complete a variety of tasks in complex, unknown environments.

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