Haptic training in simulation is a rising pedagogical trend in medical education. It is a rather new field that appeared partly because the adage “see one, do one, teach one”, from a mentoring standpoint, is undesirable due to public consideration for patient safety. Teaching strength management for a given procedure is a difficult task. This is not a skill one can retrieve from books or by only “seeing” the procedure. It needs to be experienced by the trainee. For this matter, haptic training on virtual patients offers a good opportunity to tackle this problem at the price of a constant trade-off between what technology can do and the expectation of realism. The technology is expensive, complex to maintain and very specific. Many simulators on the market use low-end devices to maintain the cost and are therefore unable to simulate proper interactions with the virtual patient. The platform presented here is an ecosystem which aims to study how to extend haptic simulations on a broader range of applications. We present an approach using innovative mechatronics, based on purely resistive force, to reach better haptic feedback at lower cost. The system is designed to be compact and safe. It allows strong and high resolution feedback as well as easy integration in existing devices. This technology will help to extend haptic simulations earlier in the curriculum where the resident requires basic hands-on experience.

Magneto-rheological fluids (MRF) are commonly applied in MRF brakes and vibration damping. The apparent viscosity dependence with respect to the magnetic field has been addressed in detail in the state of the art. The aim of this paper is to experimentally study the vibration effects on the particle chain-like structures and, as a consequence, the shear stress variation applied to the fluid. Three vibration configurations have been applied to a ferromagnetic cylinder rotating between two magnetic poles filled with MRF a “Z-vibration” where the generated displacement is along the rotation axis of the shearing cylinder, a “θ-vibration”, tangential to the cylinder, and an “R-vibration”, normal to the cylinder surface. First we focus on the vibration mode characterisation in free air, and then when plunged in the fluid. In a second step, we measure the reactive torque generated on the clutch under different magnetic field intensities with different rotation speeds and vibration amplitudes. It appears that the “R-vibration” configuration is providing the most influence, up to 20% of torque reduction observed at moderate B field. The “Z-vibration” and the “θ-vibration” configurations respectively have less influence on the torque, nevertheless vibrations always tend to decrease the corresponding yield stress in the MRF.

This paper presents the preliminary work on a new concept of a piezoelectric moto-reducer for high torque application. It combines an ultrasonic motor based on a simple disk-shaped piezoelectric buzzer with a flat strain wave reducer.

This paper focuses on a new torque detection technique for magnetorheological (MR) actuators. An MR fluid consists of a suspension of ferromagnetic micrometer-sized particles in a carrier fluid. Under the action of a magnetic field, these particles form chain-like structures that interact with the magnetic poles. The torque detection technique is based on the assumption that a relative displacement of the poles stretches the chains, altering the magnetic reluctance of the fluid gap. This hypothesis is analytically developed using an elementary group of ferromagnetic particles placed in a nonmagnetic carrier liquid. A measure of the excitation coil impedance using a high precision demodulator, is used to verify this hypothesis. Experimental results show that when the poles are displaced before the rupture of the chains, the chains are stretched and the reluctance increases. A higher sensitivity system is subsequently proposed to detect the variation of an external torque. The experimental results demonstrate that the system is able to detect the application as well as the release of the torque and can successfully be employed to detect the chain rupture critical point.

This paper proposes a novel model-based tracking approach for 3-D localization. One main difficulty of standard model-based approach lies in the presence of low-level ambiguities between different edges. In this paper, given a 3-D model of the edges of the environment, we derive a multiple hypotheses tracker which retrieves the potential poses of the camera from the observations in the image. We also show how these candidate poses can be integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Motivated by the UAV indoor localization problem where GPS signal is not available, we validate the algorithm on real image sequences from UAV flights.

This paper presents the design and the stability analysis of a hierarchical controller for unmanned aerial vehicles (UAV), using singular perturbation theory. Position and attitude control laws are successively designed by considering a time-scale separation between the translational dynamics and the orientation dynamics of a six degrees of freedom vertical take-off and landing (VTOL) UAV model. For the design of the position controller, we consider the case where the linear velocity of the vehicle is not measured. A partial state feedback control law is proposed, based on the introduction of a virtual state into the translational dynamics of the system. Results from simulation and from experiments on a miniature quadrirotor UAV are provided to illustrate the performance of the proposed control scheme.

This paper proposes a vision-based algorithm to autonomously track and chase a moving target with a small-size flying UAV. The challenging constraints associated with the UAV flight led us to consider a density-based representation of the object to track. The proposed approach to estimate the target's position, orientation and scale, is built on a robust color-based tracker using a multi-part representation. This object tracker can handle large displacements, occlusions and account for some image noise due to partial loss of wireless video link, thanks to the use of a particle filter. The information obtained from the visual tracker is then used to control the position and yaw angle of the UAV in order to chase the target. A hierarchical control scheme is designed to achieve the tracking task. Experiments on a quad-rotor UAV following a small moving car are provided to validate the proposed approach.

Classic registration methods for model-based tracking try to align the projected edges of a 3D model with the edges of the image. However, wrong matches at low level can make these methods fail. This paper presents a new approach allowing to retrieve multiple hypothesis on the camera pose from multiple low-level hypothesis. These hypothesis are integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Experiments on simulated and real video sequences show the improvement in robustness of the resulting tracker.

This paper presents a 3D model-based tracking suitable for indoor position control of an unmanned aerial vehicle (UAV). Given a 3D model of the edges of its environment, the UAV locates itself thanks to a robust multiple hypothesis tracker. The pose estimation is then fused to inertial data to provide the translational velocity required for the control. A hierarchical control is used to achieve positioning tasks. Experiments on a quad-rotor aerial vehicle validate the proposed approach.

Due to the various devices composing a smart camera system, various languages have to be known by the designer (like HDL and C/C++). Most of vision applications designers are software programmers and do not have a good knowledge of HDLs (VHDL). This paper presents a new high-level methodology for implementing vision applications on smart camera platforms. This methodology is based on a soft-core approach to manage the whole system and a dataflow (actor-oriented) language to design the processing elements. We discuss in particular interfacing constraints.

Color-based tracking methods have proved to be efficient for their robustness qualities. The drawback of such global representation of an object is the lack of information on its spatial configuration, making difficult the tracking of more complex motions. This issue can be overcome by using several kernels weighting pixels locations.

In this paper, we investigate a range of image-based visual servo control algorithms for regulation of the position of a quadrotor aerial vehicle.The most promising control algorithms have been successfully implemented on an autonomous aerial vehicle and demonstrate excellent performance.

In this paper, we present a generic framework for urban vehicle navigation using a topological map. This map i s built by taking into account the non-holonomic behaviour of the vehicle. After a localization step, a sensory route is extra cted to reach a goal. This route is followed using a sensor-based con trol strategy, based on the vehicle model and computed from the state extracted from the current and the desired sensory ima ges. In that aim, a generic model is proposed for visual sensors. Experiments with an urban electric vehicle navigating in an outdoor environment have been carried out with a fisheye came ra using a single camera and natural landmarks. A navigation al ong a 1700-meter-long trajectory validates our approach.

In this paper, we present a method to efficiently manage visual memory for autonomous vehicle navigation in large scale environments. It relies on two crucial issues for real-time navigation: an efficient organisation of the memory and small computational cost. A software platform (SoViN) dedicated to visual memory management and navigation strategies (including vision-based memory building, localization and navigation) has been developed to fulfill these requirements. We show that using this software architecture makes possible real-time navigation in large-scale outdoor situation using a single camera and natural landmarks.

An efficient method for global robot localization in a memory of omnidirectional images is presented. This method is valid for indoor and outdoor environments and not restricted to mobile robots. The proposed strategy is purely vision-based and uses as reference a set of prerecorded images (visual memory). The localization consists on finding in the visual memory the image which best fits the current image. We propose a hierarchical process combining global descriptors computed onto cubic interpolation of triangular mesh and patches correlation around Harris corners. To evaluate this method, three large images data sets have been used. Results of the proposed method are compared with those obtained from state-of-the-art techniques by means of 1) accuracy, 2) amount of memorized data required per image and 3) computational cost. The proposed method shows the best compromise in term of those criteria.

In this paper, we present a complete framework for autonomous indoor robot navigation. We show that autonomous navigation is possible in indoor situation using a single camera and natural landmarks. When navigating in an unknown environment for the first time, a natural behavior consists on memorizing some key views along the performed path, in order to use these references as checkpoints for a future navigation mission. The navigation framework for wheeled robots presented in this paper is based on this assumption. During a human-guided learning step, the robot performs paths which are sampled and stored as a set of ordered key images, acquired by an embedded camera. The set of these obtained visual paths is topologically organized and provides a visual memory of the environment. Given an image of one of the visual paths as a target, the robot navigation mission is defined as a concatenation of visual path subsets, called visual route. When running autonomously, the control guides the robot along the reference visual route without explicitly planning any trajectory. The control consists on a vision-based control law adapted to the nonholonomic constraint. The proposed framework has been designed for a generic class of cameras (including conventional, catadioptric and fish-eye cameras). Experiments with a AT3 Pioneer robot navigating in an indoor environment have been carried on with a fisheye camera. Results validate our approach.

Image moments provide an important class of image features used for image‐based visual servo control. Perspective zeroth and first order image moments provide a quasi linear and decoupled link between the image features and the translational degrees of freedom. Spherical first‐order image moments have the additional desirable passivity property. They allow to decouple the position control scheme from the rotation dynamics. This property is suitable to control an under‐actuated aerial vehicle such as a quadrotor. In this paper a range of kinematic control laws using spherical image moments and perspective image moments are experimented on a quadrotor aerial vehicle prototype. The task considered is to reach a desired position with respect to a specified target. Three control schemes show excellent performances in practice whereas each one has different theoretical properties.

Visual systems are key sensors for control of small scale unmanned aerial vehicles. In this paper we investigate a range of image based visual servo control algorithms for positioning of flying vehicles capable of hover. The image based outer control loop for translation kinematics is coupled to a high-gain inner control loop that regulates translational velocities and full attitude dynamics. Zero and first order image moments are used as visual features for the control design. Perspective projection moments with suitable scaling along with a classical image based visual servo control design lead to satisfactory transients and asymptotic stability of the closed-loop system when the image plane remains parallel to the target. However, the system response may lack robustness for aggressive manoeuvres. In order to overcome this problem, several control schemes, based on spherical image moments, are designed and their performance is analysed. All designed control laws have been tested on a kinematic robotic manipulator to demonstrate the relative strengths and weaknesses of thedifferent image based visual servo control designs. The three most promising control algorithms have been successfully implemented on an autonomous aerial vehicle showing excellent performances in all three cases.