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How to Solve Walking Robot Simulation Problem
Master the Walking Robot Simulation LeetCode problem with undetectable real-time assistance. Get instant solutions and explanations during your coding interviews.
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Start Free for WindowsMedium#874
LeetCode ProblemWalking Robot Simulation
A robot on an infinite XY-plane starts at point (0, 0) facing north. The robot receives an array of integers commands, which represents a sequence of moves that it needs to execute. There are only three possible types of instructions the robot can receive: 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, it will stay in its current location (on the block adjacent to the obstacle) and move onto the next command. Return the maximum squared Euclidean distance that the robot reaches at any point in its path (i.e. if the distance is 5, return 25).
ArrayHash TableSimulation
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Problem Breakdown
Understanding the Walking Robot Simulation Problem
Let's break down this LeetCode problem and understand what makes it challenging in interview settings.
Problem Statement
A robot on an infinite XY-plane starts at point (0, 0) facing north. The robot receives an array of integers commands, which represents a sequence of moves that it needs to execute. There are only three possible types of instructions the robot can receive: 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, it will stay in its current location (on the block adjacent to the obstacle) and move onto the next command. Return the maximum squared Euclidean distance that the robot reaches at any point in its path (i.e. if the distance is 5, return 25).
MediumProblem #874
LeetCodeWalking Robot Simulation
Related Topics
ArrayHash TableSimulation
How Phantom Code Helps
Get real-time assistance for Walking Robot Simulation problems during coding interviews. Phantom Code provides instant solutions and explanations.
Examples
# Example 1
Input
commands = [4,-1,3], obstacles = []
Output
25
# Example 2
Input
commands = [4,-1,4,-2,4], obstacles = [[2,4]]
Output
65
# Example 3
Input
commands = [6,-1,-1,6], obstacles = [[0,0]]
Output
36
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.
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Solve Walking Robot Simulation — A robot on an infinite XY-plane starts at point (0, 0) facing north. The robot r...
Here's the optimal approach using Array:
def solve(input):
# Optimal O(n) solution
return result
Time: O(n) | Space: O(n)
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Frequently Asked Questions
Common questions about solving Walking Robot Simulation and using Phantom Code during coding interviews.
How does Phantom Code help me solve coding problems like Walking Robot Simulation during interviews?
Phantom Code generates complete solutions instantly with proper complexity analysis, letting you focus on explaining your approach and demonstrating problem-solving skills rather than getting stuck on implementation details during high-pressure situations.
What if the interviewer asks follow-up questions or modifications to problems like Walking Robot Simulation?
Phantom Code adapts in real-time. Screenshot the modified problem or use audio mode to capture the interviewer's follow-up, and get an updated solution within seconds — including edge cases and optimizations.
Can Phantom Code help me communicate my solution for problems like Walking Robot Simulation?
Yes. Phantom Code provides step-by-step approach explanations with 3 progressive thoughts, so you can walk through your solution naturally. It also provides time and space complexity analysis to discuss trade-offs.
How does Phantom Code assist with different programming languages for Walking Robot Simulation type problems?
Phantom Code supports 11 programming languages including Python, Java, C++, JavaScript, TypeScript, Go, Rust, Ruby, Swift, Kotlin, and C#. Set your preferred language and get idiomatic solutions.
What coding mistakes does Phantom Code help me avoid during Walking Robot Simulation interviews?
Phantom Code catches common mistakes like off-by-one errors, missing edge cases, incorrect base conditions, and suboptimal approaches. It provides clean, well-structured code that handles all edge cases.
Is Phantom Code detectable when solving problems like Walking Robot Simulation during live interviews?
No. Phantom Code runs as an invisible desktop overlay that doesn't appear in screen shares, recordings, or proctoring software. It works with Zoom, HackerRank, CodeSignal, CoderPad, and all major platforms.
How does Phantom Code help when I'm struggling with Walking Robot Simulation style questions under pressure?
The AI provides structured guidance: first the approach (what algorithm/data structure to use and why), then the implementation with clean code, and finally complexity analysis. This helps you think clearly even under pressure.
Does Phantom Code work for the coding platforms used in Walking Robot Simulation interviews?
Yes. Phantom Code works with all major coding platforms including LeetCode, HackerRank, CodeSignal, CoderPad, HackerEarth, and any browser-based coding environment.
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