International audienceWe present a new interface for minimally invasive surgery (MIS) training that incorporates novel broadband sensory modalities that include visual, force, and thermal technology, into the evolution of the next generation of surgical robotics and simulators. A new haptic device is designed to provide high force and torque capabilities for a better touch feedback. Part of the surgical tool is kept to be the real grasper (i.e., the handle) of the haptic interface. Yet, our main novel contribution is in integrating thermal feedback in MIS applied perspectives; indeed, thermal sensing finds particular utility in detecting and isolating unstable arterial plaque and tumors. In addition, thermal energy is used in several therapeutic procedures such as tumor ablation or tissue welding. We propose several thermal exchange models based on the Pennes'bioheat transfer equation. The overall haptic interface (force and thermal display) is interfaced with an open source virtual reality simulator (the SOFA framework). We added in SOFA the necessary models dealing with thermal simulation using built-in data structure and methods. The integration is successful, and realistic simulation scenarios combining visual, thermal, and force feedback were achieved. Results using the overall simulation are presented and evaluate

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The electromyography (EMG) and electroencephalography (EEG) are two frequently used modalities of bio-signals in the field of bio-robotics. However, it is insufficient to use a single isolated modality, due to the presence of artifacts, the lack of information, etc. To solve the problem, this paper proposes an EEG/EMG data fusion method that take advantages of both signals and overcome their drawbacks to achieve accurate identification of movements. The two types of bio-signals were preprocessed through wavelet packets transform (WPT) and classified by the artificial neural network (ANN). Then, the belief theory was introduced to allow for model uncertainty and imprecision, which adapts to the ambiguities and conflicts between sources. Experimental results show that the proposed EEG/EMG data fusion method outperformed the strategies based on only one modality. The research findings provide new impetus to bio-robotic applications.

We propose a new method for thermal rendering in telepresence systems which allows an operator to feel most transparently the thermal behavior of the remote object. It is based on learning and thermal heat flux generation. Two databases are constructed from real measurements recorded during direct contact between operator's finger and different materials. One database is used in order to identify the material in the slave side, the other database is used to generate desired heat flux for the thermal display loop. The identification bloc is based on Principal Component Analysis and Neural Network. Experimental results validating the proposed method are discussed.

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In this paper, a nonlinear robust Fast Terminal Sliding Mode Controller (FTSMC) is designed to control and stabilize a new reconfigurable Unmanned Aerial Vehicle (UAV) in the presence of uncertain and variable parameters. The studied UAV is an over-actuated system due the number of actuator control inputs. It can modify the length and the angles between its four arms in different ways, which result an important variation in its Center of Gravity (CoG), inertia, and control matrix. The proposed FTSMC offers many advantages such as, reaching the desired states in a finite-time unlike the conventional sliding mode, robustness vis-a-vis uncertain and unknown parameters, fast convergence towards the sliding surface, high accuracy and reducing the chattering phenomena. Furthermore, the closed-loop stability of the this UAV is ensured by the Lyapunov theory. The eight actuators used to rotate and extend the UAV arms are controlled by simple Proportional Integral Derivative (PID) controllers. Lastly, the robustness and efficiency of the proposed controller are evaluated through a flight scenario, where the UAV geometric parameters are variable over time.

Recently, reconfigurable drones have gained particular attention in the field of automation and flying robots. Unlike the conventional drones, they are characterized by a variable mechanical structure in flight, geometric adaptability, aerial reconfiguration, high number of actuators and control inputs, and variable mathematical model. In addition, they are exploited to flight in more cluttered environments, avoid collisions with obstacles, transport and grab objects, cross narrow and small spaces, decrease different aerial damages, optimize the consumed energy, and improve agility and maneuverability in flight. Moreover, these new drones are considered as a viable solution to provide them with specific and additional functionalities. They are a promising solution in the near future, since they allow increasing considerably the capabilities and performance of classical drones in terms of multi-functionalities, geometric adaptation, design characteristics, consumed energy, control, maneuverability, agility, efficiency, obstacles avoidance, and fault tolerant control. This paper explores very interesting and recent research works, which include the classification, the main characteristics, the various applications, and the existing designs of this particular class of drones. Besides, an in-depth review of the applied control strategies will be presented. The links of the videos displaying the results of these researches will be also shown. A comparative study between the different types of flying vehicles will be established. Finally, several new challenges and future directions for reconfigurable drones will be discussed.

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