The Mathematize
Well…Well…Well…! I am 19 articles away from marking 100 days of being consistent in my learning. Learning in school was forced, & I don’t do well around authorities anyway.
The more they tell me to be a certain way; I will be distant & in conflict. Whatever you wanna call it!? stubborn, rebellious, or just a brat! IDC.
Today my ADHD brain is overworking, & I am all over the place! I know…I know…. doctors might help, but hey, I am a rebel. Lol. So I am curing myself by learning AI.
Let’s get started on AI algorithms in Robotics!
Convolutional neural networks (CNNs):
CNNs are commonly used in robotics for perception tasks, such as object recognition and segmentation, as well as for navigation and mapping.
For example, a robot can use a CNN to recognize and classify objects in its environment, and to map out a path based on that information.
CNNs are particularly effective for these tasks because they can learn to identify features that are relevant to the task at hand, even in noisy or complex environments.
Reinforcement learning (RL) and Deep reinforcement learning (DRL):
RL and DRL are machine learning algorithms that enable a robot to learn from trial and error to optimize its actions and behaviors. RL algorithms work by defining a reward function that the robot seeks to maximize over time.
The robot takes action and receives feedback (in the form of rewards or penalties) based on how well it performs the task. Over time, the robot learns to associate certain actions with higher rewards and adjusts its behavior accordingly.
DRL algorithms extend RL by using deep neural networks to enable the robot to learn more complex and abstract behaviors, such as playing a game or performing a task that requires a high level of cognitive reasoning.
Particle filters:
Particle filters are a type of Bayesian filter that can be used in robotics for localization and mapping. They work by representing the robot’s position and orientation using a set of particles, each with its weight.
As the robot moves through its environment, the particles are updated based on sensor data, and the robot’s position and orientation are estimated based on the distribution of particles.
Kalman filters:
Kalman filters are another type of Bayesian filter that can be used in robotics for state estimation and control. They work by estimating the state of a system based on noisy sensor data and using that estimate to control the system.
Kalman filters are particularly effective for systems that have linear dynamics and Gaussian noise, such as a robot’s position and orientation.
Proportional-integral-derivative (PID) control:
PID control is a classic control algorithm that can be used in robotics to regulate a system’s behavior. It works by using feedback to adjust the system’s output based on the difference between the desired set point and the measured output.
PID control is particularly effective for systems with simple dynamics that can be modeled mathematically.
What the heck!? filters are algorithms?
Yes, filters can be considered algorithms. In the context of robotics and AI, filters are mathematical algorithms that are used to process sensor data and estimate the state of a system.
Filters extract useful information from noisy and uncertain data and make predictions or control decisions based on that information.
These AI algorithms enable robots to perform a wide range of tasks, from perceiving and navigating through complex environments to learning and adapting to new situations to executing complex behaviors and tasks.
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