Minimizing the Energy Loss of the Bi-Articular Actuation in Bipedal Robots

2015 ◽  
Author(s):  
Derek Lahr ◽  
Hak Yi ◽  
Dennis Hong
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Energy Consumption ◽  
Energy Loss ◽  
Energy Losses ◽  
Bipedal Robots ◽  
Bipedal Robot ◽  
Optimum Configuration ◽  
Actuator Placement ◽  
Theoretical Energy Consumption

In this work, we investigate the effect actuator position on the theoretical energy consumption of an electrically powered bipedal robot. Specifically, considerable gains are possible through the optimization of the actuator placement relative to joints and their axes, in particular biarticular actuators, are proposed. The energy losses of electric actuators on the two most powerful and inefficient joints of a biped, the hip and the knee, are considered. In standing or walking tasks of a biped’s legs within mathematical model, furthermore, imposing constraints on the actuator placement is used in a genetic algorithm to find the optimum configuration of various biarticular configurations.

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

scholarly journals Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhuofan Liao ◽  
Jingsheng Peng ◽  
Bing Xiong ◽  
Jiawei Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Physical Distance ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Average Delay ◽  
Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

Research on Jobshop Scheduling Problem with Energy Consumption

2011 ◽  
Vol 314-316 ◽  
pp. 2071-2075
Author(s):  
Jia Hai Wang ◽  
Wen Tao Gong
Keyword(s):  
Mathematical Model ◽  
Energy Consumption ◽  
Processing Time ◽  
Electric Energy ◽  
Low Carbon ◽  
Load Shifting ◽  
Electric Energy Consumption ◽  
Jobshop Scheduling ◽  
Time Division ◽  
The Mathematical Model

Discrete machine manufacture enterprises have to induce new low-carbon manufacturing model in order to solve a dilemma of mutual restraint between development and electric energy consumption. The paper presents an approach to solve JSP with the objective of minimizing the energy consumption by shortening the distance between electricity peak and valley according to theory of load shifting in electricity. The mathematical model is proposed for JSP with objective of minimizing the energy consumption and processing time of entire batch, then the idea of time division is introduced, and a solving method based on GA built-in eM-Plant is employed to verify the model and get satisfactory scheduling results.Discrete machine manufacture enterprises have to induce new low-carbon manufacturing model in order to solve a dilemma of mutual restraint between development and electric energy consumption. The paper presents an approach to solve JSP with the objective of minimizing the energy consumption by shortening the distance between electricity peak and valley according to theory of load shifting in electricity. The mathematical model is proposed for JSP with objective of minimizing the energy consumption and processing time of entire batch, then the idea of time division is introduced, and a solving method based on GA built-in eM-Plant is employed to verify the model and get satisfactory scheduling results.

scholarly journals Using a Genetic Algorithm to Parameterize a Mathematical Model of a Porcine Ventricular Cardiomyocyte

2021 ◽  
Vol 120 (3) ◽  
pp. 333a
Author(s):  
Taylor K. Pullinger ◽  
Matthew Amoni ◽  
Itziar Irurzun-Arana ◽  
Karin R. Sipido ◽  
Eric A. Sobie
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model
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