Robotics Engineer - Path Planning Specialist

Advait Kinikar

+1-240-***-**** Email LinkedIn GitHub

EDUCATION

University of Maryland, A. James Clark School of Engineering College Park, MD Master of Engineering, Robotics Engineering May 2025 MIT-WPU, Faculty of Engineering, School of Mechanical Engineering Pune, India Bachelor of Technology, Mechanical (Robotics and Automation) Engineering Oct 2022 SKILLS

Programming: Python, C++, Bash

Robotics & Simulation: ROS2, Nav2. Moveit!2, TF2, ros2_control, Gazebo, Isaac Sim Robot Fundamentals & Path Planning: Forward Kinematics, Inverse Kinematics, Newton-Euler/Lagrangian Dynamics, Grid Based Path Planning (A*, Dijkstra), RRT, RRT-Connect, Probabilistic Road Maps (PRMs), Artificial Potential Fields (APF), Path Optimization Tools & Frameworks: Version Control (Git), Docker, OpenCV, NumPy, Matplotlib, Linux, SolidWorks Networking: MQTT(basic familiarity), Modbus (basic familiarity) PROJECTS

Path Planning using Improved RRT-Connect Algorithm (GitHub) NumPy Shapely Matplotlib SciPy Python

• Implemented bidirectional RRT-Connect with Artificial Potential Field guidance, where attractive/repulsive forces bias tree growth toward goals while avoiding obstacles, replacing blind random exploration

• Added Dijkstra-based path optimization to remove redundant waypoints, followed by cubic B-spline smoothing for continuous, executable trajectories

• Achieved a 90% reduction in computation time (from 4.79s to 0.54s) and generated paths 10-15% shorter, enhancing obstacle avoidance and path efficiency.

Autonomous Path Planning for Turtlebot3 using A* (GitHub) ROS2 Gazebo NumPy OpenCV Python

• Implemented A* search with differential-drive kinematics; action space of 8 wheel-RPM combinations respecting physical constraints of the TurtleBot3 platform

• Used weighted heuristic (5 Euclidean) to trade optimality for planning speed, achieving <200ms latency with 95% success rate in a custom Gazebo environment

• Integrated planner output with ROS2 by converting planned RPM actions to Twist velocity commands, publishing to

/cmd_vel at timed intervals for real-time trajectory execution in Gazebo simulation Industrial Arm Pick and Place Simulation (GitHub) SolidWorks ROS2 Gazebo NumPy SW2URDF Python

• Designed a UR10e-inspired 6-DOF manipulator in SolidWorks and exported to URDF for Gazebo simulation

• Implemented forward (DH method)/inverse (Jacobian based numerical method) kinematics computing joint velocities from end effector velocities in Python with NumPy.

• Demonstrated autonomous pick-and-place sequence with vacuum gripper activation, conveyor belt integration, and position- controlled joint trajectories through ros2_control EXPERIENCE

Kick Robotics LLC College Park, MD

Robotics Engineer ROS2 Python C++ Nav2 Motion Planning Path Planning Present

• Built a ROS2 node interfacing with RC transceiver over serial, parsing 4-channel control data with dead-zone filtering and publishing Twist commands at 20Hz for manual control of a 4-wheeled AMR. Implemented manual/autonomous mode switch enabling operators to override Nav2 navigation in real time — safety-critical for field testing

• Wrote multi-waypoint navigation scripts using Nav2 Basic Navigator action API, handling goal lifecycle (send, feedback, timeout, result) across office and warehouse environments

• Diagnosed USB device recognition failures on Jetson after motor controller reconfiguration. Traced broken udev symlinks with udevadm, updated device rules to restore sensor-to-compute communication

• Performed field testing and hands-on debugging of mobile robots. Supported hardware installations and creating demos. Did some field work like generating a map of the environment using SLAM

• Worked within a Nav2-based autonomy stack (AMCL, EKF, costmaps, DWB controller) on an indoor AMR with RPLIDAR S2, BNO055 IMU, and OAK-D stereo camera

DesignTech Systems Pvt. Ltd Pune, India

Robotics Design Intern Fusion360 ROS2 Gazebo Python Dec 2021 – March 2022

• Designed and optimized a payload-carrying quadcopter frame in Autodesk Fusion 360; performed structural and modal analysis for real-world conditions.

• Compared CFRP, GFRP, and aluminum using FEA to validate stress, displacement, and structural integrity.

• Reduced frame weight by 47% via topology optimization while preserving stiffness and load capacity.

• Modeled IP54-rated electronics enclosure with DFM principles and integration-ready layout.

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