Abstract
Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.
Highlights
Mobile robots are categorized in both classical and heuristic methods in Coverage Path Planning (CPP)
Coverage Path Planning (CPP) algorithms have been implemented in many ‘real-world’ applications in dynamic environments; examples include domestic robots, such as cleaning and monitoring robots, automatic lawn mowers, inspection robots, painting robots, and industrial robots [1,2,3]
We have addressed two fundamental scientific questions related to mobile robotic systems: (i) how can a robot form a high-level probabilistic representation of an operating space, and (ii) how can a robot understand and reason about the operating environment
Summary
Mobile robots are categorized in both classical and heuristic methods in Coverage Path Planning (CPP). These methods can be divided subcategories such as analytical methods, evolutionary methods, enumerative methods, and meta-heuristic methods [1]. In real-world applications, these methods are highly dependent on the ability to optimize coverage path planning with minimized obstacles in a dynamic environment [1]. Coverage Path Planning (CPP) algorithms have been implemented in many ‘real-world’ applications in dynamic environments; examples include domestic robots, such as cleaning and monitoring robots, automatic lawn mowers, inspection robots, painting robots, and industrial robots [1,2,3]. There are generally two types of motion planning approaches for a cleaning robot. In the case of stationary obstacles, robot routing uses a predefined decision
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