Bayesian neural network modeling and hierarchical MPC for a tendon-driven surgical robot with uncertainty minimization

In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot

Effects of Nonthermal Plasma (NTP) on the Growth and Quality of Baby Leaf Lettuce (Lactuca sativa var. acephala Alef.) Cultivated in an Indoor Hydroponic Growing System

The aim of this research was to develop an effective protocol for the application of nonther-mal plasma (NTP) technology to the hydroponic nutrient solution, and to investigate its effects on the growth and quality of baby leaf lettuce (Lactuca sativa var. acephala Alef.) grown in a hydroponic growing system (HGS) specifically designed for indoor home cultivation. Four HGSs were placed in separate growth chambers with temperature of 24 ± 1◦ C and relative humidity of 70 ± 5%). Lettuce plants were grown for nine days in nutrient solutions treated with NTP for 0 (control) to 120 s every hour. Results of the first experiments showed that the optimal operating time of NTP was 120 s h−1 . Fresh leaf biomass was increased by the 60 and 120 s NTP treatments compared to the control. Treating the nutrient solution with NTP also resulted in greater leaf content of total chloro-phylls, carotenoids, total phenols, and total antioxidant capacity. NTP also positively influenced chlorophyll a fluorescence in Photosystem I (PSI) and photosynthetic electron transport. These results revealed that the NTP treatment of the nutrient solution could improve the production and quality of hydroponically grown baby leaf lettuce

Model learning with backlash compensation for a tendon-driven surgical Robot

Robots for minimally invasive surgery are becoming more and more complex, due to miniaturization and flexibility requirements. The vast majority of surgical robots are tendon-driven and this, along with the complex design, causes high nonlinearities in the system which are difficult to model analytically. In this work we analyse how incorporating a backlash model and compensation can improve model learning and control. We combine a backlash compensation technique and a Feedforward Artificial Neural Network (ANN) with differential relationships to learn the kinematics at position and velocity level of highly articulated tendon-driven robots. Experimental results show that the proposed backlash compensation is effective in reducing nonlinearities in the system, that compensating for backlash improves model learning and control, and that our proposed ANN outperforms traditional ANN in terms of path tracking accuracy

Augmented neural network for full robot kinematic modelling in SE(3)

Due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build. Machine learning approaches have shown great capabilities in learning complex mapping and have widely been used in robot model learning and control. Generally, the inverse kinematics is directly learned, yet, learning the forward kinematics is simpler and allows computing exploiting the optimality of the controllers. Nevertheless, the learning method has no knowledge about the differential relationship between the position and velocity mappings. Currently, few works have targeted learning full robot poses considering both position and orientation. In this letter, we present a novel feedforward Artificial Neural network (ANN) architecture to learn full robot pose in SE(3) incorporating differential relationships in the learning process. Simulation and real world experiments show the capabilities of the proposed network to properly model the robot pose and its advantages over standard ANN

Model predictive control for a tendon-driven surgical robot with safety constraints in kinematics and dynamics

In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety and accuracy of the surgical task. Mechanical designs and actuation schemes have inevitable limitations such as backlash and joint limits. Moreover, surgical robots need to operate in narrow pathways, which may give rise to additional environmental constraints. Therefore, the control strategies must be capable of satisfying the desired motion trajectories and the imposed constraints. Model Predictive Control (MPC) has proven effective for this purpose, allowing to solve an optimal problem by taking into consideration the evolution of the system states, cost function, and constraints over time. The high nonlinearities in tendon-driven systems, adopted in many surgical robots, are difficult to be modelled analytically. In this work, we use a model learning approach for the dynamics of tendon-driven robots. The dynamic model is then employed to impose constraints on the torques of the robot under consideration and solve an optimal constrained control problem for trajectory tracking by using MPC. To assess the capabilities of the proposed framework, both simulated and real world experiments have been conducte