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]