Autonomous Motion Planning & Control Engineer - Logistics AGF Team

We are looking for a Motion Planning / Navigation Engineer to develop and deploy the algorithms that enable 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.

Key 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.
• Required Qualifications: 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.
• Good to Have: Experience with forklifts, AMRs, AGVs, autonomous vehicles, or warehouse robotics.
• Experience with forklift/steering-based or Ackermann kinematics.
• Experience with Nav2 / ROS2 Navigation Stack.
• Experience with A, Hybrid A , Dijkstra, RRT/RRT*, DWA, TEB**, or similar planning approaches.
• Experience with trajectory optimization and constrained optimization.
• Experience optimizing algorithms for real-time embedded systems.
• Experience with Gazebo, Isaac Sim, or other robotics simulators.
• Experience with industrial robot safety and real-world autonomous deployment.

What We Are Looking For: We are looking for someone who understands

• how planning and control work under the hood
• , not someone who has only integrated existing navigation packages.
• You should be able to diagnose a problem such as: "The robot follows the planned path poorly, oscillates around the trajectory, or fails to navigate a constrained warehouse aisle." and determine whether the root cause is planning, trajectory generation, kinematic constraints, controller design/tuning, localization, or actuation.
• The candidate should be comfortable going beyond parameter tuning and modifying, optimizing, or developing planning and control algorithms when required.
• Deep Planning & Control → Real-World Implementation → Reliable Autonomous Motion