Robot Simulation on Grid Perimeter

Day 96: Navigating the Obstacle Course 🤖🚧 Problem 874: Walking Robot Simulation Yesterday was a simple back-to-origin check, but today the robot had to navigate a field of obstacles while maximizing its Euclidean distance from the starting point. The Strategy: • Obstacle Lookup: I used a HashSet to store the obstacle coordinates as strings ("x,y"). This turned a potentially slow search into an O(1) lookup for every step the robot took. • Directional Logic: Instead of complex if-else blocks, I used a dirs array {{0, 1}, {1, 0}, {0, -1}, {-1, 0}} to represent North, East, South, and West. • The Turns: ∘ Turning right (-1) is just (d + 1) % 4. ∘ Turning left (-2) is (d + 3) % 4 (equivalent to turning right three times). • Distance Tracking: At every single step, I calculated the squared Euclidean distance x² +y² and kept track of the maximum value reached throughout the entire simulation. It’s all about the state management—tracking position, direction, and obstacles simultaneously. Another day of consistent problem-solving in the books. 🚀 #LeetCode #Java #Algorithms #Simulation #DataStructures #DailyCode

LeetCode Daily | Day 71 🔥 LeetCode POTD – 2069. Walking Robot Simulation II (Medium) ✨ 📌 Problem Insight Given a robot on a bounded grid: ✔ Starts at (0, 0), facing East ✔ Moves along grid edges ✔ Turns counter-clockwise when hitting boundary ✔ Need to track position + direction after steps 🔍 Initial Thinking – Simulation ❌ 💡 Idea: ✔ Move step-by-step ✔ Handle boundary checks ✔ Update direction dynamically ⏱ Problem: ❌ Can be inefficient for large steps ❌ Too many edge cases 💡 Optimized Approach – Boundary Precomputation 🔥 👉 Key Idea: ✔ Robot always moves on rectangle boundary ✔ Precompute full path once ✔ Store: • All boundary positions • Direction at each step ✔ Then movement becomes: • idx = (idx + steps) % cycle ⚡ Why this is powerful ✔ Converts simulation → array indexing ✔ No step-by-step movement needed ✔ Handles edge cases automatically ⏱ Complexity: ✔ Precompute: O(perimeter) (~400 max) ✔ step(): O(1) ✅ ✔ getPos / getDir: O(1) 🧠 Key Learning ✔ Identify cycle / pattern in problems ✔ Precomputation can eliminate simulation ✔ Mapping states → indices simplifies logic ✔ Small trick (dir[0] = South) handles full-cycle edge case 🚀 Takeaway A great example of turning a simulation problem into a math/indexing problem — very useful for interviews ⚡ #LeetCode #DSA #Algorithms #CPlusPlus #ProblemSolving #CodingJourney #DataStructures

Day 96 Today’s problem: Walking Robot Simulation (Medium) 🤖 This one was all about simulation + direction handling + efficient obstacle lookup. At first glance it feels straightforward, but handling turns and movement cleanly is where most mistakes happen. 🔑 Key Learnings: Use a direction vector to simplify movement (instead of messy if-else logic). Store obstacles in a set for O(1) lookup — huge optimization. Carefully handle left (-2) and right (-1) turns using modular arithmetic. Track maximum Euclidean distance at every step. 💡 What I Improved Today: I focused on writing clean and scalable simulation logic instead of brute forcing movement blindly. Small design choices made the solution much smoother. ✅ Result: Accepted ⚡ Runtime: 26 ms 📊 Beat ~53% in runtime & ~78% in memory Consistency is starting to pay off. Every day I’m getting better at breaking problems into patterns. #Day96 #LeetCode #CodingJourney #DataStructures #Algorithms #100DaysOfCode

LeetCode Daily | Day 70 🔥 LeetCode POTD – 874. Walking Robot Simulation (Medium) ✨ 📌 Problem Insight Given a robot on an infinite grid: ✔ Starts at (0, 0), facing North ✔ Commands include: • -2 → turn left • -1 → turn right • k → move forward k steps ✔ Obstacles block movement ✔ Find max distance² from origin reached 🔍 Initial Thinking – Simulation + Directions ✔ 💡 Idea: ✔ Maintain current position (x, y) ✔ Track direction using index (N, E, S, W) ✔ Use dx, dy arrays for movement ✔ Store obstacles in a set for fast lookup ✔ Move step-by-step (important!) • Before each step → check obstacle • If blocked → stop current command ⏱ Complexity: O(n + steps), Space: O(n) ✔ Efficient due to hashing ✔ Each move handled once 💡 Key Optimization – Hashing Obstacles 🔥 👉 Use set / unordered_set ✔ O(1) obstacle check ✔ Encode (x, y) → faster lookup possible 🧠 Key Learning ✔ Simulation problems need careful handling of edge cases ✔ Direction control using modulo is very useful ✔ Step-by-step movement avoids logical bugs ✔ Hashing turns brute-force into efficient solution A great mix of simulation + hashing — classic interview pattern ⚡ #LeetCode #DSA #Algorithms #CPlusPlus #ProblemSolving #CodingJourney #DataStructures

#Day30 of DSA Journey | LeetCode 874 - Walking Robot Simulation Today’s problem was all about simulation + direction handling + hashing — a very practical and fun one! Problem Summary: A robot starts at (0,0) facing North and follows a sequence of commands: -2 → Turn Left -1 → Turn Right 1 to 9 → Move forward step-by-step There are obstacles on the grid, and the robot must stop if it encounters one. Goal: Find the maximum squared distance from the origin reached at any point. Key Learnings: Use direction vectors to simplify movement North → (0,1) East → (1,0) South → (0,-1) West → (-1,0) Store obstacles in a HashSet for O(1) lookup Convert (x, y) → string "x,y" or use a pair hash Move step-by-step (not directly k steps!) to handle obstacles correctly Track max distance using: maxDist = max(maxDist, x*x + y*y) Approach: Initialize direction = North Process each command Rotate left/right using modular arithmetic Move step-by-step and stop if obstacle found Update max distance at each step Time Complexity: O(N * K) → N commands, each moving up to K steps Space Complexity: O(M) → for storing obstacles Takeaway: This problem strengthens your understanding of: Simulation problems Direction handling Efficient lookup using hashing #DSA #LeetCode #CodingJourney #JavaScript #ProblemSolving #100DaysOfCode

Day 97 ✅ Problem Solved: Walking Robot Simulation II 🔹 Difficulty: Medium Today’s problem was a great mix of simulation + optimization. At first glance, it looks like a simple movement problem, but the challenge is handling large step counts efficiently without simulating every single move. 💡 Key Learnings: Instead of brute force, reduce steps using the perimeter cycle of the grid Careful handling of direction changes at boundaries Edge cases matter — especially when steps % cycle == 0 Clean state tracking (position + direction) makes everything manageable ⚡ Optimization Insight: Rather than iterating step-by-step, we leverage the fact that the robot moves in a loop: 👉 Cycle = 2 * (width - 1) + 2 * (height - 1) This drastically improves performance and avoids TLE 🚫 📊 Result: ✔️ All test cases passed ⚡ Runtime: 56 ms 💾 Memory: 127.24 MB Consistency is starting to pay off. Each day, I’m getting better at spotting patterns and optimizing solutions. #LeetCode #Day97 #CodingJourney #ProblemSolving #DataStructures #Algorithms #100DaysOfCode

Day 7/30 – LeetCode Challenge Today’s problem: Robot Return to Origin (657) Problem Overview A robot starts from the origin (0, 0) and moves according to the given string. Each character represents a move: - 'U' → up - 'D' → down - 'L' → left - 'R' → right We need to determine whether the robot comes back to the origin after completing all the moves. Approach I solved this using simple simulation. - maintain `x` for left/right movement - maintain `y` for up/down movement - update the coordinates for each move Finally: - if both x and y become 0, the robot has returned to the origin - otherwise, it has not Why this works For the robot to return to the starting point: - upward and downward moves must balance - left and right moves must balance So checking the final coordinates is enough. Complexitys - Time Complexity: O(n) - Space Complexity: O(1) Key Learning Not every problem needs an advanced approach. Some problems are just about clean observation and correct simulation. #LeetCode #Day7 #DSA #ProblemSolving #CPP #CodingJourney

🚀 #LeetCode Day Problem Solving 🚀 Day-20 📌 Problem: A robot starts at position (0, 0) on a 2D plane. You are given a string moves representing its movement sequence. Each character in the string denotes a move: ➡ 'R' → Right ⬅ 'L' → Left ⬆ 'U' → Up ⬇ 'D' → Down 👉 Your task is to determine whether the robot returns to the origin (0, 0) after completing all moves. Return true if it returns to origin, otherwise false. 🧠 Example 1: Input: moves = "UD" ✅ Output: true 📖 Explanation: Move Up → (0, 1) Move Down → (0, 0) ✔ Robot returns to origin 🧠 Example 2: Input: moves = "LL" ❌ Output: false 📖 Explanation: Move Left → (-1, 0) Move Left → (-2, 0) ❌ Robot does not return to origin 💡 Key Insight: ✔ Count movements: Number of 'U' should equal 'D' Number of 'L' should equal 'R' ✔ OR simulate coordinates: Track (x, y) position 📊 Complexity Analysis: ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) 🧠 What I Learned: ✔ Simple simulation problems can be solved efficiently ✔ Importance of balancing movements ✔ Good practice for coordinate tracking ✅ Day 20 Completed 🚀 Keep going strong with DSA & consistency 💪 #Leetcode #DSA #ProblemSolving #BitManipulation #CodingJourney #InterviewPreparation #Consistency

🚀 Day 37 of 100 Days LeetCode Challenge Problem: Walking Robot Simulation Day 37 builds on previous robot problems with a full simulation + direction handling + hashing challenge 🔥 💡 Key Insight: We simulate movement step-by-step, but: 👉 Need to handle: Direction changes Obstacles Track maximum distance 🔍 Core Approach: 1️⃣ Track Direction Use directions array: North → (0,1) East → (1,0) South → (0,-1) West → (-1,0) 👉 Rotate index: Left → (dir + 3) % 4 Right → (dir + 1) % 4 2️⃣ Store Obstacles Efficiently Use a HashSet for O(1) lookup 3️⃣ Simulate Movement For each step: Move 1 unit at a time If next cell is obstacle → stop movement 4️⃣ Track Maximum Distance At every step: Compute x² + y² Keep max value 🔥 What I Learned Today: Simulation problems require step-by-step precision Hashing is essential for fast obstacle checks Direction handling patterns are reusable 📈 Challenge Progress: Day 37/100 ✅ Consistency unstoppable! LeetCode, Simulation, Hashing, Matrix, Direction Handling, Arrays, DSA Practice, Coding Challenge, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Simulation #Hashing #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

#day63 🚀 19/04/26 — Precision Tracking: Robot Return to Origin Today, I solved Robot Return to Origin (LeetCode 657), applying coordinate-based tracking to determine if a sequence of moves returns a robot to its starting position. This problem is a great exercise in Simulation and state management using simple array counters. 🤖 The Origin Logic The objective is to determine if a robot, starting at (0,0) on a 2D plane, returns to (0,0) after executing a string of moves: 'U' (up), 'D' (down), 'L' (left), and 'R' (right). The Logic: Coordinate Simulation State Representation: I used a small integer array arr of size 2 to represent the (x, y) coordinates, initialized at {0, 0}. arr[0] tracks horizontal movement (x-axis). arr[1] tracks vertical movement (y-axis). Linear Execution: I iterated through the moves string exactly once. Vertical: Increment arr[1] for 'U' and decrement for 'D'. Horizontal: Increment arr[0] for 'R' and decrement for 'L'. Verification: The robot returns to the origin if and only if both final coordinates are zero (arr[0] == 0 && arr[1] == 0). Complexity: Time: O(n) where n is the length of the moves string. Space: O(1) as we only use a fixed-size array regardless of the number of moves. 📈 Consistency Report: Foundations of Simulation Today's solution emphasizes that complex tracking problems can often be reduced to basic arithmetic operations on a fixed state. This is a more streamlined version of the Two-Pointer and Sliding Window techniques I've been mastering—while those patterns focus on sub-segments of data, this simulation tracks the global state of an object over time. Huge thanks to the roadmap for building this momentum! Moving from array partitioning to 2D state simulation shows how my problem-solving intuition is expanding. My O(n) coordinate tracking implementation is attached below! 📄👇 #DSA #Java #LeetCode #Simulation #Optimization #Complexity #63DayStreak #LearningInPublic