Robot Returns to Origin with Simple Parity Logic

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 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 551 of #750DaysOfCode 🔥 🚧 LeetCode 3661: Maximum Walls Destroyed by Robots (Hard) Today’s problem was a great mix of greedy thinking + binary search + DP optimization 🤯 🧠 Problem Insight We are given: Robots with positions & shooting distances Walls on a number line Each robot can shoot left or right, but: 👉 Bullets stop if they hit another robot 👉 Goal is to maximize unique walls destroyed 💡 Key Challenges Choosing left vs right direction optimally Avoiding overlapping counts of walls Handling blocking by neighboring robots Ensuring maximum unique destruction ⚙️ Approach Breakdown ✅ Step 1: Sort robots & walls 👉 Makes range queries possible ✅ Step 2: Use Binary Search (lowerBound / upperBound) 👉 Efficiently count walls in range ✅ Step 3: Precompute: left[i] → walls destroyed if robot shoots left right[i] → walls destroyed if robot shoots right num[i] → overlapping walls between robots ✅ Step 4: Apply Dynamic Programming Maintain: subLeft → max walls if current robot shoots left subRight → max walls if current robot shoots right 👉 Transition carefully avoids double counting ⏱ Complexity Sorting: O(n log n) Binary Search per robot: O(log n) DP traversal: O(n) 👉 Overall: O(n log n) 🧠 What I Learned How to combine Binary Search + DP Handling interval overlaps smartly Thinking in terms of choices per index (left/right) Writing clean helper functions (lowerBound, upperBound) 📌 Takeaway Hard problems are not about complex code, they’re about breaking constraints into structured decisions. #LeetCode #DataStructures #Algorithms #CodingChallenge #Java #ProblemSolving #100DaysOfCode #SoftwareEngineering #DSA #750DaysOfCode

#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

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✅ Day 93 of 100 Days LeetCode Challenge Problem: 🔹 #657 – Robot Return to Origin 🔗 https://lnkd.in/gfZBi3XR Learning Journey: 🔹 Today’s problem involved tracking movements of a robot on a 2D plane. 🔹 I used a dictionary to map each move to its coordinate change: • 'U' → +1 (y-axis) • 'D' → -1 (y-axis) • 'R' → +1 (x-axis) • 'L' → -1 (x-axis) 🔹 Maintained a coordinate array ans = [0, 0] representing (x, y). 🔹 Iterated through each move and updated the respective axis. 🔹 Finally checked whether the robot returned to the origin [0, 0]. Concepts Used: 🔹 Coordinate Simulation 🔹 HashMap / Dictionary 🔹 String Traversal Key Insight: 🔹 The robot returns to origin only if horizontal and vertical movements cancel out. 🔹 Net displacement in both x and y directions must be zero. Complexity: 🔹 Time: O(n) 🔹 Space: O(1) #LeetCode #Algorithms #DataStructures #CodingInterview #100DaysOfCode #Python #ProblemSolving #LearningInPublic #TechCareers

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

Day 93: Optimization > Completion 📉 Problem 3661: Maximum Walls Destroyed by Robots Today was a massive lesson in efficiency. I initially cleared all test cases with a Memoization + Binary Search approach, but at O(N²), I knew it wasn't the most optimal way to handle the constraints. I decided to dig deeper and refactor the entire logic into a cleaner Dynamic Programming solution. The Strategy Shift: • The O(N²) Trap: My first pass worked, but nested recursion with memoization can get heavy. I wanted to see if I could solve it in a single linear pass. • State Transition: I moved to a DP approach using subLeft and subRight to track the maximum walls destroyed up to each robot. • Precision Boundary Logic: By pre-calculating the left and right firing ranges for each robot using Binary Search (lowerBound/upperBound), I could transition states in O(N) time. I’m honestly not proud of my initial approach today. It felt a bit like forcing a solution rather than finding the most elegant one. Pushing myself to find the O(N) path when I already had a working "Pass" was the real challenge, but it’s where the growth happens. We go again tomorrow. 🚀 #LeetCode #Java #DynamicProgramming #BinarySearch #Algorithms #DailyCode

Excited to finally open-source NervLynx — a modular robotics runtime framework I’ve been building. In my experience working on robotics projects (and the ones I’ve built), most start with quick Python scripts for prototypes. That works great for demos… until you need to move to production. Suddenly you’re fighting non-deterministic behavior, impossible-to-debug data flows, missing observability, and fragile lifecycle management. NervLynx is my attempt to close that exact gap. It’s not a full autonomy stack or a ROS replacement. It’s the runtime foundation that sits underneath your nodes — giving you: Deterministic + async execution with priority scheduling Traceable dataflow (every message carries topic, source, sequence, timestamp, schema, and trace_id) Built-in safety primitives (watchdogs, backpressure detection, startup dependency supervision) Full observability out of the box (replayable JSONL traces, latency/flow stats, Prometheus-style metrics) Config-driven graph wiring via YAML + a clean plugin SDK First-class support for both Python and C++ runtimes Ready-to-use deployment profiles (Docker + systemd) Everything is MIT licensed, thoroughly tested, and ships with working smoke tests and examples so you can try it immediately. I'm currently working on sharing realtime results from actual sensors using NervLynx — and I'll be posting those results soon. If you’re building mobile robot software (perception → planning → actuation pipelines, surveillance stacks, or any hardware-in-the-loop system) and you’ve ever struggled moving from prototype scripts to something maintainable and observable, I’d genuinely love your feedback. 👉 Repo: https://lnkd.in/gGu29zMW #Robotics #OpenSource #RobotSoftware #AutonomousSystems #Embedded #Python #CPP

🚀 Day 36 of 100 Days LeetCode Challenge Problem: Robot Return to Origin Day 36 is a simple but important simulation + counting problem 🔥 💡 Key Insight: To return to origin (0,0): Total moves Right = Left Total moves Up = Down 👉 If both conditions satisfy → ✅ robot returns to origin 🔍 Core Approach: 1️⃣ Initialize Counters x = 0, y = 0 2️⃣ Process Each Move 'R' → x++ 'L' → x-- 'U' → y++ 'D' → y-- 3️⃣ Final Check If (x == 0 && y == 0) → true Else → false 💡 Alternative Approach: Count occurrences: count('R') == count('L') count('U') == count('D') 🔥 What I Learned Today: Simple problems test basic logic clarity Simulation problems are about correct tracking Don’t overcomplicate—keep it clean 📈 Challenge Progress: Day 36/100 ✅ Consistency still going strong! LeetCode, Simulation, Strings, Counting, Coordinates, Arrays, DSA Practice, Coding Challenge, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Simulation #Strings #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep