Answer
See the explanation
Work Step by Step
To design a sequence of experiments to determine where the sensors should be placed to produce a robot that successfully pushes a basketball in a straight line, follow these steps:
1. **Initial Placement**: Start by placing one sensor on the front of the robot, facing forward. Leave the other sensor unplaced for now.
2. **Test Movement**: Test the robot's ability to move forward in a straight line with only one sensor. Note any deviations from the desired path.
3. **Adjustment**: If the robot deviates from the straight line, adjust the position of the single sensor to minimize the deviation.
4. **Second Sensor Placement**: Once the robot can move relatively straight with one sensor, add the second sensor to the robot.
5. **Test and Refine**: Test the robot's ability to move forward in a straight line with both sensors. Experiment with different placements and orientations of the second sensor to minimize deviations from the desired path.
6. **Final Adjustment**: Fine-tune the positions and orientations of both sensors to optimize the robot's ability to push the basketball in a straight line.
Comparing this sequence of experiments to an evolutionary system:
- In the sequence of experiments, the process is more guided and controlled. Each step is carefully designed to achieve a specific outcome (moving the basketball in a straight line).
- The sequence relies on human intervention and decision-making to adjust and optimize the robot's design.
- Evolutionary systems, on the other hand, typically involve a more autonomous process where multiple variations are generated, tested, and selected based on their performance in achieving a specific objective.
- Evolutionary systems mimic the process of natural selection, where the fittest variations are selected for reproduction and further adaptation over multiple generations.
- While both approaches aim to optimize a system for a specific task, the sequence of experiments provides a more deterministic and directed approach, while evolutionary systems offer a more adaptive and potentially exploratory method.
