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About the Role
Develop and deploy algorithms for autonomous guided forklifts to plan, navigate, and move safely and accurately in real-world environments. The ideal candidate has strong hands-on experience in path/motion planning, trajectory generation, trajectory tracking, and advanced control, with proven experience implementing and deploying MPC or equivalent controllers on real robots. Experience with forklifts, AMRs, AGVs, autonomous vehicles, or warehouse robots is highly desirable.
Responsibilities
- Design, implement, and deploy global/local path planning and motion planning algorithms.
- Develop trajectory generation and optimization considering robot kinematic and dynamic constraints.
- Design, implement, and tune MPC or other advanced motion/trajectory controllers.
- Develop robust trajectory tracking and path-following for real-world robots.
- Implement obstacle avoidance, collision checking, and recovery behaviors.
- Handle constraints including turning radius, steering, velocity, acceleration, angular velocity, robot footprint, and actuator limits.
- Integrate planning and control with SLAM/localization, perception, odometry, and vehicle control systems.
- Develop and maintain navigation software using ROS/ROS2.
- Test and validate algorithms in simulation and on physical robots.
- Diagnose issues such as path deviation, oscillations, unstable motion, poor tracking, deadlocks, and inefficient trajectories.
- Optimize algorithms for accuracy, robustness, and real-time performance.
Requirements
- Bachelor's/Master's degree in Robotics, Computer Science, Electrical/Mechanical Engineering, or related field.
- 3+ years of hands-on experience in motion planning, navigation, or control for mobile robots/autonomous systems.
- Strong understanding of path planning, motion planning, trajectory generation, and trajectory tracking.
- Proven hands-on experience implementing and deploying MPC or equivalent advanced motion controllers on real robots.
- Strong understanding of robot kinematics, coordinate transformations, and non-holonomic motion models.
- Experience with control approaches such as MPC, LQR, nonlinear control, Pure Pursuit, Stanley, or equivalent.
- Strong understanding of global/local planners, obstacle avoidance, and collision checking.
- Strong C++ skills; Python is a plus.
- Strong hands-on experience with ROS/ROS2, with ROS2 preferred.
- Experience integrating planning and control with perception, localization, and odometry.
- Experience debugging robotics systems using RViz/RViz2, logs, simulation, and real-robot testing.
- Strong Linux and Git experience.
- Ability to take algorithms from concept → implementation → simulation → real-world deployment.
- Understands how planning and control work under the hood, not just integrating existing navigation packages.
- Able to diagnose root causes of robot navigation issues (planning, trajectory generation, kinematic constraints, controller design/tuning, localization, or actuation).
- Comfortable modifying, optimizing, or developing planning and control algorithms when required.
Skills
- Path Planning
- Motion Planning
- Trajectory Generation
- Trajectory Tracking
- Advanced Control
- MPC
- ROS/ROS2
- C++
- Python
- Linux
- Git
- RViz/RViz2
- SLAM/Localization
- Perception
- Odometry
- Vehicle Control
- Robot Kinematics
- Coordinate Transformations
- Non-holonomic Motion Models
- LQR
- Nonlinear Control
- Pure Pursuit
- Stanley Control
- Global Planners
- Local Planners
- Obstacle Avoidance
- Collision Checking
- Forklifts
- AMRs
- AGVs
- Autonomous Vehicles
- Warehouse Robotics
- Forklift Kinematics
- Steering-based Kinematics
- Ackermann Kinematics
- Nav2 / ROS2 Navigation Stack
- A* Planning
- Hybrid A* Planning
- Dijkstra Planning
- RRT/RRT*
- DWA
- TEB
- Trajectory Optimization
- Constrained Optimization
- Real-time Embedded Systems
- Gazebo
- Isaac Sim
- Robotics Simulators
- Industrial Robot Safety
- Real-world Autonomous Deployment
Experience Level
- 3+ years of hands-on experience in motion planning, navigation, or control for mobile robots/autonomous systems.
Education Level
- Bachelor's/Master's degree in Robotics, Computer Science, Electrical/Mechanical Engineering, or related field.