Neural networks in robotics
- 1. NEURAL NETWORKS IN ROBOTICS THIS IS THE FIRST VERSION AND IT WILL BE UPDATED YASMIN M.ELDERINY
- 2. AGENDA • Pros & cons of using Artificial Neural Networks in Robotics • Types of NN applied in robotics field • Applications of NN in robotics • Resources
- 3. PROS & CONS OF USING ARTIFICIAL NEURAL NETWORKS IN ROBOTICS • Pros of using NN in Robotics 1- A good method to deal with nonlinear systems and provide nonlinear models. 2- The ability of learning functions whose forms are difficult to derive using data recorded from a system or even learn on-line then use what is previously learned to adapt to changes happened to the system. 3- Parallel processing which results in fast computing and processing. 4- Multivariable system as it process many inputs and outputs. 5-Data fusion as it can operate on both quantitative an qualitative data.
- 4. PROS & CONS OF USING ARTIFICIAL NEURAL NETWORKS IN ROBOTICS .. CONT’D • Cons of using NN in Robotics These cons are caused by some types of NN 1- The probability to get stuck in local minimum. 2- less accuracy. 3- There’s no known method to determine the number of nodes.
- 5. TYPES OF NN APPLIED IN ROBOTICS FIELD 1- Backpropagation :- it’s useful for classification problems and it’s good for learning continuous functions 2- Kohonen Network :- Instead of learning an internal representation of a function, each neuron in the network will remember a feature of the problem an resultant response, and this gives the network the ability to restrict the process of updating weights to specific neurons instead of updating the entire network. 3- The Hopfield Network :- It’s frequently used for optimization problems in robotics field but it’s not very suitable for real time control. There are other types which are rarely used in robotics field -Adaline -CMAC (Cerebral Model Articulated Control)
- 6. APPLICATIONS OF NN IN ROBOTICS 1- Kinematics is the study of the movement of multi-degree of freedom that forms the structure of robotic systems. Forward kinematics, the joint angles are the inputs and the outputs would be the coordinates of the end-effectors. Inverse kinematics, the given inputs are the coordinates of the end- effectors and the outputs to calculate are the joint angles. Inverse kinematics is the difficult one because it’s computationally intensive and has multiple solutions. NN may be able to reduce the computational complexity.
- 7. APPLICATIONS OF NN IN ROBOTICS .. CONT’D 2- Robot Dynamics is concerned with the relationship between the torques applied to the joint and the joint coordinates , velocities and accelerations. The nonlinear mapping prosperity of neural network is important to deal with this kind of problems. 3- Trajectory planning is the process of defining a desired path in joint or Cartisian space for joints or grippers.
- 8. APPLICATIONS OF NN IN ROBOTICS .. CONT’D 4- Vision and Sensor systems Sensors and cameras are commonly integrated in robots. The parallesim property of NN is useful to deal with such cases. 5- Control Many researches has been done in control robots such as MLP( Multi Layer Perceptron) , CMAC , Hopfield networks and other types of NN
- 9. REFERENCES • Neural Networks in Robotics survey • Neural Networks for Control Systems survey • A survey of neural network applications in robotics