Robot Return to Origin LeetCode Challenge Day 36

🚀 Day 4 of Problem Solving Journey Today, I tackled LeetCode 3661 – Maximum Walls Destroyed by Robots 🤖💥 🔍 Problem Summary: Given positions of robots and walls on a straight line, each robot can shoot once either left or right within a limited distance. The challenge is to calculate the maximum number of unique walls that can be destroyed without bullets passing through other robots. 🧠 Key Learnings: Sorting helps simplify positional problems Binary search can optimize finding valid ranges Careful handling of edge cases (like robots blocking bullets) Efficient mapping of robot positions to distances ⚡ Approach: Used sorting + binary search to determine reachable walls for each robot and ensured no overlap or blockage affects the final count. ✅ Result: Accepted ✔️ 📌 Consistency is the key — one problem a day keeps improving problem-solving skills! #Day4 #LeetCode #ProblemSolving #Java #CodingJourney #DataStructures #Algorithms #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

🔥 Day 554 of #750DaysofCode 🔥 🚀 Problem Solved: Walking Robot Simulation Today’s problem tested my simulation + hashing skills in a really interesting way! 🤖 What I implemented: I simulated the robot’s movement step-by-step on an infinite grid while handling obstacles efficiently. 💡 Instead of using complex structures, I used: 👉 HashSet to store blocked positions 👉 Direction array to manage movement (N, E, S, W) 🧠 Core Idea: Maintain current direction using an index Rotate: Right → (dir + 1) % 4 Left → (dir + 3) % 4 Move one step at a time (very important 🚨) Before every step: Check if next position is blocked If yes → stop moving for that command ⚡ Key Insight: 👉 You cannot jump directly k steps 👉 You MUST move step-by-step to correctly detect obstacles 💻 My Approach: ✔️ Stored obstacles as "x,y" in HashSet ✔️ Used direction vector: North → (0,1) East → (1,0) South → (0,-1) West → (-1,0) ✔️ Tracked max distance using: 👉 x² + y² 📈 Complexity: Time: O(N + total steps) Space: O(M) for obstacles 🎯 What I learned: Simulation problems require careful step execution Hashing makes obstacle lookup super fast Small implementation details can make or break the solution 💬 Honestly, this problem looks easy at first, but handling directions + obstacles correctly makes it a great practice problem! #Day554 #750DaysOfCode #LeetCode #Java #DSA #CodingJourney #ProblemSolving #Developers #Tech

Just solved LeetCode 657: Robot Return to Origin and it’s a great reminder that clean, fundamental thinking often beats over-engineering. The Problem: Given a string of moves (U, D, L, R), determine if a robot starting at (0,0) returns to the origin after executing all commands. My Approach: Coordinate Tracking Simulation Instead of reaching for complex data structures, I simply tracked position: • x → horizontal (R = +1, L = -1) • y → vertical (U = +1, D = -1) • Return true if x == 0 && y == 0 Time: O(n) | Space: O(1) Key Insight: Opposite moves cancel each other out. If count(U) == count(D) and count(L) == count(R), the robot must return home. Sometimes a quick count beats step-by-step simulation! This pattern (coordinate tracking + cancellation logic) is foundational for grid problems, path simulations, and even early-stage BFS/DFS intuition. Mastering the basics makes medium problems feel much more approachable. #LeetCode #Algorithms #CodingInterview #SoftwareEngineering #Python #ProblemSolving #TechCareers #DataStructures

🚀 Day 214 of #300DaysOfCoding Today I solved a Hard-level problem on LeetCode: 👉 Minimum Total Distance Traveled This problem really tested my understanding of Dynamic Programming + Greedy Thinking. 💡 Key Learnings: Sorting plays a crucial role in optimizing 1D distance problems Converting real-world constraints into DP states makes complex problems manageable Handling capacity constraints (factories limit) using iterative assignment 🧠 Approach: Sorted robots and factories based on position Used DP + Memoization to assign robots to factories efficiently Tried multiple assignments per factory within capacity limits ⚡ Why this problem is interesting? It combines: Greedy intuition (nearest assignment) Dynamic Programming (optimal substructure) Real-world modeling (capacity constraints) 📊 Complexity: Time: O(n × m × limit) Space: O(n × m) 🔥 Problems like this improve problem-solving depth and prepare you for top product-based companies. #LeetCode #DSA #DynamicProgramming #CodingJourney #ProblemSolving #SoftwareEngineering #MCA #GATEPreparation

🔥 Day 549 of #750DaysOfCode 🔥 🚀 Solved: Robot Collisions (Hard) Today’s problem was a really interesting mix of sorting + stack + simulation — exactly the kind of question that tests real problem-solving skills 💡 🧠 Problem Insight We are given robots with: Positions 📍 Health ❤️ Directions (L / R) When two robots collide: The one with lower health ❌ gets destroyed The stronger one loses 1 health and continues If equal → both destroyed ⚡ Key Idea 👉 Collisions only happen when: A robot moving Right (R) meets one moving Left (L) So the approach becomes: Sort robots by position Use a stack to track right-moving robots Simulate collisions when a left-moving robot appears 🛠️ Approach ✔ Sort indices based on positions ✔ Use stack for R robots ✔ Resolve collisions with L robots ✔ Track alive robots and update health dynamically ⏱️ Complexity Time: O(n log n) (sorting) Space: O(n) 🎯 Key Learnings ✅ Stack is powerful for collision-type problems ✅ Sorting helps simulate real-world order ✅ Only R vs L interactions matter ✅ Clean handling of edge cases is crucial This problem really improved my understanding of simulation problems in DSA 💪 On to Day 550 🚀 #LeetCode #DSA #Java #CodingJourney #ProblemSolving #SoftwareEngineering #Stack #Algorithms #750DaysOfCode

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

#100DaysOfLeetcode journey 🚀 Day 58/100 — Optimizing Perpetual Motion! Today’s Problem: 2069. Walking Robot Simulation II 🔹 The Goal: Design a robot that traverses the perimeter of a grid. It moves forward until it hits a wall, then turns 90 degrees counter-clockwise. We need to support millions of steps and instantly return the robot's $(x, y)$ coordinates and direction. 🔹 The Insight: A naive simulation would take $O(\text{steps})$, which fails for large inputs. The key is to realize the robot's path is a Fixed Cycle. By calculating the perimeter length and using the modulo operator, we can reduce any number of steps into a single coordinate on the boundary. 🔹 The Logic: Modulo Striding: I used pos = (pos + num) % loopSize to handle infinite movement in constant time. Perimeter Segmentation: I mapped the 1D "distance traveled" into 2D coordinates by dividing the perimeter into four segments (Bottom, Right, Top, Left). The "Facing" Trap: The trickiest part is the direction. If the robot lands on $(0,0)$ after moving, it’s not facing "East" anymore—it’s facing "South" because it technically hit the boundary and turned before stopping. ✨ Achievement: Day 58! Successfully transformed a simulation problem into a coordinate geometry and modular arithmetic challenge. Crossing the 58% mark by mastering "cycle-based" optimizations is a great feeling. 🔍 Steps followed: ✔ Cycle Length Derivation: Mathematically defined the unique cells on the grid boundary. ✔ Constant-Time Positioning: Implemented $O(1)$ jumps to any future state. ✔ State-Aware Logic: Handled the edge cases of initial vs. post-movement orientation at the origin. 🔧 Complexity Analysis: Time Complexity: $O(1)$ per operation. Space Complexity: $O(1)$. 58 days down! The logic is getting cleaner, and the "Hard" constraints are becoming much more manageable with the right math. 🛠️ #Java #DSA #LeetCode #CodingJourney #100DaysOfCode #SoftwareEngineer #InternshipBound #Programming #Simulation #Geometry #Optimization #Day58

🚀 My Coding Journey — Only 90 Days to Go! Today’s problem was a really interesting one involving robots, walls, and constrained shooting ranges — and it turned out to be a great exercise in greedy thinking + binary search. 💡 Key Learning: At first glance, it feels like a simulation problem. But the real trick is: Each robot can shoot either left or right (not both) Its range is limited not just by distance, but also by neighboring robots blocking the shot This naturally forms independent intervals 🔍 Approach I Used: Sort robots by position For each robot: Compute valid shooting range (considering blocking robots) Count walls on left and right using binary search Choose the direction that destroys more walls ⚡ Big Insight: Because robots block each other, their decisions become independent, which allows a greedy solution instead of complex DP. ⏱️ Time Complexity: O((n + m) log m) 🎯 Takeaway: Sometimes problems that look like simulation can be reduced to: 👉 interval problems 👉 greedy decisions 👉 efficient counting (binary search) 📉 Challenges like this remind me how important it is to: Break constraints carefully Translate real-world rules into clean intervals 🔥 90 days remaining in this journey. Consistency > Motivation. Let’s keep going. #DSA #CodingJourney #LeetCode #Greedy #BinarySearch #ProblemSolving #100DaysOfCode

#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