How can LiDAR and robot learning revolutionize the mining industry?

LiDAR and robot learning are changing the mining business. Highly accurate 3D mapping, situational awareness, blast optimisation, automated stockpile assessment, and environmental monitoring are available via LiDAR. Smart robots using LiDAR and machine learning can do jobs without human assistance, boosting safety. Machine learning algorithms can forecast faults and schedule maintenance, reducing downtime and increasing efficiency. Optimisation of resource extraction, decision-making insights, and worker safety are also possible. Rio Tinto uses autonomous trucks, BHP Billiton uses LiDAR-equipped drones, Glencore optimises blast patterns, and Komatsu develops autonomous mining solutions.

LiDAR is a great technology to detect the distance from the sensor to the objects. They are very robust, have big ranges, and come in different forms so that this technology can be used in various applications. Besides that, there are many algorithms that are created specifically for LiDARs which simplifies their usage.

LiDARs can detect obstacles in a singular plane, few have tilt function to increase its field of view. LiDARs are considered the most accurate form of radar.

Robot learning is the hardware version of Intelligent Agents, by using machine learning and equipped with sensors and actuators, it can collect data about its environment, analyze it , then act accordingly.

LiDAR synchronized in conjunction with mobile robots solves the task of SLAM(Simultaneous Localization and Mapping). The raw data sent by LiDAR sensors can be processed by the robot to obtain the localization information and create the Mapping environment, required for the surrounding. In context of Machine Learning, the extensive data produced, can be used to understand the nature of the volatile surrounding in which the mobile robots operate, thereby enhancing efficiency in terms of speed of operation, safety of personnel or human collaboration in industrial spaces.

Robot learning can help to simplify the processing and understanding of data from LiDARs. This is especially useful for unstructured environments or to detect object classes that have a certain variety within the set. For example, using learning, one can make better algorithms to detect humans in the data from LiDAR than using classical approaches.

It refers to the synergy between LiDAR's spatial information, i.e. 3D, and Machine Learning's ability to process complex pattern algorithms and data analysis --critical information for making real-time decisions-- e.g. landscape changes, identification of landforms or assessing slope stability.

LiDARs is one of the most fundamental sensors for a robot to have. Due to its accuracy and speed, LiDARs enables the robot to see fast to analyze and react faster than typical wave based sensors.

One of the most important features of the LiDAR sensors, it does not need a source of light to see. LiDARs sensors emit their laser lights to aquire distance of surrounding objects. So in case of an accident in the mine, robot equipped with LiDAR is one of the most ideal solutions.

Robot navigation in unseen environment is a challenge. LiDAR gives information of the terrain, but it isn't sufficient enough information for navigating as the terrain can change abruptly. It's only through learning the robot can have a insight about the slopes, potholes, obstacles

SLAM based Lidar is currently used in mining coupled with UAV's to provide accurate real time data to decision makers. This combination of technologies aid in underground mapping of mining activities, changes in the underground structure and geology over time and has been very helpful in data gathering after an incident to help determine best, safest path forward. Lidar is enabling underground autonomy of UAV's in an otherwise unknown unmapped environment allowing exploration and data gathering in a very safe manner.

A typical challenge of using those two technologies is the reliability of learning algorithms. On the LiDAR side, there are possible wrong measurements due to different materials in its surroundings. Nevertheless, using simply LiDARs, safety can be increased incredibly without complex processing.

Like any new technology, maturity and experience are important to create a reliable solution, especially in a very rugged, demanding environment such as mining. Some of of the Robot+Lidar solution companies are more mature than others and it's important for users to do their homework to ensure.

Mapping of mining environments is one straightforward approach. Learning methods are then useful to utilize such maps in full.

LiDAR and robot learning could improve mining safety, efficiency, productivity, and sustainability. In dangerous areas, LiDAR-equipped robots can independently provide real-time 3D maps for monitoring and resource extraction. Robots can be guided to specific ore sources to reduce waste and maximise resource use. Avoid costly downtime with robot learning algorithms that predict equipment breakdowns. Increased productivity and lower labour costs from automation improve resource extraction and waste reduction. Data-driven decision-making optimises mining processes and reduces costs. However, costly initial investment, connection with current systems, and ethical issues make these technologies difficult to integrate.