A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers

This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability

A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers

This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability

RGB-D camera based collision prediction and avoidance for X-ray rotational angiography

Optimal clinical workflow leads to faster treatment times and a potential to cater to a larger number of patients. A key part of this is preventing collisions between moving medical systems and patient. For interventional environments, highspeed rotational angiography (RA) scans are prone to potential collisions between the C-arm X-ray system and the patient. To ensure safety, a low speed safety-run is executed before the actual high-speed scan. However, several iterations of the safetyrun are often required before a scan is collision-free leading to a suboptimal clinical work-flow. This work proposes a RGB-D camera based collision prediction system which detects collisions before the actual RA scan. Additionally, a motion planner is designed to provide appropriate patient repositioning such that the collision is avoided. The system is introduced as a first proofof-concept to eliminate the safety-run and improve clinical safety and workflow

RGB-D camera-based clinical workflow optimization for rotational angiography

High-speed Rotational Angiography scans are prone to potential collisions between the C-arm X-ray system and the patient. A key factor during rotational angiography clinical workflow is thus the initial patient positioning to ensure a collision-free scan. The current practice for this involves multiple manual iterations of a low speed safety-run prior to the actual scan. Several iterations are often required before a scan is collision-free leading to a suboptimal clinical workflow. This work proposes a RGB-D camera based safety system which automates the patient positioning process. The camera determines collisions between the C-arm and patient and a re-positioning algorithm determines the movement of the patient table required to ensure a collisoin-free scan. A unique feature of the solution is the ability to warn clinical staff when a collision-free scan is not possible. The efficacy of the proposed system is illustrated experimentally for two major RA scan types

Collision-Free Trajectory Planning With Deadlock Prevention: An Adaptive Virtual Target Approach

status: publishe

Collision-Free Trajectory Planning with Deadlock Prevention: An Adaptive Virtual Target Approach

Most human-centred robotic applications require robots to follow a certain pre-defined path. This makes the robot’s autonomous movements acceptable and predictable for humans. Planning a trajectory for the robot thus involves guiding it along this desired path. The classical approach of segmenting a path into multiple waypoints and tracking them only works well in environments which are obstacle-free or contain fixed stationary obstacles. Movable or dynamic obstacles that can potentially lie directly on waypoints result in deadlock situations causing the robot to oscillate around the desired waypoint without moving forward. This chapter presents a novel approach for trajectory planning in which an Adaptive Virtual Target (AVT) is formulated that follows the desired path irrespective of surrounding obstacles. The AVT essentially plays the role of a moving reference for the trajectory planner to track. Additionally, the AVT velocity can be adapted such that the robot can catch up in case of deviations from the path due to obstacle avoidance manoeuvres. A model predictive control (MPC) based trajectory planner tracks the AVT and accounts for obstacle avoidance. The proposed approach allows the robot to keep moving towards the goal by preventing deadlocks while simultaneously minimizing deviation from the desired path. Simulations based on a medical X-ray robot are provided to validate the approach

RGB-D camera based collision prediction and avoidance for X-ray rotational angiography

Optimal clinical workflow leads to faster treatment\u3cbr/\u3etimes and a potential to cater to a larger number of patients. A\u3cbr/\u3ekey part of this is preventing collisions between moving medical\u3cbr/\u3esystems and patient. For interventional environments, highspeed\u3cbr/\u3erotational angiography (RA) scans are prone to potential\u3cbr/\u3ecollisions between the C-arm X-ray system and the patient. To\u3cbr/\u3eensure safety, a low speed safety-run is executed before the\u3cbr/\u3eactual high-speed scan. However, several iterations of the safetyrun\u3cbr/\u3eare often required before a scan is collision-free leading to\u3cbr/\u3ea suboptimal clinical work-flow. This work proposes a RGB-D\u3cbr/\u3ecamera based collision prediction system which detects collisions\u3cbr/\u3ebefore the actual RA scan. Additionally, a motion planner is\u3cbr/\u3edesigned to provide appropriate patient repositioning such that\u3cbr/\u3ethe collision is avoided. The system is introduced as a first proofof-\u3cbr/\u3econcept to eliminate the safety-run and improve clinical safety\u3cbr/\u3eand workflow

Collision-Free Trajectory Planning with Deadlock Prevention: An Adaptive Virtual Target Approach

Most human-centred robotic applications require robots to follow a certain pre-defined path. This makes the robot’s autonomous movements acceptable and predictable for humans. Planning a trajectory for the robot thus involves guiding it along this desired path. The classical approach of segmenting a path into multiple waypoints and tracking them only works well in environments which are obstacle-free or contain fixed stationary obstacles. Movable or dynamic obstacles that can potentially lie directly on waypoints result in deadlock situations causing the robot to oscillate around the desired waypoint without moving forward. This chapter presents a novel approach for trajectory planning in which an Adaptive Virtual Target (AVT) is formulated that follows the desired path irrespective of surrounding obstacles. The AVT essentially plays the role of a moving reference for the trajectory planner to track. Additionally, the AVT velocity can be adapted such that the robot can catch up in case of deviations from the path due to obstacle avoidance manoeuvres. A model predictive control (MPC) based trajectory planner tracks the AVT and accounts for obstacle avoidance. The proposed approach allows the robot to keep moving towards the goal by preventing deadlocks while simultaneously minimizing deviation from the desired path. Simulations based on a medical X-ray robot are provided to validate the approach

The impact of behavioural executive functioning and intelligence on math abilities in children with intellectual disabilities

BACKGROUND\nLittle is known about the role of behavioural executive functioning (EF) skills and level of intelligence (IQ) on math abilities in children with mild to borderline intellectual disabilities.\nMETHOD\nTeachers of 63 children attending a school for special education (age: 10 to 13 years; IQ: 50 to 85) filled out a Behaviour Rating Inventory for Executive Function for each student. Furthermore, students took a standardised national composite math test and a specific math test on measurement and time problems. Information on level of intelligence was gathered through school records. Multiple regression analyses were performed to test direct, moderating and mediating effects of EF and IQ on math performance.\nRESULTS\nBehavioural problems with working memory and flexibility had a direct negative effect on math outcome, while concurrently, level of intelligence had a positive effect. The effect of IQ on math skills was moderated by problems with inhibition: in children with a clinical level of inhibition problems, there was no effect of level of intelligence on math performance.\nCONCLUSIONS\nFindings suggest that in students with mild to borderline intellectual disabilities and math difficulties, it is important to address their strengths and weaknesses with respect to EF and adjust instruction and remedial intervention accordingly.</p