Search

Abstract Considering the nonlinear characteristics such as backlash hysteresis and coupled motion commonly exist in cable-driven mechanism of laparoscopic surgical robot end-effector, it is a great challenge to control the motion of robotic end-effector precisely during the surgical procedure. Due to the effects of coupled motion, the surgical end-effector will not move accurately as surgeons expected. Previous studies mostly focused on the design of special compensation mechanisms and software compensation algorithms to solve coupled motion problem. However, these approaches are limited because the backlash hysteresis is ignored and the mechanism of end-effector is restricted. This paper shows an improved scheme to eliminate the coupled motion of end-effector and reduce the position tracking error. The proposed decoupling scheme is conducted in three stages. Firstly, the time and frequency domain information of the driving motor current and the motion information of surgical instrument are extracted in real-time. Thereafter, a feedforward neural network is designed to identify the movement stage of end-effector. Finally, a prediction model is designed to predict the coupling error, after that the coupling error can be eliminated by using feedforward compensation control. An experimental platform was set up to verify the effectiveness of the proposed control scheme, and the results of corresponding comparative experiments revealed that the proposed strategy can substantially improve the tracking accuracy. Highlights • A novel scheme for eliminating the coupled motion of surgical robots' end-effector. • Considering the influence of backlash hysteresis of the end-effector. • Applicable to different instruments of cable-driven surgical robots. • Does not increase the number of mechanical parts or assembly costs. • A model is designed to predict the coupling error of the end-effector in real-time. [ABSTRACT FROM AUTHOR]

This paper describes a novel approach for the autonomous localization of radioactive sources in complex unknown environments. The approach contains two stages: a source tracking stage for detailed source term estimation (STE) with recent measurements, and a relocation stage for transiting the robot to another sub-area after the current one is ruled out. The STE algorithm in conjunction with the convex polyhedrons extracted from the environment enables our method to be deployed in realistic scenarios with complex obstacles, which are often unfeasible for existing methods. The source confirmation algorithm in the relocation stage considerably accelerates the overall processing, improving the time efficiency in finding the radioactive sources. In both simulations and experiments, this approach is able to precisely localize at the success rate of 90% within an average of less than 13 measurements in 7 different environments, which is impressive when compared to the traditional STE which can hardly achieve an accurate estimation in most cases. • Dual stage planner for source search in practical unknown environment. • Divide complex world into perfect obstacle-free space with convex polyhedron. • Pseudo-Source confirmation based on particle filter and information entropy. [ABSTRACT FROM AUTHOR]

The letter presents an online obstacle avoidance strategy for a nonholonomic mobile manipulator, including dual-trajectory generation for the mobile base(MB) and end effector(EE), as well as whole-body reactive obstacle avoidance. The feasible region of the manipulator's workspace is generated by traversing the path of the MB, and the spatio-temporal trajectory of the EE is generated by quadratically constrained quadratic programming(QCQP) planning, satisfying the physical constraints and obstacle avoidance constraints. The reactive obstacle avoidance strategy is based on quadratic programming(QP), using singularity avoidance and EE trajectory tracking tasks as soft constraints with different priorities. Considering the nonholonomic constraints and motion differences of MB, we employ a dynamic tracking approach to avoid obstacles of MB. Additionally, an adaptive weight method is utilized to adjust the priority of MB path tracking task, effectively preventing joint velocity jitter during the insertion process. Our strategy has been proven to generate obstacle avoidance trajectories for EE in real-time and ensure safe joint velocities for MB and the manipulator through simulation and real-world scenarios. • A real-time generation algorithm for spatio-temporal obstacle avoidance trajectories of EE is proposed, which also satisfies the workspace constraints. • A reactive obstacle avoidance strategy is designed for nonholonomic MB obstacle avoidance, incorporating adaptive weights. • Our proposed algorithm is deployed into a nonholonomic mobile manipulator control system successfully. [ABSTRACT FROM AUTHOR]

We present an autonomous approaching scheme for mobile robot traversing obstacle stairwells, which overcomes the restricted field of vision caused by obstacles. The scheme consists of three parts: stair localization, structural parameter estimation, and nearing the stair. We use LIDAR with full-angle and long-distance measurement capabilities to initially determine the location of the stairs, and estimate staircase parameters using a depth camera with intensive data. Then, we propose an algorithm to optimize the location of stairs for real-time correction of target point locations during approaching. Finally, we conducted tests to verify the accuracy of the estimated stair positions and structural parameters and performed a comprehensive experiment to demonstrate the effectiveness of the scheme, in which the robot approaches autonomously to the front of the stairs around multiple obstacles. • We develop the double scales normal and selection (DSNS) algorithm to identify stair from sparse point clouds. • We develop the multi-plane joint recognition (MPJR) algorithm to estimate the structural parameter. • We propose the sliding window multi-round voting mechanism (SWMRVM) algorithm to optimize the stair position from recognition. [ABSTRACT FROM AUTHOR]

This paper focuses on the multiple radiation source search problems, where the mobile robot identifies the number and parameters of sources online while exploring an unknown environment. The radiation superposition and the limited observations improve the difficulty of estimation, and the exploration trajectory is also associated with estimation. A novel search strategy based on receding horizon planning is proposed, which includes the observation, estimation, and exploration modules. The observation module filters and records the radiation intensity for estimation. In the estimation module, an adaptive differential evolution algorithm is integrated into the peak suppression particle filter to avoid the local optimum. The multi-source radiation gain model is conceived to determine the observation position in the exploration module. The strategy trades off exploration of unknown areas and exploitation of known radiation fields. The results of simulations and experiments demonstrate that the proposed strategy can identify the parameters and quantities of all sources in multi-modal radiation fields. Furthermore, our strategy exhibits superior performance in searching for multi-radiation sources in unknown environments compared with the boustrophedon path and the Next-Best-View planner. • A robot search strategy is proposed and evaluated for unknown radiation environments. • Combine ADE optimization with the PSPF algorithm to improve estimation efficiency. • An exploration scheme is proposed to balance exploration and exploitation. [ABSTRACT FROM AUTHOR]

So far, gas leakage caused by natural or human factors has led to serious consequences in terms of social security. Previous strategies for locating the odor sources appear to be either defective or incomplete. For enhancing the success rate and rapidity, this paper aims to present a novel and complete strategy in search of lurking gas sources. Particle filtering and information entropy are both employed to track the plume information. To improve the tracking efficiency in this process, a novel objective function is designed by considering the entropy gains of the suspected targets as well as the repeated exploration scores. Considering the pseudo sourced caused by obstacles, a statistics-based source determine algorithm is proposed to confirm the source's authenticity, while the artificial potential field method is subsequently applied to eliminate the distractions introduced by the pseudo sources. Simulations and on-site tests are both carried out while results showed that the proposed scheme is competent to complete sources localization task in the scene that contains randomly distributed obstacles and pseudo source. • Gas source localization with ground robot in environment with obstacles and winds. • Gas source localization algorithm based on particle filter and information entropy. • Information-entropy-based gas search algorithm without repeated exploration. • Identify pseudo source and handle it with artificial potential field. [ABSTRACT FROM AUTHOR]