Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Medical Robotics II
Automatic Extraction of Command Hierarchies for Adaptive Brain-Robot InterfacingRecent advances in neuroscience and robotics have allowed initial demonstrations of brain-computer interfaces (BCIs) for controlling wheeled and humanoid robots. However, further advances have proved challenging due to the low throughput of the interfaces and the high degrees-of-freedom (DOF) of the robots. In this paper, we build on our previous work on Hierarchical BCIs (HBCIs) which seek to mitigate this problem. We extend HBCIs to allow training of arbitrarily complex tasks, with training no longer restricted to a particular robot state space (such as Cartesian space for a navigation task). We present two algorithms for learning command hierarchies by automatically extracting patterns from a user's command history. The first algorithm builds an arbitrary-level hierarchical structure (a "control grammar") whose elements can represent skills, whole tasks, collections of tasks, etc. The user "executes" single symbols from this grammar, which produce sequences of lower-level commands. The second algorithm, which is probabilistic, also learns sequences which can be executed as high-level commands, but does not build an explicit hierarchical structure. Both algorithms provide a de facto form of dictionary compression, which enhances the effective throughput of the BCI. We present results from two human subjects who successfully used the hierarchical BCI to control a simulated PR2 robot using brain signals recorded non-invasively through electroencephalography (EEG).
Powered Wheelchair Navigation Assistance through Kinematically Correct Environmental Haptic FeedbackThis article introduces a set of novel haptic guidance algorithms intended to provide intuitive and reliable assistance for electric wheelchair navigation through narrow or crowded spaces. The proposed schemes take hereto the nonholonomic nature and a detailed geometry of the wheelchair into consideration. The methods encode the environment as a set of collision-free circular paths and, making use of a model-free impedance controller, â€˜hapticallyâ€™ guide the user along collision-free paths or away from obstructed paths or paths that simply do not coincide with the motion intended by the user. The haptic feedback plays a central role as it establishes a fast bilateral communication channel between user and wheelchair controller and allows a direct negotiation about wheelchair motion. If found unsatisfactory, suggested trajectories can always be overruled by the user. Relying on inputs from user modeling and intention recognition schemes, the system can reduce forces needed to move along intended directions, thereby avoiding unnecessary fatigue of the user. A commercial powered wheelchair was upgraded and feasability tests were conducted to validate the proposed methods. The potential of the proposed approaches was hereby demonstrated.
A Haptic Instruction Based Assisted Driving System for Training the Reverse ParkingThe accident probability of beginner drivers is significantly higher than that of experienced drivers. It can be assumed that this is due to lack of driving skills which lead to making wrong decisions according to cognition and operating in correct way. In this paper, we propose a novel assisted driving system intended to help drivers to improve their skills for the reverse parking. The system is able to assist the driver by haptic instruction on the steering wheel in order to induce the driver to make the adequate operation. For the validation, we developed a 1/10 scale car simulator as a simulation environment on which we installed the proposed assistance method and conducted reverse parking experiment by using the simulator. According to the experiment, we validated that the parking accuracy and the trajectory similarity of subjects assisted by proposed system significantly increased compare to subjects unassisted. Consequently, the proposed assisted driving system could accelerate the learning of humans' driving skills.
Remote Palpation to Localize Tumors in Robot-Assisted Minimally Invasive ApproachThis paper presents a new tactile-force integrated method to localize tumors minimally invasively using robotic assistance. This method relies on using a capacitive sensor at the tip of a Tactile Sensing Instrument (TSI) which can be inserted into a patient's body in a minimally invasive manner. In this work, the operator palpates tissue containing tumors in a minimally invasive surgical (MIS) training box, representing the patient's body, through a master-slave teleoperation system which consists of a 7 degrees-of-freedom (DOF) haptic interface, used as the master, and a Mitsubishi PA10-7C robot as the slave. Using the proposed method, the operator would be able to palpate the tissue consistently, observe the pressure distribution over the tissue by a color contour map on a screen and feel the tumor on his/her fingers through a grasping mechanism of the haptic interface as a result of higher stiffness of the tumor. The tissue used for the experiments was ex vivo bovine lung and seven participants were asked to locate artificial tumors embedded in the lungs. The results show an accuracy of 93% in tumor localization using the proposed method while the average force applied to the tissue was 3.42 N and the force never exceeded 6 N.
Improvements in the Control of a Flexible Endoscopic SystemThe use of flexible cable-driven systems is common in medicine (endoscope, catheter...). Their flexiblity allows surgeons to reach internal organs through sinuous and constrained ways. Unfortunately these systems are subject to backlash due to their internal mechanism. These non linearities raise many difficulties when robotizing and controlling such systems. In this article we propose an approach to improve the cartesian control of a four ways flexible endoscopic system with strong and unknown backlash-like non linearities. The method is based on an automatic off-line hystereses learning. We show that, despite coupling between degrees of freedom, it is possible to extract information from the hystereses which allow to improve cartesian control. Experiments on a real endoscopic system show the validity and the interest of the approach.