A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Abstract

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

“…More precisely, Table 1 lists the vision-based approaches with their contributions, sensors, and real-time performance, and the most popular method is the optic flow, which is used by bees. The vision-based applications include transport [ 9 , 18 , 39 , 46 , 50 , 60 ], exploration [ 8 , 19 , 24 , 26 , 40 , 41 , 45 , 49 ], tracking and assist [ 31 , 56 , 58 ], security and surveillance [ 7 , 52 ], homing and searching [ 5 , 54 , 55 ], floor cleaning [ 6 , 43 ], and search and rescue [ 44 ]. Only 43.75% of the cited papers indicate they can operate in real time, which is achieved by processing or computation speed, minimal computation load, or predefined parameters.…”

“…Pathmakumar et al [ 43 ] described a dirty sample-gathering strategy for cleaning robots with swarm algorithms and a geometrical feature extraction. The approach covered identified dirt locations for cleaning and used geometric signatures to identify dirt-accumulated locations.…”

“…Moreover, they can cause failures in navigation or make incorrect decisions if visual disturbances exist. More powerful strategies, such as machine learning or neural networks, could be combined to overcome these issues, but they require a comprehensive dataset generation [ 43 ]. Parallel processing could also be used as a solution [ 39 ].…”

Advanced Sensors Technologies Applied in Mobile Robot

“…Using five Azure Kinect units equipped with depth sensors, the recorded skeletal data is analysed and classified using deep neural networks to output a diagnosis while the child carelessly plays the game. Article [ 7 ] presents an innovative strategy for collecting dirt samples for cleaning robots by combining geometric feature extraction and swarm algorithms. This combined approach generates an efficient optimal path that covers all identified dirt locations for an efficient cleaning mission.…”

Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning

“…Along with the unceasing progress of science and technology, all kinds of mobile robots have been imagined, and these robots are applied to many fields such as room cleaning [ 1 , 2 , 3 ], exploration [ 4 ], search-and-rescue [ 5 , 6 ], and guard patrol [ 7 ]. As one of the most important links with robot technology, path planning has become increasingly important to researchers.…”