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Cleaning is a fundamental routine task in human life that is now handed over to leading-edge technologies such as robotics and artificial intelligence. Various floor-cleaning robots have been developed with different cleaning functionalities, such as vacuuming and scrubbing. However, failures can occur when a robot tries to clean an incompatible dirt type. These situations will not only reduce the efficiency of the robot but also impose severe damage to the robots. Therefore, developing effective methods to classify the cleaning tasks performed in different regions and assign them to the respective cleaning agent has become a trending research domain. This article proposes a vision-based system that employs YOLOv5 and DeepSORT algorithms to detect and classify dirt to create a dirt distribution map that indicates the regions to be assigned for different cleaning requirements. This map would be useful for a collaborative cleaning framework for deploying each cleaning robot to its respective region to achieve an uninterrupted and energy-efficient operation. The proposed method can be executed with any mobile robot and on any surface and dirt, achieving high accuracy of 81.0%, for dirt indication in the dirt distribution map. [ABSTRACT FROM AUTHOR]

Service robots with social interactive features are developed to cater to the demand in various application domains. These robots often need to approach toward users to accomplish typical day-to-day services. Thereby, the approaching behavior of a service robot is a crucial factor in developing social interactivity between users and the robot. In this regard, a robot should be capable of maintaining proper proxemics at the termination position of an approach that improves the comfort of users. Proxemics preferences of humans depend on physical user behavior as well as personal factors. Therefore, this paper proposes a novel method to adapt the termination position of an approach based on physical user behavior and user feedback. Physical behavior of a user is perceived by the robot through analyzing skeletal joint movements of the user. These parameters are taken as inputs for a fuzzy neural network that determines the appropriate interpersonal distance. The preference of a user is learnt by modifying the internal parameters of the fuzzy neural network based on user feedback. A user study has been conducted to compare and contrast behavior of the proposed system over the existing approaches. The outcomes of the user study confirm a significant improvement in user satisfaction due to the adaptation toward users based on feedback. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method based on Fuzzy Logic Systems (FLSs) optimized to maximize the area coverage of a tiling robot by reconfiguring per an obstacle considering the "Infi" concept. The consideration of an infinite number of reconfigurable shapes by a tiling robot is defined as the "Infi" concept. The major advantage of the proposed method over state-of-the-art "Infi" methods is the ability to tune based on cost measurements without demanding an explicit set of training data. The optimization techniques, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), were used for tuning the FLSs. The FLSs tuned using these three techniques were compared with state of the art by considering hTetro through simulations. According to the statistical outcomes, the proposed method can significantly improve the area coverage compared to the conventional methods while providing performance and behavior similar to the existing "Infi" methods that demand training data. Thus, the work proposed in this paper would be of great interest for the development of a reconfigurable floor cleaning robot since the complete area coverage is a foremost feature expected from a floor cleaning robot. [ABSTRACT FROM AUTHOR]

Mobile robots are deployed in the built environment at increasing rates. However, lack of considerations for a robot-inclusive planning has led to physical spaces that would potentially pose hazards to robots, and contribute to an overall productivity decline for mobile service robots. This research proposes the use of an adapted Failure Mode and Effects Analysis (FMEA) as a structured tool to evaluate a building's level of robot-inclusivity and safety for service robot deployments. This Robot-Inclusive FMEA (RIFMEA) framework, is used to identify failures in the built environment that compromise the workflow of service robots, assess their effects and causes, and provide recommended actions to alleviate these problems. The method was supported with a case study of deploying telepresence robots in a university campus. The study concluded that common failures were related to poor furniture design, a lack of clearance and hazard indicators, and sub-optimal interior planning. [ABSTRACT FROM AUTHOR]