LeetCode 874 | Walking Robot Simulation | Java | Medium | Array & Simulation | CodeByTushu
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In today’s video, we solve LeetCode 874 — Walking Robot Simulation using Hash Table and Array Simulation in Java.
In this problem, a robot starts at the origin (0, 0) on an infinite 2D plane facing north. It executes an array of integer commands where -2 indicates a 90-degree left turn, -1 indicates a 90-degree right turn, and positive integers between 1 and 9 represent the number of units to move forward one step at a time. The plane also contains obstacles at specified coordinates. If the robot attempts to step onto an obstacle, it stops immediately on the adjacent block and proceeds to the next command. Our goal is to determine the maximum squared Euclidean distance from the origin reached by the robot at any point during its journey.
To solve this efficiently, we encode the four cardinal directions—North, East, South, and West—into directional offset arrays and manage orientation cyclically using modular arithmetic. To achieve constant-time obstacle detection without memory overhead, we hash all obstacle coordinates into a HashSet using 64-bit integer bit-packing. For each forward command, we simulate movement step-by-step, querying the hash set before committing each step. At every valid step, we track and update the maximum squared Euclidean distance x * x + y * y. This ensures a clean O(C * K + M) time complexity and O(M) auxiliary space.
🔥 Problem Highlights
✅ Cyclic direction indexing using modular arithmetic (turn right: +1, turn left: +3)
✅ 64-bit bit-packed coordinate hashing to eliminate object allocation overhead
✅ Unit-by-unit forward movement simulation with immediate collision interruption
✅ Tracking maximum squared Euclidean distance dynamically at every intermediate step
✅ Seamless handling of obstacles located at the origin or along movement paths
✅ Optimal O(C * K + M) time and O(M) auxiliary space complexity
⏱️ TIMESTAMPS
00:00 Intro & Daily Challenge Kickoff (Streak 98 Days)
01:37 Problem Understanding & 2D Obstacle Mechanics
03:42 Key Insights: 64-Bit Coordinate Packing & Modulo 4 Vectors
05:47 Optimal Grid Simulation Algorithm
07:51 Clean Java Solution Walkthrough Line by Line
09:24 Test Cases Dry Run & Benchmark Verification (11 ms, Beats 98.4%)
11:27 Time Complexity Analysis (O(M + C * K) Time)
12:29 Space Complexity Analysis (O(M) Auxiliary Space)
13:16 Day 6 Wrap-up & Channel Resources
📂 Problem Folder & Detailed Documentation:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/tree/main/solutions/April/06-April/874-Walking-Robot-Simulation
☕ Java Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/06-April/874-Walking-Robot-Simulation/Solution.java
🐍 Python Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/06-April/874-Walking-Robot-Simulation/solution.py
⚡ C++ Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/06-April/874-Walking-Robot-Simulation/solution.cpp
🌐 JavaScript Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/06-April/874-Walking-Robot-Simulation/solution.js
🔗 LeetCode Source Code
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LeetCode Medium Problems
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Time Complexity Optimization
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💬 Question of the Day
Why do we update the maximum squared distance at every single unit step rather than only at the end of each command?
1️⃣ The maximum distance might occur at an intermediate step before the robot turns or moves back toward the origin
2️⃣ Stepping one unit at a time resets the robot's coordinates to zero
3️⃣ Intermediate steps require square root calculations
4️⃣ Obstacles only block the robot at the final step of a command
#LeetCode874 #LeetCode #WalkingRobotSimulation #Java #JavaDSA #Array #HashTable #Simulation #DSA #CodingInterview #ProblemSolving #LeetCodeMedium #DailyLeetCode #JavaProgramming #CodeByTushu
👇 Comment the answer below! 🔥 Close description
In today’s video, we solve LeetCode 874 — Walking Robot Simulation using Hash Table and Array Simulation in Java.
In this problem, a robot starts at the origin (0, 0) on an infinite 2D plane facing north. It executes an array of integer commands where -2 indicates a 90-degree left turn, -1 indicates a 90-degree right turn, and positive integers between 1 and 9 represent the number of units to move forward one step at a time. The plane also contains obstacles at specified coordinates. If the robot attempts to step onto an obstacle, it stops immediately on the adjacent block and proceeds to the next command. Our goal is to determine the maximum squared Euclidean distance from the origin reached by the robot at any point during its journey.
To solve this efficiently, we encode the four cardinal directions—North, East, South, and West—into directional offset arrays and manage orientation cyclically using modular arithmetic. To achieve constant-time obstacle detection without memory overhead, we hash all obstacle coordinates into a HashSet using 64-bit integer bit-packing. For each forward command, we simulate movement step-by-step, querying the hash set before committing each step. At every valid step, we track and update the maximum squared Euclidean distance x * x + y * y. This ensures a clean O(C * K + M) time complexity and O(M) auxiliary space.
🔥 Problem Highlights
✅ Cyclic direction indexing using modular arithmetic (turn right: +1, turn left: +3)
✅ 64-bit bit-packed coordinate hashing to eliminate object allocation overhead
✅ Unit-by-unit forward movement simulation with immediate collision interruption
✅ Tracking maximum squared Euclidean distance dynamically at every intermediate step
✅ Seamless handling of obstacles located at the origin or along movement paths
✅ Optimal O(C * K + M) time and O(M) auxiliary space complexity
⏱️ TIMESTAMPS
00:00 Intro & Daily Challenge Kickoff (Streak 98 Days)
01:37 Problem Understanding & 2D Obstacle Mechanics
03:42 Key Insights: 64-Bit Coordinate Packing & Modulo 4 Vectors
05:47 Optimal Grid Simulation Algorithm
07:51 Clean Java Solution Walkthrough Line by Line
09:24 Test Cases Dry Run & Benchmark Verification (11 ms, Beats 98.4%)
11:27 Time Complexity Analysis (O(M + C * K) Time)
12:29 Space Complexity Analysis (O(M) Auxiliary Space)
13:16 Day 6 Wrap-up & Channel Resources
📂 Problem Folder & Detailed Documentation:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/tree/main/solutions/April/06-April/874-Walking-Robot-Simulation
☕ Java Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/06-April/874-Walking-Robot-Simulation/Solution.java
🐍 Python Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/06-April/874-Walking-Robot-Simulation/solution.py
⚡ C++ Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/06-April/874-Walking-Robot-Simulation/solution.cpp
🌐 JavaScript Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/06-April/874-Walking-Robot-Simulation/solution.js
🔗 LeetCode Source Code
📌 Admin type the link
🌐 All LeetCode Solutions
📌 https://codebytushu.com/Leetcode/
📱 Join CodeByTushu on WhatsApp
Get coding updates, LeetCode solutions, daily questions and more.
https://whatsapp.com/channel/0029Vb8k5mCDeON2Xwlkxe40
❤️ Donate
📌 https://codebytushu.com/Leetcode/donate.php
🔥 Perfect For
LeetCode Medium Problems
Java DSA Preparation
Grid & Coordinate Simulation
Hash Set Lookup Techniques
Directional Vector Manipulation
Time Complexity Optimization
Coding Interview Preparation
Problem Solving
🌐 Follow CodeByTushu
▶️ YouTube: https://www.youtube.com/@codebytushu
📸 Instagram: https://instagram.com/codebytushu/
💼 LinkedIn: https://www.linkedin.com/company/codebytushu/
🌍 Website: https://codebytushu.com
🐙 GitHub: https://github.com/CodeByTushu
❤️ Don’t Forget To
👍 Like | 💬 Comment | 🔔 Subscribe
💬 Question of the Day
Why do we update the maximum squared distance at every single unit step rather than only at the end of each command?
1️⃣ The maximum distance might occur at an intermediate step before the robot turns or moves back toward the origin
2️⃣ Stepping one unit at a time resets the robot's coordinates to zero
3️⃣ Intermediate steps require square root calculations
4️⃣ Obstacles only block the robot at the final step of a command
#LeetCode874 #LeetCode #WalkingRobotSimulation #Java #JavaDSA #Array #HashTable #Simulation #DSA #CodingInterview #ProblemSolving #LeetCodeMedium #DailyLeetCode #JavaProgramming #CodeByTushu
👇 Comment the answer below! 🔥 Close description
