Complete Path Planning for a Tetris-Inspired Self-Reconfigurable Robot by the Genetic Algorithm of the Traveling Salesman Problem

The efficiency of autonomous systems that tackle tasks such as home cleaning, agriculture harvesting, and mineral mining depends heavily on the adopted area coverage strategy. Extensive navigation strategies have been studied and developed, but few focus on scenarios with reconfigurable robot agents. This paper proposes a navigation strategy that accomplishes complete path planning for a Tetris-inspired hinge-based self-reconfigurable robot (hTetro), which consists of two main phases. In the first phase, polyomino form-based tilesets are generated to cover the predefined area based on the tiling theory, which generates a series of unsequenced waypoints that guarantee complete coverage of the entire workspace. Each waypoint specifies the position of the robot and the robot morphology on the map. In the second phase, an energy consumption evaluation model is constructed in order to determine a valid strategy to generate the sequence of the waypoints. The cost value between waypoints is formulated under the consideration of the hTetro robot platform’s kinematic design, where we calculate the minimum sum of displacement of the four blocks in the hTetro robot. With the cost function determined, the waypoint sequencing problem is then formulated as a travelling salesman problem (TSP). In this paper, a genetic algorithm (GA) is proposed as a strong candidate to solve the TSP. The GA produces a viable navigation sequence for the hTetro robot to follow and to accomplish complete coverage tasks. We performed an analysis across several complete coverage algorithms including zigzag, spiral, and greedy search to demonstrate that TSP with GA is a valid and considerably consistent waypoint sequencing strategy that can be implemented in real-world hTetro robot navigations. The scalability of the proposed framework allows the algorithm to produce reliable results while navigating within larger workspaces in the real world, and the flexibility of the framework ensures easy implementation of the algorithm on other polynomial-based shape shifting robots.

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Robot path planning based on genetic algorithm with hybrid initialization method

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211423 ◽

2021 ◽

pp. 1-16

Author(s):

Zhaojun Zhang ◽

Rui Lu ◽

Minglong Zhao ◽

Shengyang Luan ◽

Ming Bu

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Iteration Process ◽

Initial Population ◽

Local Optimum ◽

Complex Environments ◽

Random Initialization ◽

Planning Problems ◽

Complete Path

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

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Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm

Robotica ◽

10.1017/s0263574714001878 ◽

2014 ◽

Vol 34 (4) ◽

pp. 823-836 ◽

Cited By ~ 22

Author(s):

Hamed Shorakaei ◽

Mojtaba Vahdani ◽

Babak Imani ◽

Ali. Gholami

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Cost Function ◽

Simple Formula ◽

Parallel Genetic Algorithm ◽

Probability Map ◽

Genetic Encoding ◽

Cell Position ◽

The Cost ◽

Unique Vector

SUMMARYThe current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts; one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chromosome for crossover operation. The effectiveness of the method is shown by several simulations.

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Optimal Multirobot Coverage Path Planning: Ideal-Shaped Spanning Tree

Mathematical Problems in Engineering ◽

10.1155/2018/3436429 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Chunqing Gao ◽

Yingxin Kou ◽

Zhanwu Li ◽

An Xu ◽

You Li ◽

...

Keyword(s):

Path Planning ◽

Spanning Tree ◽

Spanning Trees ◽

Optimal Path ◽

Polynomial Algorithms ◽

Minimum Length ◽

Second Phase ◽

Complete Coverage ◽

Preparation Time ◽

Coverage Path Planning

The present paper attempts to find the optimal coverage path for multiple robots in a given area including obstacles. For single robot coverage path planning (CPP) problem, an improved ant colony optimization (ACO) algorithm is proposed to construct the best spanning tree and then obtain the optimal path, which contributes to minimizing the energy/time consumption. For the multirobot case, first the DARP (Divide Areas based on Robots Initial Positions) algorithm is utilized to divide the area into separate equal subareas, so much so that it transforms the mCPP problem into several CPP problems, degrading the computation complexity. During the second phase, spanning tree in each subarea is constructed by the aforementioned algorithm. In the last phase, the specific end nodes are exchanged among subareas to achieve ideal-shaped spanning trees, which can also decrease the number of turns in coverage path. And the complete algorithms are proven to be approximately polynomial algorithms. Finally, the simulation confirms the complete algorithms’ advantages: complete coverage, nonbacktracks, minimum length, zero preparation time, and the least number of turns.

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An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle

Sensors ◽

10.3390/s19112640 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2640 ◽

Cited By ~ 14

Author(s):

Junfeng Xin ◽

Jiabao Zhong ◽

Fengru Yang ◽

Ying Cui ◽

Jinlu Sheng

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Autonomous Navigation ◽

Travelling Salesman Problem ◽

Optimal Path ◽

Convergence Speed ◽

Planning Problem ◽

Unmanned Surface Vehicle ◽

Domain Inversion ◽

And Control

The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others.

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OPTIMASI MULTI TRAVELLING SALESMAN PROBLEM (M-TSP) UNTUK DISTRIBUSI PRODUK PADA HOME INDUSTRI TEKSTIL DENGAN ALGORITMA GENETIKA

KLIK - KUMPULAN JURNAL ILMU KOMPUTER ◽

10.20527/klik.v4i2.86 ◽

2017 ◽

Vol 4 (2) ◽

pp. 125

Author(s):

Agung Mustika Rizki ◽

Wayan Firdaus Mahmudy ◽

Gusti Eka Yuliastuti

Keyword(s):

Genetic Algorithm ◽

Textile Industry ◽

Travelling Salesman Problem ◽

Test Results ◽

Travelling Salesman ◽

Fitness Value ◽

Optimal Value ◽

Distribution Process ◽

Optimal Population ◽

The Cost

In the field of textile industry, the distribution process is an important factor that can affect the cost of production. For that we need optimization on the distribution process to be more efficient. This problem is a model in the Multi Travelling Salesman Problem (M-TSP). Much research has been done to complete the M-TSP model. Among several methods that have been applied by other researchers, genetic algorithms are a workable method for solving this model problem. In this article the authors chose the genetic algorithm is expected to produce an optimal value with an efficient time. Based on the results of testing and analysis, obtained the optimal population amount of 120. For the optimal generation amount is 800. The test results related to the number of population and the number of generations are used as input to test the combination of CR and MR, obtained the optimal combination of CR = 0 , 4 and MR = 0.6 with a fitness value of 2.9964.Keywords: Textile Industry, Multi Travelling Salesman Problem (M-TSP), Genetic AlgorithmPada bidang industri tekstil, proses distribusi merupakan satu faktor penting yang dapat berpengaruh terhadap biaya produksi. Untuk itu diperlukan optimasi pada proses distribusi agar menjadi lebih efisien. Masalah seperti ini merupakam model dalam Multi Travelling Salesman Problem (M-TSP). Banyak penelitian telah dilakukan untuk menyelesaikan model M-TSP. Diantara beberapa metode yang telah diterapkan oleh peneiti lain, algoritma genetika adalah metode yang bisa diterapkan untuk penyelesaian permasalahan model ini. Dalam artikel ini penulis memilih algoritma genetika diharapkan dapat menghasilkan nilai yang optimal dengan waktu yang efisien. Berdasarkan hasil pengujian dan analisis, didapatkan jumlah populasi yang optimal sebesar 120. Untuk jumlah generasi yang optimal adalah sebesar 800. Hasil pengujian terkait jumlah populasi dan jumlah generasi tersebut dijadikan masukan untuk melakukan pengujian kombinasi CR dan MR, didapatkan kombinasi yang optimal yakni CR=0,4 dan MR=0,6 dengan nilai fitness sebesar 2,9964.Kata kunci: Industri Tekstil, Distribusi, Multi Travelling Salesman Problem (M-TSP), Algoritma Genetika

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Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots

Sensors ◽

10.3390/s20020445 ◽

2020 ◽

Vol 20 (2) ◽

pp. 445 ◽

Cited By ~ 12

Author(s):

Anh Vu Le ◽

Nguyen Huu Khanh Nhan ◽

Rajesh Elara Mohan

Keyword(s):

Path Planning ◽

Complete Coverage ◽

Coverage Path Planning ◽

Reconfigurable Robots ◽

Navigation Path ◽

Planning Technique ◽

Reconfigurable Robot ◽

Cleaning Robots ◽

The Traveling Salesman Problem ◽

Tiling Patterns

Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots.

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Research on Coverage Path Planning of Mobile Robot Based on Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.819.379 ◽

2013 ◽

Vol 819 ◽

pp. 379-383 ◽

Cited By ~ 1

Author(s):

San Peng Deng ◽

Zhong Min Wang ◽

Peng Zhou ◽

Hong Bing Wu

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Mobile Robot ◽

Cell Decomposition ◽

Global Motion ◽

Complete Coverage ◽

Regional Environment ◽

Coverage Path Planning ◽

Environment Model ◽

Local Space

This paper presents a complete coverage path planning method, which combines local space coverage with global motion planning. It is realized by modeling mobile robot environment based on Boustrophedon cell decomposition method; and according to the characteristics of regional environment model, the connectivity of the traversing space is represented by a complete weighted connected matrix. Then Genetic algorithm (GA) is used to optimize the subspace traversal distance to obtain the shortest global traversal sequence of mobile robot.

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Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

Energies ◽

10.3390/en12061136 ◽

2019 ◽

Vol 12 (6) ◽

pp. 1136 ◽

Cited By ~ 15

Author(s):

Anh Le ◽

Ping-Cheng Ku ◽

Thein Than Tun ◽

Nguyen Huu Khanh Nhan ◽

Yuyao Shi ◽

...

Keyword(s):

Path Planning ◽

Complete Coverage ◽

Energy Aware ◽

Coverage Path Planning ◽

Differential Drive ◽

Reconfigurable Robots ◽

Cleaning Robot ◽

Sequencing Strategy ◽

Planning Strategies ◽

Four Blocks

The efficiency of energy usage applied to robots that implement autonomous duties such as floor cleaning depends crucially on the adopted path planning strategies. Energy-aware for complete coverage path planning (CCPP) in the reconfigurable robots raises interesting research, since the ability to change the robot’s shape needs the dynamic estimate energy model. In this paper, a CCPP for a predefined workspace by a new floor cleaning platform (hTetro) which can self-reconfigure among seven tetromino shape by the cooperation of hinge-based four blocks with independent differential drive modules is proposed. To this end, the energy consumption is represented by travel distances which consider operations of differential drive modules of the hTetro kinematic designs to fulfill the transformation, orientation correction and translation actions during robot navigation processes from source waypoint to destination waypoint. The optimal trajectory connecting all pairs of waypoints on the workspace is modeled and solved by evolutionary algorithms of TSP such as Genetic Algorithm (GA) and Ant Optimization Colony (AC) which are among the well-known optimization approaches of TSP. The evaluations across several conventional complete coverage algorithms to prove that TSP-based proposed method is a practical energy-aware navigation sequencing strategy that can be implemented to our hTetro robot in different real-time workspaces. Moreover, The CCPP framework with its modulation in this paper allows the convenient implementation on other polynomial-based reconfigurable robots.

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Inclusion of Hydraulic Controls in Rehabilitation Models of Drainage Networks to Control Floods

Water ◽

10.3390/w13040514 ◽

2021 ◽

Vol 13 (4) ◽

pp. 514

Author(s):

Leonardo Bayas-Jiménez ◽

F. Javier Martínez-Solano ◽

Pedro L. Iglesias-Rey ◽

Daniel Mora-Melia ◽

Vicente S. Fuertes-Miquel

Keyword(s):

Genetic Algorithm ◽

Drainage System ◽

Optimal Solution ◽

Hydraulic Control ◽

Drainage Systems ◽

Storm Water Management Model ◽

Drainage Networks ◽

Extreme Rain ◽

Rain Events ◽

The Cost

A problem for drainage systems managers is the increase in extreme rain events that are increasing in various parts of the world. Their occurrence produces hydraulic overload in the drainage system and consequently floods. Adapting the existing infrastructure to be able to receive extreme rains without generating consequences for cities’ inhabitants has become a necessity. This research shows a new way to improve drainage systems with minimal investment costs, using for this purpose a novel methodology that considers the inclusion of hydraulic control elements in the network, the installation of storm tanks and the replacement of pipes. The presented methodology uses the Storm Water Management Model for the hydraulic analysis of the network and a modified Genetic Algorithm to optimize the network. In this algorithm, called the Pseudo-Genetic Algorithm, the coding of the chromosomes is integral and has been used in previous studies of hydraulic optimization. This work evaluates the cost of the required infrastructure and the damage caused by floods to find the optimal solution. The main conclusion of this study is that the inclusion of hydraulic controls can reduce the cost of network rehabilitation and decrease flood levels.

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