Teleoperated techniques enable remote human-robot interaction and have been widely accepted in robot-assisted surgeries. However, it is still hard to guarantee the safety of teleoperated surgery due to the imperfect input commands limited by remote perception, preventing teleoperated surgery from being widely used. We propose a new framework to avoid the collision of surgery robots and human tissue caused by inaccurate inputs. We directly take the medical volume data and propose to use the implicit neural field to guide teleoperated robot-assisted surgery. With guidance, the trajectory of the robot manipulator is optimized to safely work inside a narrow workspace. We evaluated our method in several aspects and conducted a real-world experiment on a head phantom. Experimental results show that our proposed method can effectively avoid the collision between the surgical tool and the human tissue during teleoperation.

Haptic feedback is widely used to enhance realism in virtual reality (VR). Shape and softness are two common factors perceived by the users in the haptic rendering of soft objects. To integrate these factors, we propose a new handheld shape-changing device, TapeTouch, to provide various shapes and softness in real time. TapeTouch is based on a controllable shape-changing tape, which is mainly composed of four motors and a section of brass tape. We design a structure of the components to fit a portable controller and allow to flexibly adjust the shape of the brass tape. After decoding desired shapes into the signals to control the motor, we automatically reproduce varying shapes and levels of softness to the finger or palm touching the shape-changing tape. We conducted two user studies to understand the capability of TapeTouch to render shape and softness, and the results showed that TapeTouch could provide a variety of distinguishable shapes as well as multiple levels of softness. Based on the results, we performed two VR experience studies to verify that the haptic feedback from TapeTouch enhances VR realism.

In this paper, a novel optimization method is proposed to minimize the fuel consumption for plug-in hybrid electric vehicles (PHEVs). First, the quadratic relationship between fuel rate and battery power is constructed. Then, based on the above relationship, the optimal battery power sequence can be gained by quadratic programming (QP). In addition, to find the optimal engine on-off command, genetic algorithm is incorporated with the QP for the search of engine on-off command. Finally, the simulation result shows that, compared with the CD/CS method, the proposed strategy provides a better fuel economy with lower computation burden.