This letter presents a novel computationally efficient and robust pattern tracking method based on a time-encoded, frame-free visual data. Recent interdisciplinary developments, combining inputs from engineering and biology, have yielded a novel type of camera that encodes visual information into a continuous stream of asynchronous, temporal events. These events encode temporal contrast and intensity locally in space and time. We show that the sparse yet accurately timed information is well suited as a computational input for object tracking. In this letter, visual data processing is performed for each incoming event at the time it arrives. The method provides a continuous and iterative estimation of the geometric transformation between the model and the events representing the tracked object. It can handle isometry, similarities, and affine distortions and allows for unprecedented real-time performance at equivalent frame rates in the kilohertz range on a standard PC. Furthermore, by using the dimension of time that is currently underexploited by most artificial vision systems, the method we present is able to solve ambiguous cases of object occlusions that classical frame-based techniques handle poorly.

The optical trap is a powerful non-contact approach for manipulating micron sized objects. Teleoperation of optical tweezers can be performed by coupling with a haptic interface, which allows an efficient robotic device to control positions and get force feedback. This provides users direct and intuitive microscopic interactions. The major difficulty in order for haptic devices to generate a reliable tactile sensation lies in its high frequency requirement of more than 1 kHz. This paper presents a fast force feedback teleoperation system for optical tweezers that attains this high frequency. The used force sensor is a novel event-based camera that transmits output as a continuous stream of asynchronous temporal events thus enabling high speed event-based visual processing. This new sensor is compared to a conventional frame based one to show advantages of our setup. A complex task of exploiting three dimensional target surface is performed demonstrating the robustness and efficiency of the presented method. This is the first time microspheres are used to touch targets of arbitrary form and color, which may interest broad-reaching biological and physical applications.

This work presents a stable vision based haptic feedback for micromanipulation using both an asynchronous Address Event Representation (AER) silicon retina and a conventional frame-based camera. At this scale, most of the grippers used to manipulate objects lack of force sensing. High frequency vision detection thus provides a sound solution to get information about the position of the object and the tool to provide virtual haptic guides. Artificial retinas present high update rates, which enables to address one of the major challenge of haptic feedback teleoperation systems, namely stability. However static objects are not detected. The haptic feedback is thus based on an asynchronous silicon retina to provide a high update rate of moving objects and a frame-based camera to retrieve the position of the target object. This approach is validated by pick-and-place of microspheres (diameter: around 50 micrometers) using a piezoelectric microgripper. The displacement of the tool, as well as the opening and closing of the gripper are controlled by the haptic device. Haptic virtual guides are transmitted to users to assist them in the different steps of the pick-and-place task: a virtual stiffness ensures the correct alignment of the tool with respect to the object, a repulsive haptic force enables users to monitor the gripping step, and operators are assisted while picking and placing the object.

Micromanipulation systems have recently been receiving increased attention. Teleoperated or automated micromanipulation is a challenging task due to the need for high-frequency position or force feedback to guarantee stability. In addition, the integration of sensors within micromanipulation platforms is complex. Vision is a commonly used solution for sensing; unfortunately, the update rate of the frame-based acquisition process of current available cameras cannot ensure-at reasonable costs-stable automated or teleoperated control at the microscale level, where low inertia produces highly unreachable dynamic phenomena. This paper presents a novel vision-based microrobotic system combining both an asynchronous address event representation silicon retina and a conventional frame-based camera. Unlike frame-based cameras, recent artificial retinas transmit their outputs as a continuous stream of asynchronous temporal events in a manner similar to the output cells of a biological retina, enabling high update rates. This paper introduces an event-based iterative closest point algorithm to track a microgripper's position at a frequency of 4 kHz. The temporal precision of the asynchronous silicon retina is used to provide a haptic feedback to assist users during manipulation tasks, whereas the frame-based camera is used to retrieve the position of the object that must be manipulated. This paper presents the results of an experiment on teleoperating a sphere of diameter around 50 μm using a piezoelectric gripper in a pick-and-place task.