Neural networks in robotic vision and guidance

Humans see "stereoscopically" the eyes view two slightly different images of something and combine them into a three-dimensional image in the brain, those inputs are made into a scene with depth, color, and texture. Neurons in the brain do the work.?

If we can give robots new abilities, they can take on new jobs, or work faster and more repeatably.?

Robots guided by 3D vision systems can recognize objects and are generally repeatable. But these work cells are typically slower than humans, struggle with randomness and don’t always take the optimal path. Customized programming and specialized lighting complicate their deployment.?

If the existing way of giving robots better sight has reached its limits, what’s the new alternative??

Teach a neural network to see stereoscopically?

Neural networks offer a powerful method for interpreting 2D images into 3D scenes. They mimic neurons in the human brain and can give robots human-like sight. AI is sometimes called a “black box,” but the math can be fully understood. A neural network is a software function. If you opened the code, you would see a huge list of constants, the numerical values of the image taken by the 2D cameras, an enormous series of multiplications, additions, replacing negative values with zero, and not a lot more.?

The special part to focus on advanced neural network is how it can be written. The human part of building the neural network is defining the problem and how the resulting robotic work cell should perform. Defining the problem involves these questions:?

• What is the object geometry??
• What is its material??
• What are the size and characteristics of the bin or area where the objects are located??
• What is the position of the robots??
• What is the height of the cameras??

To train a neural network, we gather a large collection of images along with the desired outputs. For each one, we can question “what small change can be made to the list of constants that will make the output closer to the desired output?” Then make the change. It could be good or bad change. Gradually, the functions will improve.??

The process involves an accelerated computing machine analyzing a large dataset of images of the object, subject to a high level of variation in each image, including its position and contact against other objects, dimensions, finish (e.g. shiny) and interaction with light.?

How to approach?

Synthetic data is a path out of this trap. We can create images that parallel the real world, like sockets in a bin, or the assembly of a part.?For example consider the below image, it represents a visual representation of box of items, when feed into neural network it will teach neural network a understanding of how objects can present themselves in the real world.?

The alternative method is to write a traditional code. That would collect several images, along with the desired outputs. The logic would be gradually built up, attempting to match actual outputs to desired outputs, until the complexity was overwhelming, and the problem declared too hard.?

One advantage of training neural networks is that it’s a data-driven strategy. If one has a function to write, the main requirement is to gather a large set of images and desired outputs.

Here’s a detailed look at how neural networks can be utilized in various aspects of robot control:

1. Learning Control Policies

Neural networks can be used to learn control policies through methods like reinforcement learning (RL). In this approach:

• Training: A neural network learns by interacting with an environment and receiving feedback in the form of rewards or penalties. For example, a robot might learn to navigate a maze by receiving positive reinforcement for reaching the goal and negative reinforcement for collisions.
• Deployment: Once trained, the neural network can generate control actions (such as motor commands) based on the current state of the robot and its environment.

2. Sensor Fusion and Perception

Neural networks can process and integrate data from multiple sensors to enhance a robot’s perception:

• Data Integration: For instance, a neural network can combine inputs from cameras, lidar, and IMUs (Inertial Measurement Units) to create a comprehensive understanding of the robot’s surroundings.
• Feature Extraction: Convolutional Neural Networks (CNNs) are particularly effective for extracting features from visual data, enabling tasks like object recognition, scene segmentation, and obstacle detection.

3. Trajectory Planning and Motion Control

Neural networks can assist in planning and controlling robot trajectories:

• Path Planning: Neural networks can predict and plan the robot’s path to avoid obstacles and reach goals efficiently. Techniques like Deep Q-Networks (DQN) and policy gradients can be used for this purpose.
• Motion Control: Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks can handle temporal dependencies in motion control, allowing for smooth and adaptive movements.

4. Adaptive Control Systems

Neural networks can be used to adapt control parameters in real-time:

• Parameter Tuning: Adaptive control systems can use neural networks to adjust parameters based on performance feedback. For example, a neural network might learn to compensate for changes in load or environmental conditions.
• Real-Time Adjustment: The network can process real-time sensor data and adjust control strategies dynamically to maintain optimal performance.

5. Human-Robot Interaction

Neural networks can enhance interactions between robots and humans:

• Speech Recognition: Neural networks, especially those trained on natural language processing tasks, can enable robots to understand and respond to voice commands.
• Gesture Recognition: Neural networks can be trained to recognize human gestures through visual inputs, allowing robots to interpret and respond to non-verbal cues.

6. Simulation and Training

Neural networks are useful for training robots in simulated environments:

• Simulated Training: Robots can be trained in virtual environments using neural networks to learn and refine control strategies before applying them in real-world scenarios.
• Transfer Learning: Knowledge gained in simulation can be transferred to real robots, reducing the need for extensive real-world training.

7. Fault Detection and Recovery

Neural networks can be employed to detect and recover from faults:

• Anomaly Detection: Neural networks can monitor sensor data and control signals to identify anomalies or potential failures.
• Recovery Strategies: Once a fault is detected, the neural network can help devise recovery strategies or alternative actions to maintain operational functionality.

What are the conclusions we reach from this information?

AI allows us to build sophisticated mathematical models that create human-like vision and speed. Modern image processing technology and computing power makes it all possible. Their inner workings can be reverse engineered, reweighted and fully understood. And AI/ML will become closer and closer to human sight, speed and perception.?