MODEL BASED CONTINUAL PLANNING AND CONTROL FOR ASSISTIVE ROBOTS

2012 ◽  
Keyword(s):  
Assistive Robots ◽  
Model Based ◽  
Planning And Control ◽  
And Control

On-Line Reconfigurable Machines

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
Author(s):  
Lara S. Crawford
Keyword(s):  
Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Planning And Scheduling ◽  
Model Based ◽  
Planning And Control ◽  
Resource Reasoning ◽  
On Line ◽  
Scheduling Heuristic ◽  
High Level ◽  
And Control

A recent trend in intelligent machines and manufacturing has been toward reconfigurable manufacturing systems, which move away from the idea of a fixed factory line executing an unchanging set of operations, and toward the goal of an adaptable factory structure. The logical next challenge in this area is that of on-line reconfigurability. With this capability, machines can reconfigure while running, enable or disable capabilities in real time, and respond quickly to changes in the system or the environment (including faults). We propose an approach to achieving on-line reconfigurability based on a high level of system modularity supported by integrated, model-based planning and control software. Our software capitalizes on many advanced techniques from the artificial intelligence research community, particularly in model-based domain-independent planning and scheduling, heuristic search, and temporal resource reasoning. We describe the implementation of this design in a prototype highly modular, parallel printing system.

scholarly journals A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

2018 ◽  
Author(s):  
Liting Sun ◽  
Cheng Peng ◽  
Wei Zhan ◽  
Masayoshi Tomizuka
Keyword(s):  
Nonlinear Models ◽  
Autonomous Driving ◽  
Optimization Methods ◽  
Imitation Learning ◽  
Short Term ◽  
Model Based ◽  
Planning And Control ◽  
And Control ◽  
Optimal Driving

Safety and efficiency are two key elements for planning and control in autonomous driving. Theoretically, model-based optimization methods, such as Model Predictive Control (MPC), can provide such optimal driving policies. Their computational complexity, however, grows exponentially with horizon length and number of surrounding vehicles. This makes them impractical for real-time implementation, particularly when nonlinear models are considered. To enable a fast and approximately optimal driving policy, we propose a safe imitation framework, which contains two hierarchical layers. The first layer, defined as the policy layer, is represented by a neural network that imitates a long-term expert driving policy via imitation learning. The second layer, called the execution layer, is a short-term model-based optimal controller that tracks and further fine-tunes the reference trajectories proposed by the policy layer with guaranteed short-term collision avoidance. Moreover, to reduce the distribution mismatch between the training set and the real world, Dataset Aggregation is utilized so that the performance of the policy layer can be improved from iteration to iteration. Several highway driving scenarios are demonstrated in simulations, and the results show that the proposed framework can achieve similar performance as sophisticated long-term optimization approaches but with significantly improved computational efficiency.

scholarly journals Sustainability in Model-based Planning and Control of Global Value Creation Networks

2017 ◽  
Vol 8 ◽  
pp. 183-190 ◽  
Author(s):  
N. Oertwig ◽  
R. Jochem ◽  
T. Knothe
Keyword(s):  
Value Creation ◽  
Model Based ◽  
Planning And Control ◽  
And Control

On-Line Reconfigurable Machines

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 73-88 ◽  
Author(s):  
Lara S. Crawford ◽  
Minh Binh Do ◽  
Wheeler S. Ruml ◽  
Haitham Hindi ◽  
Craig Eldershaw ◽  
...  
Keyword(s):  
Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Planning And Scheduling ◽  
Model Based ◽  
Planning And Control ◽  
Resource Reasoning ◽  
On Line ◽  
Scheduling Heuristic ◽  
High Level ◽  
And Control

A recent trend in intelligent machines and manufacturing has been toward reconfigurable manufacturing systems, which move away from the idea of a fixed factory line executing an unchanging set of operations, and toward the goal of an adaptable factory structure. The logical next challenge in this area is that of on-line reconfigurability. With this capability, machines can reconfigure while running, enable or disable capabilities in real time, and respond quickly to changes in the system or the environment (including faults). We propose an approach to achieving on-line reconfigurability based on a high level of system modularity supported by integrated, model-based planning and control software. Our software capitalizes on many advanced techniques from the artificial intelligence research community, particularly in model-based domain-independent planning and scheduling, heuristic search, and temporal resource reasoning. We describe the implementation of this design in a prototype highly modular, parallel printing system.

Model-based process planning and control: laser beam welding with CALAS

1997 ◽  
Author(s):  
Michael J. Dahmen ◽  
B. Fuerst ◽  
Stefan Kaierle ◽  
Ernst-Wolfgang Kreutz ◽  
Reinhart Poprawe ◽  
...  
Keyword(s):  
Laser Beam ◽  
Process Planning ◽  
Laser Beam Welding ◽  
Model Based ◽  
Planning And Control ◽  
And Control ◽  
Control Laser

Modellbasierte Auswertung von Echtzeitdaten*/Model-based analysis of real-time data for the automated production planning and control in a medium-sized company

2019 ◽  
Vol 109 (03) ◽  
pp. 174-178
Author(s):  
D. Stalinski ◽  
D. Scholz
Keyword(s):  
Production Planning ◽  
Production Planning And Control ◽  
Time Data ◽  
Automated Production ◽  
Wood Processing ◽  
Model Based ◽  
Planning And Control ◽  
Real Time Data ◽  
And Control ◽  
Model Based Analysis

Der Beitrag stellt ein modellgestütztes Verfahren vor, das die Vorgabezeiten für die Planung und Steuerung eines Produktionsprozesses auf Basis von RFID-Rückmeldedaten generiert. Das Verfahren ist ein wichtiger Baustein zur Automatisierung der Produktionsplanung und -steuerung in einem mittelständischen Betrieb aus der Holzverarbeitung.   This paper presents a model-based approach for generating the expected producing times for planning and controlling a production process based on RFID confirmation data. The method is an important part of an automated production planning and control in a medium-sized wood processing company.

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