Context-aware robotic arm using fast embedded model predictive control

Abstract

The growing number of collaborative robotics in unstructured environments creates highly nonconvex nonlinear shared dynamical systems. For safety and speed, path planning and collision avoidance are of the utmost importance in these situations. We present a novel nonlinear MPC solution for use on a three-dimensional four-axis robotic manipulator. The system is the first of it's kind to take into account moving obstacles. Using the OpEn framework, optimisation is done by the PANOC and ALM techniques. Experimentation demonstrates extremely fast solver times on both PC and embedded platforms.

Safety Augmentation for Volitional Human Locomotion via Lower-Limb Exoskeletons: A Case Study

“…One potential challenge of using a two-QP structure instead of a one-QP structure is the increased computational complexity. The time complexity of calculating ( 12) and ( 14) is O(n 3 log(n)) with matrix inverse, which is feasible for real-time implementations on a microprocessor in similar studies [29].…”

Learning-based augmentation of physics-based models: an industrial robot use case

“…In our case, we require an accurate prediction of the movement of the arm for given actuator torque inputs over a given finite time window ahead. In robotic MPC applications, this window is typically between 100 milliseconds (e.g., Zhao et al, 2023) and 1 second (e.g., Trimble et al, 2020). An overview of the connection between MPC and system identification for our use case is presented in Figure 1.…”