CleanAll: Efficient Object Collection Route Planning From LiDAR Point Clouds Transmission in Cleaning Scenarios

초록

Due to technological advancements, today's world is rapidly changing from traditional and legacy approaches to smarter and more autonomous techniques. Intelligent systems and superconnectivity make possible the era of pervasive services, where these services can be used by anyone, anywhere, and at any time (AAA). These advancements have completely transformed modern living, increasing convenience and providing greater comfort. Robotic cleaning is one such revolutionized area where self-driving robots and automated systems are used to navigate and clean dirt. 3-D mapping adapts changes based on visual conditions to detect and remove waste obstacles from the location. These robots are useful for collecting waste and cleaning the floor. In this research work, we have proposed a light detection and range (LiDAR)-based cleaning robotic system called "CleanAll: Efficient Object Collection Route Planning from LiDAR Point Clouds in the Cleaning Scenario." It is an automotive system that calculates an optimized path from its 3-D point-cloud data to collect some objects through its object collector. CleanAll moves over the optimized path using point-cloud data along with a dual-antenna GNSS receiver and an IMU. A defined dataset is already used to compare and detect object shapes, and an extended Kalman filter is used to obtain the robot's position. CleanAll is more responsive, and its quick decision-making makes it a better option for cleaning scenarios where dirt and trash are scattered throughout areas. The optimized path used by the robot is not only useful in finding the shortest path, but it also saves time and consumes the minimum energy by following these short paths for trash collection. CleanAll is evaluated in three different scenarios, collecting all objects (scattered/line-by-line) with an average standard error rate of 0.4593. To assess performance on other parameters, it has been tested with two standard datasets, ACIN and IPA. The TPRs have 0.75, and the FPRs have been recorded as 0.40 in ACIN, while the TPRs in IPA have 0.7 and 0.4 in the same scenario. This means that CleanAll performs better using the ACIN dataset, achieving higher TPRs with lower FPRs. All these experiments illustrate that CleanAll performs better using the ACIN dataset, which aims to recognize and clean trash more precisely and efficiently.

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