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Rahul Kistaiahgari
Entry-level Robotics & Autonomous Systems Engineer specializing in autonomy, simulation, and ML
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About
Robotics software candidate who built a Q-learning smart delivery drone navigation system, focusing on 2D path planning with dynamic obstacle avoidance using reward shaping and real-time sensor feedback. Actively learning ROS 2 by building Python simulation projects with publisher/subscriber patterns and has experience coordinating multi-agent drone simulations via message passing.
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Key Strengths
- Built Q-learning-based drone navigation system end-to-end (environment + agent)
- Reward shaping and real-time sensor feedback integration for dynamic obstacle avoidance
- Iterative optimization via simulation (tuning learning rate and exploration strategy) to improve convergence
- Debugged and improved navigation efficiency by analyzing reward function and convergence behavior
- Implemented multi-agent coordination using Python message passing/shared data structures
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