Company: OTSA
Filing Date: 2025-06-09
Form Type: F-1
Source: 0001213900-25-052720
Chunk: 169

Company: OTSAW Ltd
Filing Date: 2025-06-09
Form: F-1
Chunk 169
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 proximity, even where multiple obstacles may be moving at different velocities without a pre -knownmotion profile. This capability allows the AMRs to reliably and safely navigate in busy, ever -changingdynamic outdoor and indoor environments. Fully AI -enabledAMRs must be able to learn, adapt, remember, and constantly improve their movements. Our proprietary AI principles and machine learning algorithms empower AMRs with the advanced ability to interact with their surrounding environments. Our AMRs can respond, adapt, learn, and recover from failures and unpredictability. The machine learning algorithm is designed to gather as much information as possible. Through repeated learning and adaption in proportion to the hours they operate, the AMRs can continuously enhance their understanding of their operating environment. The more information an AMR learns, the more intelligent it becomes. Over time, AMRs can more efficiently prioritize and filter collected data throughout its deployment and operation, improving its decision -makingability across various situations to ensure safety, efficiency and reliability. Fleet Management and Control System We developed a software platform, the Fleet Management and Control System, for our AMR users and operators. The platform easily integrates into the user’s existing system, offering high compatibility and rapid implementation. The platform is user -friendly, it is highly scalable and adaptable to a wide range of application scenarios. The platform allows real -timedata access, AMR traffic control, battery charging management, live video feed, automated task allocation, real -timeremote monitoring, and the ability to operate multiple AMRs across different sites. The platform 103 also supports two -wayaudio -visualcommunication and the ability to schedule autonomous navigation. The platform offers two methods of fleet management. In the “Smart Fleet, Simple Robot” model, the fleet manager oversees all robot movements, determining routes, stops, and interactions with infrastructure. Alternatively, the “Simple Fleet, Smart Robot” model lets the fleet manager assign tasks, while the robots make local decisions autonomously, such as managing their stops and routes. The choice between these models depends on specific operational needs and use cases. Consequently, AMR enabled facilities are able to benefit from more comprehensive services and multitasking capabilities by the AMRs. Moreover, our solutions help our customers reduce their carbon footprint, aligning with their Environmental, Social, and Governance (ESG) objectives. By optimizing resource use and reducing waste, our customers can achieve significant sustainability milestones, positioning themselves as responsible and forward -thinkingorganizations in their respective industries. Our AMRs and UV-C LED Disinfection Systems We offer a range of AM