scholarly journals Genetic Scheduling and Reinforcement Learning in Multirobot Systems for Intelligent Warehouses

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jiajia Dou ◽  
Chunlin Chen ◽  
Pei Yang
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
State Of The Art ◽  
Dynamic Environments ◽  
Search Method ◽  
Multirobot Systems ◽  
Evolutionary Search ◽  
Hybrid Solution ◽  
Overall Efficiency ◽  
Simulation Results

A new hybrid solution is presented to improve the efficiency of intelligent warehouses with multirobot systems, where the genetic algorithm (GA) based task scheduling is combined with reinforcement learning (RL) based path planning for mobile robots. Reinforcement learning is an effective approach to search for a collision-free path in unknown dynamic environments. Genetic algorithm is a simple but splendid evolutionary search method that provides very good solutions for task allocation. In order to achieve higher efficiency of the intelligent warehouse system, we design a new solution by combining these two techniques and provide an effective and alternative way compared with other state-of-the-art methods. Simulation results demonstrate the effectiveness of the proposed approach regarding the optimization of travel time and overall efficiency of the intelligent warehouse system.

Collaborative Scheduling of Algorithms for Path Planning of Unmanned Systems

2021 ◽  
Vol 01 ◽  
Author(s):  
Ying Li ◽  
Chubing Guo ◽  
Jianshe Wu ◽  
Xin Zhang ◽  
Jian Gao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Systems ◽  
Ant Colony Optimization Algorithm ◽  
A Algorithm ◽  
Collaborative Scheduling ◽  
Simulation Results ◽  
Planning Problems ◽  
Effective Path

Background: Unmanned systems have been widely used in multiple fields. Many algorithms have been proposed to solve path planning problems. Each algorithm has its advantages and defects and cannot adapt to all kinds of requirements. An appropriate path planning method is needed for various applications. Objective: To select an appropriate algorithm fastly in a given application. This could be helpful for improving the efficiency of path planning for Unmanned systems. Methods: This paper proposes to represent and quantify the features of algorithms based on the physical indicators of results. At the same time, an algorithmic collaborative scheme is developed to search the appropriate algorithm according to the requirement of the application. As an illustration of the scheme, four algorithms, including the A-star (A*) algorithm, reinforcement learning, genetic algorithm, and ant colony optimization algorithm, are implemented in the representation of their features. Results: In different simulations, the algorithmic collaborative scheme can select an appropriate algorithm in a given application based on the representation of algorithms. And the algorithm could plan a feasible and effective path. Conclusion: An algorithmic collaborative scheme is proposed, which is based on the representation of algorithms and requirement of the application. The simulation results prove the feasibility of the scheme and the representation of algorithms.

scholarly journals Validation of a Dynamic Planning Navigation Strategy Applied to Mobile Terrestrial Robots

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4322 ◽  
Author(s):  
Caroline Silva ◽  
Átila de Oliveira ◽  
Marcelo Fernandes
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Autonomous Navigation ◽  
Genetic Parameters ◽  
Dynamic Environments ◽  
Dynamic Planning ◽  
Navigation Algorithm ◽  
Navigation Strategy ◽  
Terrestrial Environments ◽  
Simulation Results

This work describes the performance of a DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm) algorithm applied to autonomous navigation in unknown static and dynamic terrestrial environments. The main aim was to validate the functionality and robustness of the DPNA-GA, with variations of genetic parameters including the crossover rate and population size. To this end, simulations were performed of static and dynamic environments, applying the different conditions. The simulation results showed satisfactory efficiency and robustness of the DPNA-GA technique, validating it for real applications involving mobile terrestrial robots.

scholarly journals Integrating Iterative Crossover Capability in Orthogonal Neighborhoods for Scheduling Resource-Constrained Projects

2013 ◽  
Vol 21 (2) ◽  
pp. 341-360 ◽  
Author(s):  
Reza Zamani
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Project Scheduling ◽  
Search Space ◽  
Search Method ◽  
Evolutionary Search ◽  
Resource Constrained ◽  
Search Mechanism ◽  
Benchmark Instances ◽  
Project Scheduling Problem

An effective hybrid evolutionary search method is presented which integrates a genetic algorithm with a local search. Whereas its genetic algorithm improves the solutions obtained by its local search, its local search component utilizes a synergy between two neighborhood schemes in diversifying the pool used by the genetic algorithm. Through the integration of these two searches, the crossover operators further enhance the solutions that are initially local optimal for both neighborhood schemes; and the employed local search provides fresh solutions for the pool whenever needed. The joint endeavor of its local search mechanism and its genetic algorithm component has made the method both robust and effective. The local search component examines unvisited regions of search space and consequently diversifies the search; and the genetic algorithm component recombines essential pieces of information existing in several high-quality solutions and intensifies the search. It is through striking such a balance between diversification and intensification that the method exploits the structure of search space and produces superb solutions. The method has been implemented as a procedure for the resource-constrained project scheduling problem. The computational experiments on 2,040 benchmark instances indicate that the procedure is very effective.

scholarly journals OPTIMIZATION OF ARCHITECTURAL LAYOUT BY THE IMPROVED GENETIC ALGORITHM

2005 ◽  
Vol 11 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Romualdas Baušys ◽  
Ina Pankrašovaite
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Rate Of Convergence ◽  
Conceptual Design ◽  
Search Method ◽  
Evolutionary Search ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Current Stage

In this paper we consider architectural layout problem that seeks to determine the layout of Units based on lighting, heating, available sizes and other objectives and constraints. For a conceptual design of architectural layout we present an approach based on evolutionary search method known as the genetic algorithms (GAs). However, the rate of convergence of GAs is often not good enough at their current stage. For this reason, the improved genetic algorithm is proposed. We have analysed and compared the performance of standard and improved genetic algorithm for architectural layout problem solutions and presented the results of performance.

scholarly journals Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

2021 ◽  
Vol 8 ◽  
Author(s):  
Chen-Lung Lu ◽  
Zi-Yan Liu ◽  
Jui-Te Huang ◽  
Ching-I Huang ◽  
Bo-Hui Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Visually Impaired ◽  
State Of The Art ◽  
Dynamic Environments ◽  
Learning Approach ◽  
Independent Mobility ◽  
Blind And Visually Impaired ◽  
Visually Impaired People ◽  
Localization And Mapping ◽  
Navigation Method

Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)–based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed.

scholarly journals Epistasis-Based Basis Estimation Method for Simplifying the Problem Space of an Evolutionary Search in Binary Representation

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Junghwan Lee ◽  
Yong-Hyuk Kim
Keyword(s):  
Genetic Algorithm ◽  
Test Problem ◽  
Estimation Method ◽  
Search Space ◽  
Search Method ◽  
Binary Representation ◽  
Evolutionary Search ◽  
Problem Space ◽  
Fitness Evaluation ◽  
Suitable Change

An evolutionary search space can be smoothly transformed via a suitable change of basis; however, it can be difficult to determine an appropriate basis. In this paper, a method is proposed to select an optimum basis can be used to simplify an evolutionary search space in a binary encoding scheme. The basis search method is based on a genetic algorithm and the fitness evaluation is based on the epistasis, which is an indicator of the complexity of a genetic algorithm. Two tests were conducted to validate the proposed method when applied to two different evolutionary search problems. The first searched for an appropriate basis to apply, while the second searched for a solution to the test problem. The results obtained after the identified basis had been applied were compared to those with the original basis, and it was found that the proposed method provided superior results.

scholarly journals Learning Reward Function with Matching Network for Mapless Navigation

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3664 ◽  
Author(s):  
Qichen Zhang ◽  
Meiqiang Zhu ◽  
Liang Zou ◽  
Ming Li ◽  
Yong Zhang
Keyword(s):  
Reinforcement Learning ◽  
Optimal Strategy ◽  
State Of The Art ◽  
The State ◽  
Matching Network ◽  
Additional Training ◽  
Reward Function ◽  
Moving Obstacles ◽  
Reward Shaping ◽  
Simulation Results

Deep reinforcement learning (DRL) has been successfully applied in mapless navigation. An important issue in DRL is to design a reward function for evaluating actions of agents. However, designing a robust and suitable reward function greatly depends on the designer’s experience and intuition. To address this concern, we consider employing reward shaping from trajectories on similar navigation tasks without human supervision, and propose a general reward function based on matching network (MN). The MN-based reward function is able to gain the experience by pre-training through trajectories on different navigation tasks and accelerate the training speed of DRL in new tasks. The proposed reward function keeps the optimal strategy of DRL unchanged. The simulation results on two static maps show that the DRL converge with less iterations via the learned reward function than the state-of-the-art mapless navigation methods. The proposed method performs well in dynamic maps with partially moving obstacles. Even when test maps are different from training maps, the proposed strategy is able to complete the navigation tasks without additional training.

scholarly journals Genetic Network Programming with Rules

2009 ◽  
Vol 13 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Fengming Ye ◽  
◽  
Lu Yu ◽  
Shingo Mabu ◽  
Kaoru Shimada ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Directed Graphs ◽  
Genetic Network ◽  
Dynamic Environments ◽  
Network Programming ◽  
Optimal Solutions ◽  
Simulation Environment ◽  
Simulation Results ◽  
Genetic Network Programming ◽  
Conventional Algorithm

Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator systems, etc and GNP has obtained some outstanding results. In order to improve GNP's performance further, this paper proposes a new method called GNP with Rules. The aim of the proposed method is to balance exploitation and exploration of GNP, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposed method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. These 4 steps are added to the conventional algorithm of GNP. In order to measure the performance of the proposed method, the tileworld was used as the simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

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