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MD ARIFUL HAQUE
MD ARIFUL HAQUE

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874. Walking Robot Simulation

874. Walking Robot Simulation

Difficulty: Medium

Topics: Array, Hash Table, Simulation

A robot on an infinite XY-plane starts at point (0, 0) facing north. The robot can receive a sequence of these three possible types of commands:

  • -2: Turn left 90 degrees.
  • -1: Turn right 90 degrees.
  • 1 <= k <= 9: Move forward k units, one unit at a time.

Some of the grid squares are obstacles. The ith obstacle is at grid point obstacles[i] = (xi, yi). If the robot runs into an obstacle, then it will instead stay in its current location and move on to the next command.

Return _the maximum Euclidean distance that the robot ever gets from the origin squared (i.e. if the distance is 5, return 25).

Note:

  • North means +Y direction.
  • East means +X direction.
  • South means -Y direction.
  • West means -X direction.
  • There can be obstacle in [0,0].

Example 1:

  • Input: commands = [4,-1,3], obstacles = []
  • Output: 25
  • Explanation: The robot starts at (0, 0):
    1. Move north 4 units to (0, 4).
    2. Turn right.
    3. Move east 3 units to (3, 4).
    4. The furthest point the robot ever gets from the origin is (3, 4), which squared is 32 + 42 = 25 units away.

Example 2:

  • Input: commands = [4,-1,4,-2,4], obstacles = [[2,4]]
  • Output: 65
  • Explanation: The robot starts at (0, 0):
    1. Move north 4 units to (0, 4).
    2. Turn right.
    3. Move east 1 unit and get blocked by the obstacle at (2, 4), robot is at (1, 4).
    4. Turn left.
    5. Move north 4 units to (1, 8).
    6. The furthest point the robot ever gets from the origin is (1, 8), which squared is 12 + 82 = 65 units away.

Example 3:

  • Input: commands = [6,-1,-1,6], obstacles = []
  • Output: 36
  • Explanation: The robot starts at (0, 0):
    1. Move north 6 units to (0, 6).
    2. Turn right.
    3. Turn right.
    4. Move south 6 units to (0, 0).
    5. The furthest point the robot ever gets from the origin is (0, 6), which squared is 62 = 36 units away.

Constraints:

  • 1 <= commands.length <= 104
  • commands[i] is either -2, -1, or an integer in the range [1, 9].
  • 0 <= obstacles.length <= 104
  • -3 * 104 <= xi, yi <= 3 * 104
  • The answer is guaranteed to be less than 231

Solution:

We need to simulate the robot's movement on an infinite 2D grid based on a sequence of commands and avoid obstacles if any. The goal is to determine the maximum Euclidean distance squared that the robot reaches from the origin.

Approach

  1. Direction Handling:

    • The robot can face one of four directions: North, East, South, and West.
    • We can represent these directions as vectors:
      • North: (0, 1)
      • East: (1, 0)
      • South: (0, -1)
      • West: (-1, 0)
  2. Turning:

    • A left turn (-2) will shift the direction counterclockwise by 90 degrees.
    • A right turn (-1) will shift the direction clockwise by 90 degrees.
  3. Movement:

    • For each move command, the robot will move in its current direction, one unit at a time. If it encounters an obstacle, it stops moving for that command.
  4. Tracking Obstacles:

    • Convert the obstacles list into a set of tuples for quick lookup, allowing the robot to quickly determine if it will hit an obstacle.
  5. Distance Calculation:

    • Track the maximum distance squared from the origin that the robot reaches during its movements.

Let's implement this solution in PHP: 874. Walking Robot Simulation

<?php
/**
 * @param Integer[] $commands
 * @param Integer[][] $obstacles
 * @return Integer
 */
function robotSim($commands, $obstacles) {
    ...
    ...
    ...
    /**
     * go to ./solution.php
     */
}

// Test cases
echo robotSim([4,-1,3], []) . "\n"; // Output: 25
echo robotSim([4,-1,4,-2,4], [[2,4]]) . "\n"; // Output: 65
echo robotSim([6,-1,-1,6], []) . "\n"; // Output: 36
?>
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Explanation:

  • Direction Management: We use a list of vectors to represent the directions, allowing easy calculation of the next position after moving.
  • Obstacle Detection: By storing obstacles in a set, we achieve O(1) time complexity for checking if a position is blocked by an obstacle.
  • Distance Calculation: We continuously update the maximum squared distance the robot reaches as it moves.

Test Cases

  • The example test cases provided are used to validate the solution:
    • [4,-1,3] with no obstacles should return 25.
    • [4,-1,4,-2,4] with obstacles [[2,4]] should return 65.
    • [6,-1,-1,6] with no obstacles should return 36.

This solution efficiently handles the problem constraints and calculates the maximum distance squared as required.

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