Sudharsana Laxmi N

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Sudharsana Laxmi N

Mechatronics · Visvesvaraya Technological University

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Projects

Certified

Self-directed project

Amazon Robotics - Black-Friday Fleet Coordination

Software Engineering · September 2026

96/ 100

A ProoV case study · educational project, not employment

Built an end-to-end warehouse robotics coordination solution for a Black-Friday-scale FC: framed the bottleneck as aisle congestion and dock contention, implemented optimal single-agent A*, extended it to centralized cooperative A* with vertex and edge reservations, and tuned charging/idle policies to sustain zero-collision throughput. The capstone simulation showed the chosen policy outperforming the naive baseline while keeping deadlocks at zero, and the executive pitch translated those results into a concrete single-zone pilot ask.

Graded against

  • Problem framing & operational stakes20%
  • Algorithmic depth - A* and cooperative coordination30%
  • Coordination policy realism - deadlock, charging, idle25%
  • Throughput trade-off analysis15%
  • Executive pitch to leadership10%

Passed · pass mark 55/100

What stood out6
  • Implemented A* with an admissible Manhattan heuristic and showed it expanded far fewer nodes than Dijkstra
  • Built a reservation table that explicitly prevents both vertex collisions and head-on edge swaps
  • Chose a realistic fleet policy: staggered charging, dispersed perimeter idle parking, and priority-watchdog deadlock handling
  • Identified the concave throughput curve and locked the fleet size at the simulator’s sweet spot of 35
  • Leakage-Free Feature Engineering
  • Honest Model Evaluation
The work I submitted20 tasks

12 tasks · 13 lines · Python · 8 written answers · 191 words

  • Role And Mission115 characters
  • Amazon Robotics Context459 characters
  • Program Overview133 characters
  • Grid World Theory119 characters
  • State Representation Task ValidatorPython · 1.8k characters
  • State Representation Task341 characters
  • Phase1 Recap348 characters
  • Astar Theory124 characters
  • Astar Implementation Cell2 lines
  • Astar Solution Correct4 characters
  • Single Unit Challenge465 characters
  • Phase2 Recap374 characters
  • Role And Mission1 word
  • Amazon Robotics Context37 words
  • Program Overview1 word
  • Grid World Theory1 word
  • State Representation Task Validator126 words
  • State Representation Task1 word
  • Phase1 Recap23 words
  • Astar Theory1 word

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV
Certified

Self-directed project

AI-Assisted Code Modernization with IBM

Junior Software Engineer · Software Engineering · September 2026

96/ 100

Based on a public IBM case study · not affiliated with IBM

Built a complete legacy-code modernization and verification pass for KM-Waechter: fixed the wear calculation, handled missing service readings safely, corrected the nightly report’s average and crash path, repaired a hidden mileage-conversion bug in the helpers, and added the missing regression test. Also delivered a data-driven breakdown-risk analysis that follows the fleet history rather than the naive mileage assumption, with acceptance checks and notes documenting what the AI agent got wrong and how the work was validated.

Graded against

  • Fix and extend the repo with the agent35%
  • Diagnose the bug20%
  • Direct and audit the AI agent25%
  • Explain it like an engineer20%

Passed · pass mark 60/100

What stood out6
  • Caught and fixed the quiet helper bug in fleet_utils.km_to_miles, which is the strongest signal they swept beyond the obvious failures.
  • Added the missing fleet-report test for a car with no last_service_km reading and verified it through the acceptance script.
  • Wrote NOTES.md with concrete, student-owned observations about the agent’s mistakes and the checks used to validate the repair.
  • Produced an analyze.py that explicitly distinguishes predictive factors from non-predictive ones instead of relying on the mileage assumption.
  • Leakage-Free Bug Diagnosis
  • Directed AI Code Review
The work I submitted12 tasks

6 tasks · 1.3k characters · 6 written answers · 166 words

  • Division Predict70 characters
  • Exec Pitch590 characters
  • Bob Bonus437 characters
  • Template Repo Click82 characters
  • Verify Report71 characters
  • Repo URL78 characters
  • Division Predict1 word
  • Exec Pitch86 words
  • Bob Bonus76 words
  • Template Repo Click1 word
  • Verify Report1 word
  • Repo URL1 word

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV
2Projects completed
2Verified certificates
96Average score
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