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[cite: 1].

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[cite: 1].

Experience with forklifts, AMRs, AGVs, autonomous vehicles, or warehouse robots is highly desirable[cite: 1].

Key Responsibilities

• Design, implement, and deploy global/local path planning and motion planning algorithms[cite: 1].
• Develop trajectory generation and optimization considering robot kinematic and dynamic constraints[cite: 1].
• Design, implement, and tune MPC or other advanced motion/trajectory controllers[cite: 1].
• Develop robust trajectory tracking and path-following for real-world robots[cite: 1].
• Implement obstacle avoidance, collision checking, and recovery behaviors[cite: 1].
• Handle constraints including turning radius, steering, velocity, acceleration, angular velocity, robot footprint, and actuator limits[cite: 1].
• Integrate planning and control with SLAM/localization, perception, odometry, and vehicle control systems[cite: 1].
• Develop and maintain navigation software using ROS/ROS2[cite: 1].
• Test and validate algorithms in simulation and on physical robots[cite: 1].
• Diagnose issues such as path deviation, oscillations, unstable motion, poor tracking, deadlocks, and inefficient trajectories[cite: 1].
• Optimize algorithms for accuracy, robustness, and real-time performance[cite: 1].

Required Qualifications

• Bachelor's/Master's degree in Robotics, Computer Science, Electrical/Mechanical Engineering, or related field[cite: 1].
• 3+ years of hands-on experience in motion planning, navigation, or control for mobile robots/autonomous systems[cite: 1].
• Strong understanding of path planning, motion planning, trajectory generation, and trajectory tracking[cite: 1].
• Proven hands-on experience implementing and deploying MPC or equivalent advanced motion controllers on real robots[cite: 1].
• Strong understanding of robot kinematics, coordinate transformations, and non-holonomic motion models[cite: 1].
• Experience with control approaches such as MPC, LQR, nonlinear control, Pure Pursuit, Stanley, or equivalent[cite: 1].
• Strong understanding of global/local planners, obstacle avoidance, and collision checking[cite: 1].
• Strong C++ skills; Python is a plus[cite: 1].
• Strong hands-on experience with ROS/ROS2, with ROS2 preferred[cite: 1].
• Experience integrating planning and control with perception, localization, and odometry[cite: 1].
• Experience debugging robotics systems using RViz/RViz2, logs, simulation, and real-robot testing[cite: 1].
• Strong Linux and Git experience[cite: 1].
• Ability to take algorithms from concept → implementation → simulation → real-world deployment[cite: 1].

Good to Have

• Experience with forklifts, AMRs, AGVs, autonomous vehicles, or warehouse robotics[cite: 1].
• Experience with forklift/steering-based or Ackermann kinematics[cite: 1].
• Experience with Nav2 / ROS2 Navigation Stack[cite: 1].
• Experience with A*, Hybrid A*, Dijkstra, RRT/RRT*, DWA, TEB, or similar planning approaches[cite: 1].
• Experience with trajectory optimization and constrained optimization[cite: 1].
• Experience optimizing algorithms for real-time embedded systems[cite: 1].
• Experience with Gazebo, Isaac Sim, or other robotics simulators[cite: 1].
• Experience with industrial robot safety and real-world autonomous deployment[cite: 1].

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[cite: 1].

You should be able to diagnose a problem such as:[cite: 1]

"The robot follows the planned path poorly, oscillates around the trajectory, or fails to navigate a constrained warehouse aisle."[cite: 1]

and determine whether the root cause is planning, trajectory generation, kinematic constraints, controller design/tuning, localization, or actuation[cite: 1].

The candidate should be comfortable going beyond parameter tuning and modifying, optimizing, or developing planning and control algorithms when required[cite: 1].

Deep Planning & Control → Real-World Implementation → Reliable Autonomous Motion[cite: 1]