Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robots to detect and avoid hazardous objects with a higher confidence level, even if the object is under occlusion. First, the image-level-contextual-encoding module was proposed and incorporated with the Faster RCNN ResNet 50 object detector model to improve the low-featured and occluded hazardous object detection in an indoor environment. Further, a safe-distance-estimation function was proposed to avoid hazardous objects. It computes the distance of the hazardous object from the robot’s position and steers the robot into a safer zone using detection results and object depth data. The proposed framework was trained with a custom image dataset using fine-tuning techniques and tested in real-time with an in-house-developed mobile cleaning robot, BELUGA. The experimental results show that the proposed algorithm detected the low-featured and occluded hazardous object with a higher confidence level than the conventional object detector and scored an average detection accuracy of 88.71%.

The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path planning capabilities in urban landscapes. The robot’s locomotion is based on a differential drive, facilitating easier maneuverability on semi-automated planar surfaces in landscaping infrastructure. The robot’s fumigator payload consists of a spray gun and a chemical tank, which can pan and fumigate up to 4.5 m from the ground. The system incorporates a wireless charging mechanism to allow for the autonomous charging of the mosquito catchers. A genetic algorithm fused with an A*-based prioritized path planning algorithm is developed for efficient navigation and charging of mosquito catchers. The algorithm, designed for maximizing charging efficiency, considers the initial charge percentage of mosquito catchers and the time required for fumigation to determine the optimal path for charging and fumigation. The experiment results show that the path planning algorithm can generate an optimized path for charging and fumigating multiple mosquito catchers based on their initial charge percentage. This paper concludes by summarizing the key findings and highlighting the significance of the fumigation robot in landscaping applications.

Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics.

Floor-cleaning robots are becoming popular and operating in public places to ensure the places are clean and tidy. These robots are often operated in a dynamic environment that is less safe and has a high probability of ending up in accidents. Sound event-based context detection is expected to overcome drawbacks in a robot’s visual sensing to avoid a hazardous environment, especially in improper illumination and occlusion situations. Even though numerous studies in the literature discuss the benefits of sound-based context detection, there is no work reported related to context avoidance for cleaning robots. To this end, we propose a novel context avoidance framework based on a deep-learning method that can detect and classify a specific sound and localize the source from a robot’s frame to avoid that environment. The proposed model receives the spectrogram from the array of microphones as the input and produces two parallel outputs. The first output provides information about the spectrum class after running the classification task. The second output contains the localization message of the identified sound source. With the identity of the location that needs to be avoided, the proposed module will generate an alternative trajectory. The proposed model is evaluated in two real-world scenarios, wherein the model is trained to detect the escalator sound in the robot’s surroundings and avoid its location. In all considered scenarios, the developed system accomplished a significantly higher success rate in detecting and avoiding the escalator.