scholarly journals Optimization Complete Area Coverage by Reconfigurable hTrihex Tiling Robot

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3170 ◽  
Author(s):  
Anh Vu Le ◽  
Rizuwana Parween ◽  
Rajesh Elara Mohan ◽  
Nguyen Huu Khanh Nhan ◽  
Raihan Enjikalayil Abdulkader
Keyword(s):  
Specific Area ◽  
Energy Shift ◽  
Area Coverage ◽  
Planning Problem ◽  
Simulation Environment ◽  
Complete Coverage ◽  
Narrow Space ◽  
Optimal Movement ◽  
The Traveling Salesman Problem ◽  
Navigation Planning

Completed area coverage planning (CACP) plays an essential role in various fields of robotics, such as area exploration, search, rescue, security, cleaning, and maintenance. Tiling robots with the ability to change their shape is a feasible solution to enhance the ability to cover predefined map areas with flexible sizes and to access the narrow space constraints. By dividing the map into sub-areas with the same size as the changeable robot shapes, the robot can plan the optimal movement to predetermined locations, transform its morphologies to cover the specific area, and ensure that the map is completely covered. The optimal navigation planning problem, including the least changing shape, shortest travel distance, and the lowest travel time while ensuring complete coverage of the map area, are solved in this paper. To this end, we propose the CACP framework for a tiling robot called hTrihex with three honeycomb shape modules. The robot can shift its shape into three different morphologies ensuring coverage of the map with a predetermined size. However, the ability to change shape also raises the complexity issues of the moving mechanisms. Therefore, the process of optimizing trajectories of the complete coverage is modeled according to the Traveling Salesman Problem (TSP) problem and solved by evolutionary approaches Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Hence, the costweight to clear a pair of waypoints in the TSP is defined as the required energy shift the robot between the two locations. This energy corresponds to the three operating processes of the hTrihex robot: transformation, translation, and orientation correction. The CACP framework is verified both in the simulation environment and in the real environment. From the experimental results, proposed CACP capable of generating the Pareto-optimal outcome that navigates the robot from the goal to destination in various workspaces, and the algorithm could be adopted to other tiling robot platforms with multiple configurations.

scholarly journals On the Traveling Salesman Problem in Nautical Environments: an Evolutionary Computing Approach to Optimization of Tourist Route Paths in Medulin, Croatia

2019 ◽  
Vol 57 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Sandi Baressi Šegota ◽  
Ivan Lorencin ◽  
Kazuhiro Ohkura ◽  
Zlatan Car
Keyword(s):  
Traveling Salesman Problem ◽  
Optimal Path ◽  
Solution Space ◽  
Evolutionary Computing ◽  
Problem Formulation ◽  
Computing Methods ◽  
Traveling Salesman ◽  
Planning Problem ◽  
Extensive Search ◽  
The Traveling Salesman Problem

The Traveling salesman problem (TSP) defines the problem of finding the optimal path between multiple points, connected by paths of a certain cost. This paper applies that problem formulation in the maritime environment, specifically a path planning problem for a tour boat visiting popular tourist locations in Medulin, Croatia. The problem is solved using two evolutionary computing methods – the genetic algorithm (GA) and the simulated annealing (SA) - and comparing the results (are compared) by an extensive search of the solution space. The results show that evolutionary computing algorithms provide comparable results to an extensive search in a shorter amount of time, with SA providing better results of the two.

scholarly journals A MOVING OBSTACLES PLANNER ALGORITHM FOR A SIX-WHEELED SURVEYING ROBOT

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 191-195
Author(s):  
S. Bazhan ◽  
A. Hosseininaveh
Keyword(s):  
State Of The Art ◽  
Planning Problem ◽  
Simulation Environment ◽  
Ground Vehicle ◽  
Robot Operating System ◽  
Moving Obstacles ◽  
Wheeled Robot ◽  
Military Applications ◽  
Window Approach ◽  
Path Planning Problem

Abstract. Nowadays, robotic systems such as ground vehicle robots are mostly used in many industrial and military applications. Therefore, the path planning problem in the robotics domain is very important. Moving Obstacles Planner (MOP) algorithms have got the researchers interests in recent years and some of the most recent ones have been implemented in Robot Operating System (ROS) which is an open source middle wear to work with robots. This paper aims to compare the state-of-the-art MOP algorithms including Rapidly exploring Random Tree (RRT) and those implemented in the ROS navigation stack such as Dynamic Window Approach (DWA) local planner coupled with Dijkstra and A* as global planners on a six-wheeled robot known as MOOR in simulation environment. The results reveal that all of these algorithms have been designed for a square shape footprint robot and thus have limitations for MOOR with a rectangular footprint shape.

scholarly journals Using Genetic Algorithms for Navigation Planning in Dynamic Environments

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ferhat Uçan ◽  
D. Turgay Altılar
Keyword(s):  
Fuel Consumption ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Flight Path ◽  
Main Concern ◽  
Planning Problem ◽  
Process Unit ◽  
Flight Planning ◽  
Air Vehicle ◽  
Navigation Planning

Navigation planning can be considered as a combination of searching and executing the most convenient flight path from an initial waypoint to a destination waypoint. Generally the aim is to follow the flight path, which provides minimum fuel consumption for the air vehicle. For dynamic environments, constraints change dynamically during flight. This is a special case of dynamic path planning. As the main concern of this paper is flight planning, the conditions and objectives that are most probable to be used in navigation problem are considered. In this paper, the genetic algorithm solution of the dynamic flight planning problem is explained. The evolutionary dynamic navigation planning algorithm is developed for compensating the existing deficiencies of the other approaches. The existing fully dynamic algorithms process unit changes to topology one modification at a time, but when there are several such operations occurring in the environment simultaneously, the algorithms are quite inefficient. The proposed algorithm may respond to the concurrent constraint updates in a shorter time for dynamic environment. The most secure navigation of the air vehicle is planned and executed so that the fuel consumption is minimum.

scholarly journals A Two-Stage Method for Target Searching in the Path Planning for Mobile Robots

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6919
Author(s):  
Tao Song ◽  
Xiang Huo ◽  
Xinkai Wu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Search Time ◽  
Optimal Solution ◽  
Corner Detection ◽  
Planning Problem ◽  
Complete Coverage ◽  
Two Stage ◽  
Set Up ◽  
Target Searching

The path planning for target searching in mobile robots is critical for many applications, such as warehouse inspection and caring and surveillance for elderly people in the family scene. To ensure visual complete coverage from the camera equipped in robots is one of the most challenging tasks. To tackle this issue, we propose a two-stage optimization model to efficiently obtain an approximate optimal solution. In this model, we first develop a method to determine the key locations for visual complete coverage of a two-dimensional grid map, which is constructed by drawing lessons from the method of corner detection in the image processing. Then, we design a planning problem for searching the shortest path that passes all key locations considering the frequency of target occurrence. The testing results show that the proposed algorithm can achieve the significantly shorter search path length and the shorter target search time than the current Rule-based Algorithm and Genetic Algorithm (GA) in various simulation cases. Furthermore, the results show that the improved optimization algorithm with the priori known frequency of occurrence of the target can further improve the searching with shorter searching time. We also set up a test in a real environment to verify the feasibility of our algorithm.

scholarly journals Multi-Agent Planning for Automatic Geospatial Web Service Composition in Geoportals

2018 ◽  
Vol 7 (10) ◽  
pp. 404 ◽  
Author(s):  
Mahdi Farnaghi ◽  
Ali Mansourian
Keyword(s):  
Web Services ◽  
Web Service ◽  
Spatial Data ◽  
Execution Time ◽  
Search Space ◽  
Planning Problem ◽  
Ai Planning ◽  
Simulation Environment ◽  
Multi Agent ◽  
Open Geospatial Consortium

Automatic composition of geospatial web services increases the possibility of taking full advantage of spatial data and processing capabilities that have been published over the internet. In this paper, a multi-agent artificial intelligence (AI) planning solution was proposed, which works within the geoportal architecture and enables the geoportal to compose semantically annotated Open Geospatial Consortium (OGC) Web Services based on users’ requirements. In this solution, the registered Catalogue Service for Web (CSW) services in the geoportal along with a composition coordinator component interact together to synthesize Open Geospatial Consortium Web Services (OWSs) and generate the composition workflow. A prototype geoportal was developed, a case study of evacuation sheltering was implemented to illustrate the functionality of the algorithm, and a simulation environment, including one hundred simulated OWSs and five CSW services, was used to test the performance of the solution in a more complex circumstance. The prototype geoportal was able to generate the composite web service, based on the requested goals of the user. Additionally, in the simulation environment, while the execution time of the composition with two CSW service nodes was 20 s, the addition of new CSW nodes reduced the composition time exponentially, so that with five CSW nodes the execution time reduced to 0.3 s. Results showed that due to the utilization of the computational power of CSW services, the solution was fast, horizontally scalable, and less vulnerable to the exponential growth in the search space of the AI planning problem.

scholarly journals Multi-constrained cooperative path planning of multiple drones for persistent surveillance in urban environments

2021 ◽  
Author(s):  
Yu Wu ◽  
Shaobo Wu ◽  
Xinting Hu
Keyword(s):  
Path Planning ◽  
Urban Environments ◽  
Time Interval ◽  
Planning Problem ◽  
Complete Coverage ◽  
Coverage Area ◽  
Covered Area ◽  
Long Time ◽  
Planning Algorithm ◽  
Path Planning Algorithm

AbstractDifferent from the usual surveillance task in which the goal is to achieve complete coverage of the specified area, the cooperative path planning problem of drones for persistent surveillance task is studied in this paper considering multiple constraints of the covered area. The goal is to maximize the combinational coverage area of drones while giving preference to the area that hasn’t been visited beyond a certain time interval. The influence of shooting resolution and blocking of buildings are considered, and the state information of each grid is defined to record the visit information of the ground area. Considering the characteristic of the established model, the multi-constrained cooperative path planning (MCCPP) algorithm is developed. The grids which have not been visited for a long time are received special attentions, and the drone is led to reducing the flight height to cover the gird which has a special requirement on the shooting resolution. The cooperation mechanism among drones is also set to ensure that all the drones can determine the next path point synchronously. An emergency path planning algorithm with the continuous checking strategy is designed for a drone to fly to the specified area and finish a complete coverage of it.

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

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 445 ◽  
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.

scholarly journals An Improved Approximation Algorithm for the Subpath Planning Problem and Its Generalization

2017 ◽  
Author(s):  
Hanna Sumita ◽  
Yuma Yonebayashi ◽  
Naonori Kakimura ◽  
Ken-ichi Kawarabayashi
Keyword(s):  
Artificial Intelligence ◽  
Approximation Algorithm ◽  
Traveling Salesman Problem ◽  
Numerical Experiments ◽  
Traveling Salesman ◽  
Planning Problem ◽  
Solution Quality ◽  
The Traveling Salesman Problem ◽  
Direct Use ◽  
And Robotics

This paper focuses on a generalization of the traveling salesman problem (TSP), called the subpath planning problem (SPP). Given 2n vertices and n independent edges on a metric space, we aim to find a shortest tour that contains all the edges. SPP is one of the fundamental problems in both artificial intelligence and robotics. Our main result is to design a 1.5-approximation algorithm that runs in polynomial time, improving the currently best approximation algorithm. The idea is direct use of techniques developed for TSP. In addition, we propose a generalization of SPP called the subgroup planning problem (SGPP). In this problem, we are given a set of disjoint groups of vertices, and we aim to find a shortest tour such that all the vertices in each group are traversed sequentially. We propose a 3-approximation algorithm for SGPP. We also conduct numerical experiments. Compared with previous algorithms, our algorithms improve the solution quality by more than 10% for large instances with more than 10,000 vertices.

Advanced TEM sample preparation techniques for submicron Si devices

1995 ◽  
Vol 53 ◽  
pp. 516-517 ◽  
Author(s):  
P. B. Basham ◽  
H. L. Tsai
Keyword(s):  
Sample Preparation ◽  
Process Development ◽  
Light Transmission ◽  
Specific Area ◽  
Turnaround Time ◽  
Small Hole ◽  
Area Of Interest ◽  
Transmission Electron ◽  
Polished Side ◽  
Preparation Techniques

The use of transmission electron microscopy (TEM) to support process development of advanced microelectronic devices is often challenged by a large amount of samples submitted from wafer fabrication areas and specific-spot analysis. Improving the TEM sample preparation techniques for a fast turnaround time is critical in order to provide a timely support for customers and improve the utilization of TEM. For the specific-area sample preparation, a technique which can be easily prepared with the least amount of effort is preferred. For these reasons, we have developed several techniques which have greatly facilitated the TEM sample preparation.For specific-area analysis, the use of a copper grid with a small hole is found to be very useful. With this small-hole grid technique, TEM sample preparation can be proceeded by well-established conventional methods. The sample is first polished to the area of interest, which is then carefully positioned inside the hole. This polished side is placed against the grid by epoxy Fig. 1 is an optical image of a TEM cross-section after dimpling to light transmission.

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