Discover the ideal robotic application for single layer feedforward networks. Learn why wall following is perfectly suited for this simple neural network architecture.
Table of Contents
Question
Which application out of these of robots can be made of single layer feedforward network?
A. wall climbing
B. rotating arm and legs
C. gesture control
D. wall following
Answer
D. wall following
Explanation
The question asks which robotic application can be effectively implemented using a single layer feedforward network. The correct answer is D. wall following.
Understand Single Layer Feedforward Networks
A single layer feedforward network, often referred to as a perceptron, is the simplest form of a neural network. It consists of an input layer and an output layer without any hidden layers in between. This simplicity means that it can only solve linearly separable problems and is best suited for tasks that do not require complex decision boundaries or feedback mechanisms.
Application in Robotics
In robotics, tasks vary significantly in complexity, from simple navigation to complex decision-making processes. A single layer feedforward network is suitable for basic control tasks that do not require intricate data processing or feedback loops.
Wall Following
Wall following is a straightforward task where a robot maintains a certain distance from a wall while moving alongside it. This task can be effectively managed using simple sensory input (e.g., distance sensors) to adjust the robot’s path. The linear nature of the problem—maintaining a consistent distance from the wall—makes it ideal for a single layer feedforward network, which can directly map sensor inputs to control actions without needing complex computations or adjustments.
Why Other Options Are Less Suitable
- Wall Climbing (A): This task involves complex dynamics and requires the robot to adapt to various surfaces and angles, demanding more sophisticated processing than a single layer can provide.
- Rotating Arm and Legs (B): Coordinating multiple joints and limbs involves solving inverse kinematics problems, which typically require more complex neural networks with hidden layers.
- Gesture Control (C): Recognizing and interpreting gestures involves processing visual or sensory data patterns, which are often non-linear and require deeper networks like CNNs for effective performance.
In summary, wall following is the most appropriate application for a single layer feedforward network due to its simplicity and linear nature, making it feasible without the need for complex computations or feedback mechanisms.
Wall folloing is a simple task and doesn’t require any feedback.
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