scholarly journals Motion planner for a Tetris-inspired reconfigurable floor cleaning robot

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091444 ◽  
Author(s):  
Prabakaran Veerajagadheswar ◽  
Ku Ping-Cheng ◽  
Mohan Rajesh Elara ◽  
Anh Vu Le ◽  
Masami Iwase
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Robotic Systems ◽  
Planning Techniques ◽  
Essential Ingredient ◽  
Coverage Path Planning ◽  
Cleaning Robot ◽  
Simulated Environment ◽  
Navigation Path ◽  
Planning Technique

Coverage path planning technique is an essential ingredient in every floor cleaning robotic systems. Even though numerous approaches demonstrate the benefits of conventional coverage motion planning techniques, they are mostly limited to fixed morphological platforms. In this article, we put forward a novel motion planning technique for a Tetris-inspired reconfigurable floor cleaning robot named “hTetro” that can reconfigure its morphology to any of the seven one-sided Tetris pieces. The proposed motion planning technique adapts polyomino tiling theory to tile a defined space, generates reference coordinates, and produces a navigation path to traverse on the generated tile-set with an objective of maximizing the area coverage. We have summarized all these aspects and concluded with experiments in a simulated environment that benchmarks the proposed technique with conventional approaches. The results show that the proposed motion planning technique achieves significantly higher performance in terms of area recovered than the traditional methods.

scholarly journals Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2585 ◽  
Author(s):  
Anh Le ◽  
Veerajagadheswar Prabakaran ◽  
Vinu Sivanantham ◽  
Rajesh Mohan
Keyword(s):  
Path Planning ◽  
Laser Sensor ◽  
Coverage Area ◽  
Operation System ◽  
Coverage Path Planning ◽  
Cleaning Robot ◽  
Simulated Environment ◽  
Planning Approach ◽  
Reconfigurable Robot ◽  
Set Up

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

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.

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys

Robotica ◽  
2018 ◽  
Vol 36 (8) ◽  
pp. 1144-1166 ◽  
Author(s):  
Héctor Azpúrua ◽  
Gustavo M. Freitas ◽  
Douglas G. Macharet ◽  
Mario F. M. Campos
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Robots ◽  
Mineral Exploration ◽  
Statistical Investigation ◽  
Coverage Path Planning ◽  
Simulated Environment ◽  
Outdoor Setting ◽  
Geophysical Surveys ◽  
Multi Robot

SUMMARYThe field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.

Coverage Path Planning for Cleaning Robot Adapted to Biased Dirt Distribution

2016 ◽  
Vol 2016 (0) ◽  
pp. 1A1-13b4 ◽  
Author(s):  
Takahiro SASAKI ◽  
Guillemo ENRIQUEZ ◽  
Takanobu MIWA ◽  
Shuji HASHIMOTO
Keyword(s):  
Path Planning ◽  
Coverage Path Planning ◽  
Cleaning Robot

scholarly journals A survey on inspecting structures using robotic systems

2016 ◽  
Vol 13 (6) ◽  
pp. 172988141666366 ◽  
Author(s):  
Randa Almadhoun ◽  
Tarek Taha ◽  
Lakmal Seneviratne ◽  
Jorge Dias ◽  
Guowei Cai
Keyword(s):  
Path Planning ◽  
Autonomous Systems ◽  
Robotic Systems ◽  
Accurate Information ◽  
Model Reconstruction ◽  
Critical Importance ◽  
Complete Coverage ◽  
Shape Model ◽  
Coverage Path Planning ◽  
Types Of Faults

Advancements in robotics and autonomous systems are being deployed nowadays in many application domains such as search and rescue, industrial automation, domestic services and healthcare. These systems are developed to tackle tasks in some of the most challenging, labour intensive and dangerous environments. Inspecting structures (e.g. bridges, buildings, ships, wind turbines and aircrafts) is considered a hard task for humans to perform and of critical importance since missing any details could affect the structure’s performance and integrity. Additionally, structure inspection is time and resource intensive and should be performed as efficiently and accurately as possible. Inspecting various structures has been reported in the literature using different robotic platforms to: inspect difficult to reach areas and detect various types of faults and anomalies. Typically, inspection missions involve performing three main tasks: coverage path planning, shape, model or surface reconstruction and the actual inspection of the structure. Coverage path planning ensures the generation of an optimized path that guarantees the complete coverage of the structure of interest in order to gather highly accurate information to be used for shape/model reconstruction. This article aims to provide an overview of the recent work and breakthroughs in the field of coverage path planning and model reconstruction, with focus on 3D reconstruction, for the purpose of robotic inspection.

Sign in / Sign up

Export Citation Format

Share Document