Experiments on multi-agent capture of a stochastically moving object using modified projective path planning

Robotica ◽  
2012 ◽  
Vol 31 (2) ◽  
pp. 267-284 ◽  
Author(s):  
Vijaysingh Shinde ◽  
Ashish Dutta ◽  
Anupam Saxena
Keyword(s):  
Path Planning ◽  
Past Research ◽  
Moving Object ◽  
Processing Unit ◽  
Time Step ◽  
Object A ◽  
Central Processing ◽  
Form Closure ◽  
Minimum Number ◽  
Planning Algorithm

SUMMARYMost of the past research in swarm robotics has considered object capture and transport using a specified and very large number of agents. The objects therein were either stationary or moving deterministically (i.e., along a known path). In most previous efforts, the obstacles were also considered stationary. Here we present a modified projective path planning algorithm and illustrate via laboratory experiments that an object exhibiting stochastic (unplanned) but low-speed motion can be restrained by a limited number of agents guided in real-time across randomly moving obstacles. Relaxation of certain restrictions in the grasping objective allows for the determination of a minimum number and placement of agents around the perimeter of any generically shaped prismatic object. A closed loop experiment is designed using a single overhead camera that provides the visual feedback and helps determine the instantaneous positions of all entities in the workspace. Control signals are sent to the robots via wireless modules by a central processing unit to navigate and guide them to their respective new positions in the subsequent time-step. Agents continue to receive signals until they restrict the moving object in form closure.

Step Ring Based 3D Path Planning via GPU Simulation for Subtractive 3D Printing

2016 ◽  
Author(s):  
Zhengkai Wu ◽  
Thomas M. Tucker ◽  
Chandra Nath ◽  
Thomas R. Kurfess ◽  
Richard W. Vuduc
Keyword(s):  
3D Printing ◽  
Path Planning ◽  
Large Scale ◽  
Cnc Machining ◽  
Scale Model ◽  
Material Surface ◽  
Processing Unit ◽  
Cad Model ◽  
Set Partition ◽  
Central Processing

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring based 3-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and CNC machining via GPU simulated software. Path planning is applied through material surface removal visualization in high resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer aided design (CAD) design model as STL (stereolithography) voxel, and then local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global all automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and g-code optimization for large scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multi-level set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring based pass planning verses global all path planning. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.

scholarly journals Collision Avoidance for a Car-like Mobile Robots using Deep Reinforcement Learning

2021 ◽  
Vol 11 (11) ◽  
pp. 22-30
Author(s):  
Kiwon Yeom ◽  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Vehicle ◽  
Dynamic Environment ◽  
Time Step ◽  
Learning Path ◽  
Angular Velocities ◽  
Planning Algorithm ◽  
Path Planning Algorithm

—The applications of mobile robots are more and more diverse and extensive. The motion planning of the mobile robots should be considered in aspect of effectiveness of the navigation, and collision-free motion is essential for mobile robots. In addition, dynamic path planning of unknown environment has always been a challenge for mobile robots. Aiming at navigation problems, this paper proposes a Deep Reinforcement Learning (DRL) based path planning algorithm which can navigate nonholonomic car-like mobile robots in an unknown dynamic environment. The output of the learned network are the robot’s translational and angular velocities for the next time step. The method combines path planning on a 2D grid with reinforcement learning and does not need any supervision. The experiments illustrate that our trained policy can be applied to solve complex navigation tasks. Furthermore, we compare the performance of our learned controller to the popular approaches. Keywords— Deep reinforcement learning, path planning, , artificial neural network, mobile robot, autonomous vehicle

Path Planning for Mobile Robots in 3D Dynamic Environments

2011 ◽  
Vol 403-408 ◽  
pp. 1401-1404
Author(s):  
Li Jia Chen ◽  
He Jin ◽  
Jin Ke Bai ◽  
Hai Tao Mao
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Artificial Potential Field ◽  
Uniform Motion ◽  
Breadth First Search ◽  
Time Step ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Aiming at the robustness of the path planning of mobile robots in the 3D dynamic environment, an improved ARF (Artificial Potential Field) based path planning algorithm is proposed in this paper. Supposing that all the obstacles move regularly and the robot is on uniform motion in a grid 3D environment. Firstly, the algorithm computes the future statuses of the environment, such as the coordinate of all the obstacles and the goal, until a time step T in which there is at least one route between the start and goal. T is obtained by BFS (Breadth First Search) and environment configuration parameters. Secondly, because in every time step the environment can be consider as being static, ARF is used to determine the potential value of every space position in each time step. Finally, a route along the lowest potential values is found for the robot from the start to goal. Simulation results show that the algorithm makes the robot avoid obstacles effectively and reach the goal safely.

scholarly journals Examining Photolysis Rates with a Prototype Online Photolysis Module in CMAQ

2007 ◽  
Vol 46 (8) ◽  
pp. 1252-1256 ◽  
Author(s):  
Francis S. Binkowski ◽  
Saravanan Arunachalam ◽  
Zachariah Adelman ◽  
Joseph P. Pinto
Keyword(s):  
Nitrogen Dioxide ◽  
Cross Sections ◽  
Lookup Table ◽  
Processing Unit ◽  
Computationally Efficient ◽  
Time Step ◽  
Central Processing ◽  
Online Module ◽  
Table Method ◽  
Photolysis Rates

Abstract A prototype online photolysis module has been developed for the Community Multiscale Air Quality (CMAQ) modeling system. The module calculates actinic fluxes and photolysis rates (j values) at every vertical level in each of seven wavelength intervals from 291 to 850 nm, as well as the total surface irradiance and aerosol optical depth within each interval. The module incorporates updated opacity at each time step, based on changes in local ozone, nitrogen dioxide, and particle concentrations. The module is computationally efficient and requires less than 5% more central processing unit time than using the existing CMAQ “lookup” table method for calculating j values. The main focus of the work presented here is to describe the new online module as well as to highlight the differences between the effective cross sections from the lookup-table method currently being used and the updated effective cross sections from the new online approach. Comparisons of the vertical profiles for the photolysis rates for nitrogen dioxide (NO2) and ozone (O3) from the new online module with those using the effective cross sections from a standard CMAQ simulation show increases in the rates of both NO2 and O3 photolysis.

scholarly journals Fast Path Planning for Autonomous Ships in Restricted Waters

2018 ◽  
Vol 8 (12) ◽  
pp. 2592 ◽  
Author(s):  
Hongguang Lyu ◽  
Yong Yin
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Gradient Methods ◽  
Artificial Potential Field ◽  
Time Step ◽  
Human Errors ◽  
International Regulations ◽  
Planning Algorithm ◽  
Gradient Based ◽  
Autonomous Ship

Presently, there is increasing interest in autonomous ships to reduce human errors and support intelligent navigation, where automatic collision avoidance and path planning is a key problem, especially in restricted waters. To solve this problem, a path-guided hybrid artificial potential field (PGHAPF) method is first proposed in this paper. It is essentially a reactive path-planning algorithm that provides fast feedback in a changeable environment, including dynamic target ships (TSs) and static obstacles, for steering an autonomous ship safely. The proposed strategy, which is a fusion of the potential field and gradient methods, consists of potential-based path planning for arbitrary static obstacles, gradient-based decision-making for dynamic TSs, and their combination with consideration of the prior path and waypoint selection optimization. A three-degree-of-freedom dynamic model of a Mariner class vessel and a low-level controller have been incorporated together in this method to ensure that the vessel’s positions are updated at each time step in order to acquire a more applicable and reliable trajectory. Simulations show that the PGHAPF method has the potential to rapidly generate adaptive, collision-free and International Regulations for Preventing Collisions at Sea (COLREGS)-constrained trajectories in restricted waters by deterministic calculations. Furthermore, this method has the potential to perform path planning on an electronic chart platform and to overcome some drawbacks of traditional artificial potential field (APF) methods.

scholarly journals Analysis of the performance of a hybrid CPU/GPU 1D2D coupled model for real flood cases

2020 ◽  
Vol 22 (5) ◽  
pp. 1198-1216
Author(s):  
Isabel Echeverribar ◽  
Mario Morales-Hernández ◽  
Pilar Brufau ◽  
Pilar García-Navarro
Keyword(s):  
High Speed ◽  
High Performance ◽  
Data Transfer ◽  
Coupled Model ◽  
Main Channel ◽  
Processing Unit ◽  
Step Size ◽  
Time Step ◽  
Time Step Size ◽  
Central Processing

Abstract Coupled 1D2D models emerged as an efficient solution for a two-dimensional (2D) representation of the floodplain combined with a fast one-dimensional (1D) schematization of the main channel. At the same time, high-performance computing (HPC) has appeared as an efficient tool for model acceleration. In this work, a previously validated 1D2D Central Processing Unit (CPU) model is combined with an HPC technique for fast and accurate flood simulation. Due to the speed of 1D schemes, a hybrid CPU/GPU model that runs the 1D main channel on CPU and accelerates the 2D floodplain with a Graphics Processing Unit (GPU) is presented. Since the data transfer between sub-domains and devices (CPU/GPU) may be the main potential drawback of this architecture, the test cases are selected to carry out a careful time analysis. The results reveal the speed-up dependency on the 2D mesh, the event to be solved and the 1D discretization of the main channel. Additionally, special attention must be paid to the time step size computation shared between sub-models. In spite of the use of a hybrid CPU/GPU implementation, high speed-ups are accomplished in some cases.

Perangkat keras komputer

2020 ◽  
Author(s):  
Roudati jannah
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras premprosesan (processing/central processing unit) – perangkat keras luaran (output/output device system) – perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (storage device system/external memory).

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