Drones and the Future of Highway, Street, and Bridge Construction

Overview

Emerging technologies are revolutionizing various industries, and highway, street, and bridge construction is no exception. In particular, the use of drones is increasingly defining the future of these sectors. This guide delves into the role of drones in highway, street, and bridge construction advancement and shares key insights on how drones are shaping these sectors and companies like 3Laws Robotics are leading the way in this transformation.

Drones in Highway Construction

Drones are increasingly taking center-stage in highway construction. The Associated General Contractors of America reported that 56% of construction firms were using drones as of 2020. Drones provide critical data for monitoring, planning, and executing construction projects which enhances safety, efficiency, and accuracy. They perform land surveying rapidly and accurately, reducing the process from weeks to mere hours. Given their efficiency, the global construction drone market is projected to reach USD 4.8 billion by 2026.

Drone Technology in Street Construction

Just as they are in highway construction, drones are vital tools in street construction. Drones improve the efficiency of projects through speedy and high-quality data collection for site assessments. They facilitate aerial imaging and 3D mapping which shortens the planning time by up to 60%. With the help of drones, mapping a two-mile street that would traditionally take about two weeks can now be done in a day. This improvement in time management efficiency contributes to a potential 20% reduction in overall project costs.

Impact of Drones on Bridge Construction and Inspection

In bridge construction and inspection, drones shine with their ability to execute inspections quickly and safely, reducing both the inspection time and associated risks. Drones can complete bridge inspections approximately 70% faster than human inspectors. Furthermore, their ability to capture high-resolution images from various angles reduces the risks involved in manual inspections by up to 96%, enhancing site safety significantly.

Key Takeaways

56% of construction firms are using drones for improved efficiency and safety, predicting a rise in the global construction drone market to USD 4.8 billion by 2026. • Drones reduce planning time by 60% in street construction, leading to a potential 20% reduction in overall project costs. • Bridge inspections can be done 70% faster with a 96% reduction in risk thanks to drone technology.


3Laws Robotics and Highway, Street, and Bridge Construction

At the forefront of this technological advancement is 3Laws Robotics. Developing innovative software to enhance safety and reliability for robotics systems, 3Laws Robotics addresses a significant pain point for robotics companies: certification. Their software, 3Laws Supervisor, not only simplifies this process by offering robust safety features but also aids in providing evidence of system robustness.

Built on Control Barrier Functions (CBFs), the technology claims to provide mathematically provable safety. The software showcases its adaptability across diverse industries and applications such as warehouse automation, human-robot interaction, and dynamic environments.

Notably, 3Laws Robotics has helped an autonomous forklift customer achieve a 40% efficiency gain, resulting in a 6-month payback period. The company's software is adaptable, allowing it to work with a wide range of platforms, including mobile robots, cars, drones, and manipulators. It is also compatible with popular robotics middleware such as ROS and ROS2, and aims to enhance operational efficiency by minimizing downtime caused by unnecessary e-stops or collisions.

3Laws Robotics is the next-generation safety solution for highway, street, and bridge construction through its dynamic, predictive safety technology. Its ability to provide real-time guardrails for autonomy stacks positions it as a key player in unlocking the full potential of robotics in these domains.






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News in Robot Autonomy