UltraBot: Autonomous Mobile Robot for Indoor UV-C Disinfection

Perminov, Stepan; Mikhailovskiy, Nikita; Sedunin, Alexander; Okunevich, Iaroslav; Kalinov, Ivan; Kurenkov, Mikhail; Tsetserukou, Dzmitry

doi:10.1109/case49439.2021.9551413

Cited by 36 publications

(11 citation statements)

References 45 publications

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“…This shortcoming is one of the main obstacles to visual SLAM algorithms application into outdoor mobile robotics: the outdoor light conditions are highly unpredictable, and, therefore, the reliability of such algorithms is insufficient for outdoor operation. The use of visual algorithms is challenging even in indoor conditions: for example, robots operating in warehouses [3], [4], shopping rooms [5] and hospitals [6], [7] require a sufficient level of lighting, that, in case of fully unmanned facilities, increases the operation costs.…”

Section: A Motivationmentioning

confidence: 99%

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

Savinykh¹,

Kurenkov²,

Kruzhkov³

et al. 2022

Preprint

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Existing visual SLAM approaches are sensitive to illumination, with their precision drastically falling in dark conditions due to feature extractor limitations. The algorithms currently used to overcome this issue are not able to provide reliable results due to poor performance and noisiness, and the localization quality in dark conditions is still insufficient for practical use. In this paper, we present a novel SLAM method capable of working in low light using Generative Adversarial Network (GAN) preprocessing module to enhance the light conditions on input images, thus improving the localization robustness. The proposed algorithm was evaluated on a custom indoor dataset consisting of 14 sequences with varying illumination levels and ground truth data collected using a motion capture system. According to the experimental results, the reliability of the proposed approach remains high even in extremely low light conditions, providing 25.1% tracking time on darkest sequences, whereas existing approaches achieve tracking only 0.6% of the sequence time.

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Section: A Motivationmentioning

confidence: 99%

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

Savinykh¹,

Kurenkov²,

Kruzhkov³

et al. 2022

Preprint

Self Cite

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“…The Covid-19 pandemic of 2020 has created a rapidly growing trend for minimizing personal contact and robotic automatization in all areas of human life and industry application, e.g., for stocktaking automation in warehouses [4]- [7], shopping malls [8], disinfection of hospitals and offices [9], [10], and autonomous chargers [11]. For epidemiological security, and due to vast advances in automation technology, various contact-less last-mile delivery methods are currently being developed.…”

Section: A Motivationmentioning

confidence: 99%

CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification

Protasov¹,

Karpyshev²,

Kalinov³

et al. 2021

Preprint

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Mobile autonomous robots include numerous sensors for environment perception. Cameras are an essential tool for robot's localization, navigation, and obstacle avoidance. To process a large flow of data from the sensors, it is necessary to optimize algorithms, or to utilize substantial computational power. In our work, we propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous outdoor mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360degree field of view was used as the experimental setup. The obtained experimental results revealed that the proposed optimization accelerates the inference time of the neural network in the cases with up to 5 out of 6 cameras containing target objects.

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“…Amidst coping with pandemics like COVID-19, the demand for cleaning services has increased steeply in recent years [ 2 ]. For the past decade, we can observe a successful usage of leading-edge technologies, including robots for efficient cleaning in both domestic and professional cleaning domains [ 3 , 4 , 5 , 6 ]. The necessity for high-quality performance, efficiency, and favorable factors from the industry has paved the way for the successful application of robotics technology in the field of automated cleaning.…”

Section: Introductionmentioning

confidence: 99%

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

Pathmakumar

Muthugala

Samarakoon

et al. 2022

Sensors

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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.

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UltraBot: Autonomous Mobile Robot for Indoor UV-C Disinfection

Cited by 36 publications

References 45 publications

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification

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

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