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Robot Assisted Surgery is attracting increasing amount of attention as it offers numerous benefits to patients as well as surgeons. Heart surgery requires a high level of precision and dexterity, in contrast to other surgical specialties. Robot assisted heart surgery is not as widely performed due to numerous reasons including a lack of appropriate and intuitive surgical interfaces to control minimally invasive surgical tools. In this paper, finger motion of the surgeon is analyzed during cardiac surgery tasks on an ex-vivo animal model with the purpose of designing a more intuitive master console. First, a custom finger tracking system is developed using IMU sensors, which is lightweight and comfortable enough to allow free movement of the surgeons fingers/hands while using instruments. The proposed system tracks finger joint angles and fingertip positions for three involved fingers (thumb, index, middle). Accuracy of the IMU sensors has been evaluated using an optical tracking system (Polaris, NDI). Finger motion of the cardiac surgeon while using a Castroviejo instrument is studied in suturing and knotting scenarios. The results show that PIP and MCP joints have larger Range Of Motion (ROM), and faster rate of change compared to other finger/thumb joints, while thumb has the largest Fingertip WorkSpace (FWS) of all three digits.

Summary In this paper, the behavioral learning of robots through spiking neural networks is studied in which the architecture of the network is based on the thalamo-cortico-thalamic circuitry of the mammalian brain. According to a variety of neurons, the Izhikevich model of single neuron is used for the representation of neuronal behaviors. One thousand and ninety spiking neurons are considered in the network. The spiking model of the proposed architecture is derived and prepared for the learning problem of robots. The reinforcement learning algorithm is based on spike-timing-dependent plasticity and dopamine release as a reward. It results in strengthening the synaptic weights of the neurons that are involved in the robot’s proper performance. Sensory and motor neurons are placed in the thalamus and cortical module, respectively. The inputs of thalamo-cortico-thalamic circuitry are the signals related to distance of the target from robot, and the outputs are the velocities of actuators. The target attraction task is used as an example to validate the proposed method in which dopamine is released when the robot catches the target. Some simulation studies, as well as experimental implementation, are done on a mobile robot named Tabrizbot. Experimental studies illustrate that after successful learning, the meantime of catching target is decreased by about 36%. These prove that through the proposed method, thalamo-cortical structure could be trained successfully to learn to perform various robotic tasks.

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Unmanned aerial vehicles are becoming an important part of the modern life. Despite some recent advances in GPS-aided navigation of quadrotors, the concern of crash and collision still overshadows their reliability and safety, especially in GPS-denied environments. Therefore, the necessity for developing fully automatic methods for safe, accurate, and independent landing of drones increases over time. This paper investigates the autolanding process by focusing on an accurate and continuous position estimation of the drone using a monocular vision system and the fusion with the inertial measurement unit and ultrasonic sensors' data. An ARUCO marker is used as the landing pad, and the information is processed in the ground station through a real-time Wi-Fi link. In order to overcome the closed loop instability caused by the communication and localization delays, we propose a method called "movement slicing method". This method divides the moves around the marker into moving and waiting slices and makes the landing process not only more accurate but also faster. Experimental results show a successful landing of the UAV on a predefined location, while it is accurately aligned with the marker using the proposed method.