Robot Sensor: Circular Memory Control with Python

Have you ever imagined an explorer robot with limited memory, whose actions are governed by a simple flow of instructions? 🤖 Today, we’re going to simulate that process, facing a challenge that combines memory manipulation with strategic movements.

🔮 Problem Statement

Our explorer robot has a set of memory cells and receives instructions from a sensor. The goal is to implement a function that processes these instructions and updates the memory cells accordingly.

Parameters:

• actions (string): A text string containing the sensor’s instructions. Possible instructions are:
  • '+': Add 1 to the current memory cell.
  • '-': Subtract 1 from the current memory cell.
  • '*': Multiply the current memory cell by 2.
  • 'r': Move to the memory cell to the right.
  • 'l': Move to the memory cell to the left.

Return Value:

• int[8]: An array of 8 integers representing the final state of the memory cells after applying all actions.

Example:

>>> robot_walkthrough('++l++-*l*l*')
[2, 0, 0, 0, 0, 0, 0, 2]

Additional Notes:

• The robot has 8 memory cells, initialized to 0.
• If a cell exceeds the value 255, it returns to 0.
• If a cell is less than 0, it is initialized to 255.
• The memory cells are circular. Moving to the right of the last cell takes you to the first, and vice versa.
• We always start from the first memory cell (index 0).

🧩 Step-by-Step Solution

We’ll start by initializing our robotic “brain”: a list representing the memory cells. We also need a pointer to track the current cell we’re operating on.

cells = [0] * 8
actual = 0

Next, we iterate over each action provided by the sensor. This is where the robot’s logic comes to life. ⚙️

for action in actions:
    if action in '+-*':
        cells[actual] += 1 if action == '+' else -1 if action == '-' else cells[actual]
    elif action in 'rl':
        actual += 1 if action == 'r' else -1

This snippet is the heart of our function. If the action is an arithmetic operator, we modify the value of the current cell. If it’s a movement, we adjust the actual index to move to the correct cell.

It’s crucial to keep the pointer within the bounds of the array. We implement circular behavior, as if the cells were arranged in a circle.

    if actual > 7 or actual < 0:
        actual = 7 if actual < 0 else 0

Finally, we control the values of the cells so they remain within the valid range (0-255). If a cell exceeds the limit, we reset it.

    if cells[actual] > 255 or cells[actual] < 0:
        cells[actual] = 0 if cells[actual] > 255 else 255

Complete Solution:

def robot_walkthrough(actions):
	"level: difficult; points: 8; array_strictly_equal: True"
	cells = [0] * 8
	actual = 0
	for action in actions:
		if action in '+-*':
			cells[actual] += 1 if action == '+' else -1 if action == '-' else cells[actual]
		elif action in 'rl':
			actual += 1 if action == 'r' else -1
		if actual > 7 or actual < 0:
			actual = 7 if actual < 0 else 0
		if cells[actual] > 255 or cells[actual] < 0:
			cells[actual] = 0 if cells[actual] > 255 else 255
	return cells

🧠 Key Concepts

One of the pillars of this solution is circular index handling. Instead of simply letting the index go out of bounds of the array, we adjust it to “wrap” back to the beginning or end. This emulates a memory space connected in a loop, essential for many data processing and simulation algorithms. Did you know that this concept is extensively used in the design of buffers in operating systems and signal processing?

Chained conditionals (if/elif/else) are vital for directing the program flow based on the current action. They allow us to process different types of instructions efficiently without resorting to multiple separate if blocks.

Finally, value limit management in the cells (0-255) introduces a simple form of overflow and underflow, fundamental concepts in computer architecture and data representation. Understanding how to deal with these situations is crucial for writing robust and predictable code.

💫 Final Thoughts

This simulation, although simple, opens the door to more complex problems. We could add new instructions, increase the memory size, or even introduce programming concepts, such as conditional jumps or subroutines.

An interesting improvement would be to allow numeric values to be entered directly into the cells, instead of just unit increments or decrements. Another possibility is to add a register that stores the last action performed, allowing the robot to “remember” its recent history.

Did you find this journey through our robot’s memory interesting? If you want to continue exploring the intricacies of code and discovering how to bring your own digital creations to life, don’t hesitate to explore more articles on our programming blog! 🚀