Bridge Detection Features

Detection: The integration of computer vision technology into the autonomous rock breaker system is a game-changer. It enables the system to detect, measure, and track rock bridges, providing the data needed for efficient, proactive, and safe rock breaking operations. This technology is a key element in the move towards fully autonomous rock breaking, offering significant benefits in terms of efficiency, safety, and cost-effectiveness.

Rock Bridge Detection and Segmentation: Rock bridges are a common occurrence in mining and construction operations, where large rocks can form arch-like structures that block the flow of material. These bridges pose significant operational challenges and safety risks. By using advanced object detection and segmentation techniques, the computer vision system can accurately identify these rock bridges in real-time. This allows the autonomous rock breaker to target these structures for breaking, significantly improving the efficiency and safety of operations.

Measurement and Tracking: Beyond simple detection, the computer vision system can also measure the extent of the rock bridges, providing valuable data on their size, shape, and position. This information is crucial for planning the rock breaking process, ensuring that it is carried out in the most effective and efficient manner.

Moreover, the system can track the evolution of the rock bridges over time. This allows it to predict when and where breaking will be needed, enabling proactive management of the rock breaking process. This predictive capability can further enhance the efficiency of operations and reduce the risk of unexpected blockages or collapses.

Key Element for Autonomous Operation: The computer vision technology is a key element in enabling the autonomous operation of the rock breaker. By providing accurate, real-time data on the operational environment, it allows the autonomous system to make informed decisions and carry out its tasks without human intervention. This not only increases efficiency but also reduces the risk of human error and the potential for accidents or injuries.

Rock Breaker Arm Control

In the context of a rock breaker, which is a type of robotic arm with three joints each providing a rotational degree of freedom, inverse kinematics is used to determine the angles of these joints to position the end of the arm (where the rock breaker is located) at a desired location.

The exact inverse kinematics problem for a RRR rock breaker can be defined as follows:

Given a desired position (x, y, z) in 3D space for the end of the rock breaker, the goal is to find the joint angles (θ1, θ2, θ3) that will position the end of the arm at this location.

The solution to this problem involves a combination of trigonometric and algebraic equations. The exact form of these equations will depend on the specific configuration of the arms, including the lengths of the arm segments and the orientation of the joints. It's important to note that for a given position, there may be multiple sets of joint angles that can achieve that position (known as multiple solutions), or there may be no solution if the position is outside the reach of the arm.

Once the joint angles have been calculated, they are used to control the hydraulic actuators that move the joints, positioning the rock breaker at the desired location. This process is repeated many times per second to allow the arm to move smoothly and accurately.

While the exact inverse kinematics control can provide precise control of the rock breaker, it can be challenging to implement in practice due to factors such as the large size of the rock breaker, the harsh operating conditions, and the need for accurate position feedback. This is why alternative methods, such as reinforcement learning, are being explored for controlling the rock breaker.

Minealytics - Autonomous Rock Breaker Control

Minealytics offers a flexible and adaptable approach to controlling a rock breaker, capable of handling the large momentum of the machine, inaccurate sensor feedback, harsh operating conditions, wear and aging of components, changes in the environment, and variability in target bridge formations. By continuously learning from its interactions with the environment, RL can adapt its strategies to maintain optimal performance under a wide range of conditions.

1. Large Momentum: Rock breakers are heavy machines, and their large momentum can make them difficult to control precisely. RL can learn to account for this momentum in its decision-making process, adjusting the control actions to achieve the desired positioning and movement.

2. Inaccurate Sensor Feedback: Sensors used in harsh environments like mining or construction sites can often provide inaccurate feedback due to dust, vibrations, temperature variations, and other factors. RL algorithms can learn to interpret this noisy data and make optimal decisions despite the uncertainty.

3. Harsh Environment: The harsh operating conditions can cause unpredictable changes in the system dynamics. RL algorithms are capable of continuously learning from their interactions with the environment, allowing them to adapt to these changes and maintain optimal performance.

4. Wear and Aging: Over time, the components of the rock breaker can wear out and the hydraulics can age, altering the system's dynamics. RL can adapt to these changes by continuously updating its understanding of the system based on the latest data.

5. Changes in Environment: The environment in which the rock breaker operates can change significantly over time, due to factors like weather conditions, changes in the terrain, and variations in the material being broken. RL is well-suited to handle such variability, as it can learn and adapt its strategies based on the current conditions.

6. Variability in Target Bridge Formations: The formation of rock bridges can vary widely, making it challenging to predict and plan the breaking process. RL can handle this variability by learning from a wide range of experiences, allowing it to develop strategies that can handle different bridge formations effectively.

ROM Bin Autonomous Rockbreaking

In the context of Run-of-Mine (ROM) bin operations, the implementation of autonomous rock breaking using reinforcement learning and computer vision offers transformative benefits. Firstly, the speed of ROM bin blockage clearance is significantly enhanced as autonomous systems can operate continuously and more efficiently than manual counterparts. This leads to reduced downtime and increased throughput. Additionally, the use of computer vision for detecting 'bridges' or blockages enables pre-emptive rock breaking, where potential obstructions are identified and addressed before they escalate into full blockages, further optimising the flow of material. Automated cleanup operations, orchestrated by the Minealytics trajectory control of the rock breaker arms, ensure that the bin is kept in optimal condition with minimal human intervention. The system's ability to learn from human operators through imitation learning accelerates its proficiency, while continuous reinforcement learning allows it to adapt to changes in wear and tear, hydraulic oil degradation, and environmental conditions. This adaptability not only improves operational efficiency but also prolongs the lifespan of equipment and reduces maintenance costs, leading to a more sustainable and cost-effective mining operation.

Crusher Autonomous Rockbreaking

In the context of autonomous rockbreaking over crushers, integrating reinforcement learning and computer vision brings substantial advancements in operational efficiency and safety. The speed and precision of rock breaking are notably improved, directly impacting the crusher's throughput and reducing potential bottlenecks. This speed is vital in maintaining a steady flow of material, thus maximising crusher utilisation and reducing idle time.

Pre-emptive rock breaking is a significant advantage. With advanced computer vision, the system can identify oversized rocks or potential obstructions before they reach the crusher, mitigating the risk of jams and equipment damage. This proactive approach ensures smoother operations and minimises unplanned downtime, which is critical in high-volume processing environments.

Automated cleanup operations facilitated by intelligent trajectory control of the rockbreaker arms are crucial for maintaining crusher efficiency. This automation removes the need for manual intervention, which can be time-consuming and poses safety risks. By automating these processes, the system ensures consistent operational conditions and reduces human exposure to hazardous environments.

The incorporation of human imitation learning accelerates the system's ability to adapt to various operational scenarios, essentially learning from the best practices of experienced operators. Furthermore, continuous reinforcement learning allows the system to evolve and optimise its performance over time. It adapts to changes such as equipment wear, variations in rock properties, and environmental factors, ensuring sustained efficiency and reducing maintenance requirements. This adaptive capability is key in managing the dynamic and often harsh conditions in mining operations, leading to a more resilient, efficient, and safe rock-breaking process.

Technology

Reinforcement learning

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent learns from the consequences of its actions, rather than from being explicitly taught, which makes it a powerful tool for complex tasks like controlling a rock breaker. Here are some advantages of using reinforcement learning in this context:

1. Adaptability: RL algorithms can adapt to new environments and tasks. This is particularly useful in the context of rock breaking, where conditions can vary widely. The RL agent can learn and improve its performance over time, adapting to changes in the environment.

2. Handling Complexity: The task of controlling a rock breaker involves a series of complex decisions, such as positioning the rock breaker arms and timing the breaking process. RL is well-suited to handle such complex tasks, as it can learn from a vast amount of experiences and make decisions based on those experiences.

3. Scalability: RL algorithms can be scaled to different rock breaker configurations without significant additional costs. This means that the same RL algorithm can be used to control different types of rock breakers, reducing the time and cost of implementing automation.

4. Robustness: RL algorithms can handle uncertainty and variability in the environment. This is crucial in rock breaking operations, where conditions can be harsh and unpredictable. The RL agent can learn to make optimal decisions even under these conditions.

5. Elimination of Manual Derivation: Traditional control methods often require the manual derivation of control laws or kinematic equations, which can be time-consuming and error-prone. RL, on the other hand, learns these control strategies directly from interaction with the environment, eliminating the need for manual derivation.

6. Continuous Learning and Improvement: RL algorithms have the ability to continuously learn and improve their performance over time. This means that the more the rock breaker operates, the better the RL algorithm becomes at controlling it.