Cognigear
Autonomy Stack Engineering

Motion Planning & Control Tuning for Industrial Vehicles

Design and tune planners and controllers for specific vehicle types (haul trucks, yard trucks, forklifts, AGVs, cranes).

Timeline
7 Weeks to Value
Typical Engagement
$70k–$200k
Focus Areas
Articulated trucks, Ackermann steering, Differential drive

Motion Planning & Control Tuning for Industrial Vehicles

Achieve smooth, precise, and efficient movement for your autonomous heavy equipment. Tune controllers to handle 400-ton masses or millimeter-precise docking.

  • Replace "jerky" bang-bang control with smooth MPC (Model Predictive Control)
  • Handle complex kinematics (articulated haulers, trailers) in confined spaces
  • Optimize speed profiles for fuel efficiency and reduced mechanical wear

Who this is for

Control Engineers, Robotics Leads, and Vehicle Performance Managers at:

  • Industrial OEMs refining vehicle dynamics performance
  • Autonomy companies porting stacks to new, larger platforms
  • Brownfield sites needing robots to navigate tight, human-designed layouts

Operational context

This engagement focuses on:

  • Algorithms – Lattice planners, Hybrid A*, RRT*, MPC, PID, Pure Pursuit, LQR
  • Dynamics – Modeling vehicle mass, tire slip, hydraulic lag, and articulation joints
  • Constraints – Rollover prevention, actuator limits, slope/grade handling

Trigger phrases you might be saying

  • “The truck oscillates left and right when driving straight.”
  • “We can’t back up the trailer into the dock autonomously.”
  • “The robot is tearing up the tires because the turning is too aggressive.”
  • “We overshoot the stop line every time it rains.”

Business outcomes

  • Higher availability on slippery/challenging terrain through robust control
  • Reduced maintenance costs (less tire/brake/hydraulic wear)
  • Improved cycle times via optimized velocity profiles
  • Safety compliance via guaranteed stopping distances and stability margins

What we deliver

  • Vehicle kinematic and dynamic model identification
  • Architecting the Planning & Control subsystem (Global Planner → Local Planner → Controller)
  • Tuning of controller parameters (PID gains, MPC cost weights)
  • Implementation of path smoothing algorithms
  • Docking / Precision maneuvering logic design

How it works

  1. Model – Check/create the mathematical model of the vehicle (System Identification)
  2. Design – Select the right planning/control architecture for the use case
  3. Tune – Iteratively tune parameters in simulation and then on-vehicle

Timeline & effort

  • Duration: 6-10 weeks
  • Client time: Access to vehicle for testing, safety driver support
  • Data: Vehicle specs (geometry, mass, actuator limits), CAN logs

Pricing bands

Fixed-fee: $70k–$200k, depending on:

  • Vehicle kinematic complexity (e.g., reversing a double trailer is hard)
  • Operational speed (high speed = stability critical)
  • Simulation infrastructure availability

Tech stack & integrations

  • Solvers: OSQP, Ipopt, ACADO (for MPC)
  • Libraries: OMPL, navigation2 (ROS2), CasADi
  • Models: Bicycle model, Ackermann, Differential Drive, Custom Articulated

Risks & safeguards

We explicitly design for:

  • Stability – rigorous theoretical stability analysis (Lyapunov) where possible
  • Actuator saturation – handling cases where the physical machine simply can't do what the code asks
  • Latency compensation – predicting system state to account for computing/actuation delays
  • Fail-safe trajectories – pre-calculating emergency escape paths

Site examples

  • Container Port (Europe) – Developed a reversing controller for a terminal tractor with a trailer, enabling automated reversing into parking spots with +/- 10cm accuracy.
  • Open-pit Mine (South America) – Tuned the velocity planner for 300-ton haul trucks to optimize for fuel burn on ramp ascents, achieving 4% fuel savings.

Frequently asked questions

Can you make it drive smoother? Yes. "Jerky" driving is usually a sign of poor trajectory generation or overtuned gains. We implement jerk-limited trajectories and MPC to smooth this out.

Do we need a perfect simulator? Simulation helps immensely, but we can do System Identification (SysID) using real vehicle logs to build a "good enough" model for tuning.

Does this include obstacle avoidance? This covers the execution of avoidance trajectories. Defining what to avoid and which side to pass is "Behavior Planning" (Service 8), though they are closely linked.


Target KPIs

  • Path tracking error (cross-track)
  • Fuel/Energy consumption efficiency
  • Ride comfort (jerk)
  • Docking accuracy
  • Speed profile consistency

Deployed Environments

Confined spacesRampsSlippery/muddy surfaces

Ready to start?

Book a 15-minute technical scoping call to discuss your fleet requirements.

Book Scoping Call

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