Geometric Work of Manipulators and Path Planning Based on Minimum Energy Consumption

1992 ◽  
Vol 114 (3) ◽  
pp. 414-421 ◽  
Author(s):  
Li-Shan Chou ◽  
Shin-Min Song
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Minimum Energy ◽  
Dynamic Programming Method ◽  
Continuous Path ◽  
End Effector ◽  
Minimum Energy Consumption ◽  
Special Geometry ◽  
Pick And Place ◽  
Regular Open

The energy efficiency of the robots of today’s generation is in general very poor due to the existence of “geometric work.” The geometric work is geometry dependent and can be eliminate by adopting a special geometry which decouples the gravitational motion from the horizontal motions. Instead of adopting a special geometry, this paper studies the geometric work of a regular open-chained manipulator and applies it to the path planning for minimum energy consumption. For a given manipulator geometry and end-effector position, the zones of velocity with zero geometric work are determined analytically. A map which describes these zones of zero geometric work at various positions in workspace is then constructed for path planning with zero geometric work. The path planning for minimum energy consumption is generated by the dynamic programming method and the results are compared with the map of zero geometric work. It is found that the end-effector tends to move within the zones of zero geometric work as much as possible. If the end-effector has to cross the boundary of a zone at some point, it again moves within the zones after crossing the boundary. The presented method can also be used to arrange the pick and place positions for minimum travel energy consumption. That is, the two positions should be selected so that a continuous path which connects them with zero geometric work and with monotonously ascending or descending features is available.

scholarly journals Optimization of Energy-Efficient Speed Profile for Electrified Vehicles

2018 ◽  
Author(s):  
Hadi Abbas ◽  
Youngki Kim ◽  
Jason B. Siegel ◽  
Denise M. Rizzo
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Minimum Energy ◽  
Efficient Operation ◽  
Operating Modes ◽  
Minimum Energy Consumption ◽  
System A ◽  
Trip Time ◽  
Route Information

This paper presents a study of energy-efficient operation of vehicles with electrified powertrains leveraging route information, such as road grades, to adjust the speed trajectory. First, Pontryagin’s Maximum Principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only 5 modes are required to achieve minimum energy consumption; full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. The minimum energy consumption problem is reformulated and solved in the distance domain using Dynamic Programming to optimize speed profiles. A case study is shown for a light weight military robot including road grades. For this system, a tradeoff between energy consumption and trip time was found. The optimal cycle uses 20% less energy for the same trip duration, or could reduce the travel time by 14% with the same energy consumption compared to the baseline operation.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

From Construction to Operation: Achieving Indoor Thermal Comfort via Altering External Walls Specifications in Egypt

2013 ◽  
Vol 689 ◽  
pp. 250-253 ◽  
Author(s):  
Mohamed M. Mahdy ◽  
Marialena Nikolopoulou
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Natural Ventilation ◽  
Minimum Energy ◽  
Residential Energy ◽  
Indoor Thermal Comfort ◽  
Minimum Energy Consumption ◽  
Different Types ◽  
Running Cost ◽  
The Cost

The objective of this research is to study the effect of using different material specifications for the external walls on the cost of the energy consumption for achieving internal thermal comfort. We refer to this as operation running cost, which in turn is compared to initial construction cost for each type of the used external walls. In order to achieve this objective, dynamic thermal simulation were carried out for four different types of external walls – commonly used in Egypt – in two different sets of cooling: natural ventilation and mechanical means. Experiments recommend that using the Egyptian Residential Energy Code (EREC) to achieve inner thermal comfort with the minimum energy consumption (consequently the minimum CO2 emissions) and the minimum running cost as well.

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