Robotics See Motion Control Advances
Autonomous robots are making major leaps in factory automation, driven by breakthroughs in motion control and AI. New systems now achieve sub-millimeter precision in pick-and-place tasks, while advanced algorithms like Model Predictive Control (MPC) enable real-time path planning in dynamic environments, expanding robotics into logistics and beyond.
The hardware executing these complex motion algorithms is shifting from custom ASICs to Field-Programmable Gate Arrays (FPGAs). Companies like Yaskawa Electric use Intel FPGAs in their robot controllers to shorten development cycles and allow for rapid logic changes, a flexibility not possible with fixed-function ASICs. FPGAs excel at the parallel processing required for tasks like sensor fusion and can implement motor control loops 10-100x faster than software-based solutions. Model Predictive Control (MPC) is effective in dynamic settings because it continuously re-plans. At each step, the algorithm optimizes a sequence of future control actions over a finite time horizon but implements only the first action. It then updates its plan based on new sensor data, allowing it to adapt to moving obstacles and changing goals in real time, a method also known as receding horizon control. The push for precision has led to specialized hardware like the MiGriBot, a miniature robot developed at France's FEMTO-ST Institute. It achieves 720 pick-and-place cycles per minute with micrometer accuracy—eclipsing the ~250 cycles of typical industrial robots—by using a lightweight, articulated structure and piezoelectric actuators. System integration is also advancing with servo drives now being built directly into robotic actuators, eliminating bulky control cabinets and reducing points of failure. Communication protocols like EtherCAT are becoming standard for these integrated systems, enabling the high-speed, deterministic data exchange necessary for complex, multi-axis motion. This technology is critical in the Los Angeles aerospace sector. Local firms like Honeybee Robotics develop advanced motion control solutions for NASA and commercial space programs, creating robotics for use in extreme environments. Major aerospace players like Northrop Grumman are also key employers in the aerospace robotics market. The next evolution is Learning Model Predictive Control (LeMPC), where the system's underlying model is refined with data from each operational run. This allows autonomous mobile robots to learn the nuances of their environment—like worn surfaces or varying payloads—and continuously self-calibrate their control strategy for improved precision and throughput.
