Robot joint manufacturer from FoxTech: In architectural surveying, handheld LiDAR provides a fast and accurate way to capture the dimensions and structure of existing buildings. This includes gathering data for renovations, expansions, or verifying on-site conditions. The generated 3D models can also support Building Information Modeling (BIM) workflows, enhancing the precision and efficiency of construction projects. For archaeology, handheld LiDAR enables non-invasive scanning of fragile structures, artifacts, and excavation areas. The technology helps researchers record detailed site conditions and monitor changes over time, preserving valuable data while minimizing the risk of damage. Read extra details on handheld lidar scanner.
We offer a variety of robot chassis, including tracked, wheeled, and Automated Guided Vehicle (AGV) platforms, suitable for industrial, security, and logistics applications. These chassis feature high payload capacity, all-terrain adaptability, and intelligent navigation systems, enabling efficient automation solutions. Our UGV Crawler Chassis offers robust all-terrain mobility for demanding applications. Designed for payloads ranging from 50kg to 120kg, these platforms are ideal for outdoor inspections, remote operations, and security tasks. Featuring advanced navigation and rugged track designs, they ensure stable performance on various terrains.
Heritage Building Scanning in Ximen Old Street, Yiwu, Zhejiang (Handheld + Aerial Mode) – According to user requirements, a historical building was scanned using both aerial and handheld modes, resulting in a complete dataset of the heritage structure. Highway Bridge Facade Scanning in Zhejiang (Aerial Mode Only) – Data collection focused on evaluating bridge navigability. The measured area included both facades of a 1400-meter bridge section. Manual drone flights enabled full-scope scanning in a single mission, significantly improving efficiency. The data showed elevation accuracy better than 5 cm, supporting accurate navigability assessments.
Here’s how handheld lidar can improve your bottom line: Reduced Labor Costs: Faster data collection means less time spent on fieldwork, reducing labor expenses. Fewer Errors: Accurate data minimizes the need for rework, saving time and money. Increased Productivity: Streamlined workflows and faster data processing lead to increased productivity and higher revenue. Improved Safety: Less time spent in the field reduces the risk of accidents and injuries, lowering insurance costs. New Revenue Opportunities: The ability to offer new services, like 3D modeling and virtual tours, can generate additional income. Calculate the ROI of investing in a handheld lidar scanner for sale for your specific business. Consider factors like labor costs, project timelines, and potential revenue increases. You might be surprised at how quickly the investment pays for itself. We at Foxtech Robotics can help you assess your needs and find a solution that fits your budget. Find additional info at foxtechrobotics.com.
Kicking off 2025, humanoid robots continue to dominate headlines, from a dazzling presence at CES 2025 to shaking up capital markets. Industry giants are entering the fray, while companies race to announce mass production plans. This revolutionary tool is rapidly advancing, with its transformative potential drawing increasing attention. The humanoid robotics industry is on the brink of reshaping technology and society, underscoring its growing importance and imminent impact across various sectors. With advancements in AI, modular design, and lightweight materials, humanoid robots are poised to become integral to industrial operations. Energy management innovations, such as new battery technologies, will enhance performance. As costs decline and capabilities expand, the global market for humanoid robots is expected to grow significantly from 2024 to 2035, reshaping industrial processes across multiple sectors.
Technology Breakthrough: How Handheld SLAM Devices Solve These Challenges – Open-pit mines are vast. Static scanning requires repeated setup, which slows down data collection and makes large-scale modeling inefficient. High labor costs: Traditional methods require team coordination and involve cumbersome workflows prone to human error. Poor adaptability to dynamic scenes: Mining operations are highly dynamic. Activities such as blasting, excavation, and support frequently change the terrain. Static survey results become outdated quickly, limiting their usefulness in real-time decision-making. Geological disasters, like collapses or landslides, demand rapid post-event mapping to assess the site quickly and accurately.