Abstract
Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement. The aim of path planning is to optimise the efficacy of robotic movement in a defined operational environment. For example, robots have been employed in many domains including: Cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, and agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.
Highlights
Robots have been employed in a diverse range of domains and systems; for example robotic systems have addressed the demands of: Cleaning, automated paint spraying, window cleaning, forest monitoring, and agriculture, and in disaster areas and locations hazardous to humans [1,2]
We present an evaluation of our SmSTC algorithm with the results obtained in experimental testing using simulation and in a ‘real-world’ environment
We have carried out a comparative analysis which compares the performance of our proposed approach with the other current approaches; Figures 13–17 show the relative performance of our SmSTC
Summary
Robots have been employed in a diverse range of domains and systems; for example robotic systems have addressed the demands of: Cleaning (such as robotic vacuum cleaners), automated paint spraying, window cleaning, forest monitoring, and agriculture (where robotic control and monitoring systems are often implemented using geostationary positional satellite data), and in disaster areas and locations hazardous to humans (such as war zones and locations with restricted access) [1,2]. An typical application of CPP is an automatic robotic domestic vacuum cleaner which, when activated using CPP, can manoeuvre around a DOE (typically a room or on one level) to clean the area while avoiding static (moving) obstacles (such as furniture) or dynamic (moving) objects (such as a domestic animal) when traversing the DOE [7,8]. To solve this problem, it is necessary to build an algorithm to find optimal coverage path for the DOE; in studies addressing CPP
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
