How do you solve Walking Robot Simulation on LeetCode?

Walking Robot Simulation (LeetCode #874) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Using a set for obstacles allows for O(1) lookups, which is crucial for performance.

What is the time complexity of Walking Robot Simulation?

The optimal solution for Walking Robot Simulation runs in O(n) time and uses O(n) space. The time complexity is O(n) because we process each command once, and checking for obstacles is O(1) due to the hash set.

What is the brute force solution for Walking Robot Simulation?

The brute force approach for Walking Robot Simulation is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach simulates each command step by step, checking for obstacles at every move. This method is straightforward but inefficient, as it checks every possible position for obstacles. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Walking Robot Simulation?

Walking Robot Simulation uses the following concepts: Array, Hash Table, Simulation. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Walking Robot Simulation?

Always clarify the problem requirements and constraints before jumping to code. Think about edge cases, such as starting on an obstacle or having multiple obstacles. Explain your thought process while coding to demonstrate your understanding.

What are common mistakes when solving Walking Robot Simulation?

Not checking for obstacles before moving, which can lead to incorrect distance calculations. Confusing the direction changes and not updating the direction correctly.

#874

Walking Robot Simulation

Medium

ArrayHash TableSimulationHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

The optimal solution uses a set to track obstacles and simulates the robot's movements efficiently. Instead of checking for obstacles at each step, we only check the next position before moving.

⚙️

Algorithm

3 steps

1Step 1: Initialize the robot's position at (0, 0) and set its initial direction to north.
2Step 2: Store obstacles in a set for O(1) lookup time.
3Step 3: For each command, adjust the direction for turns and move forward while checking for obstacles only at the next position.

solution.py26 lines

1# Full working Python code
2class Solution:
3    def robotSim(self, commands, obstacles):
4        direction = 0  # 0: North, 1: East, 2: South, 3: West
5        pos = (0, 0)
6        obstacle_set = set(map(tuple, obstacles))
7        max_distance = 0
8        for command in commands:
9            if command == -2:  # Turn left
10                direction = (direction + 3) % 4
11            elif command == -1:  # Turn right
12                direction = (direction + 1) % 4
13            else:  # Move forward
14                for _ in range(command):
15                    if direction == 0:  # North
16                        next_pos = (pos[0], pos[1] + 1)
17                    elif direction == 1:  # East
18                        next_pos = (pos[0] + 1, pos[1])
19                    elif direction == 2:  # South
20                        next_pos = (pos[0], pos[1] - 1)
21                    else:  # West
22                        next_pos = (pos[0] - 1, pos[1])
23                    if next_pos not in obstacle_set:
24                        pos = next_pos
25                    max_distance = max(max_distance, pos[0]**2 + pos[1]**2)
26        return max_distance

ℹ

Complexity note: The time complexity is O(n) because we process each command once, and checking for obstacles is O(1) due to the hash set.

1Using a set for obstacles allows for O(1) lookups, which is crucial for performance.
2Understanding direction changes and how to represent them with simple arrays or vectors can simplify the code.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.