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An increasing number of grounded robots are being used in prostate interventions to improve clinical outcomes, but their large size and high-cost limit their popularity. Thus, we present a hand-held 3-degree of freedom (DoF) parallel robot with a-remote center of motion (RCM) for minimally invasive prostate biopsy applications, combining the flexibility of hand-held devices with the precision of robotic assistance. First, the kinematic structure of robotic assistance is introduced according to its design requirements. Then, the kinematic analysis of robotic assistance is carried out by using a simplified kinematic model. The kinematic parameters are designed according to the desired workspace. A prototype has been developed and validated in animal experiments. Twenty beagles of different sizes were selected for the robot-assisted and controlled experiments, resulting in target errors of 3.30 ± 1.63 mm and 5.40 ± 1.76 mm, respectively. The error of robot-assisted experiments was significantly better than in controlled experiments. Preliminary animal tests have demonstrated that the hand-held robot can improve the accuracy of free-hand biopsy punctures.

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Based on the maximum likelihood estimation principle, we derive a collaborative estimation framework that fuses several different estimators and yields a better estimate. Applying it to compressive sensing (CS), we propose a collaborative CS (CCS) scheme consisting of a bank of K CS systems that share the same sensing matrix but have different sparsifying dictionaries. This CCS system is expected to yield better performance than each individual CS system, while requiring the same time as that needed for each individual CS system when a parallel computing strategy is used. We then provide an approach to designing optimal CCS systems by utilizing a measure that involves both the sensing matrix and dictionaries and hence allows us to simultaneously optimize the sensing matrix and all the K dictionaries. An alternating minimization-based algorithm is derived for solving the corresponding optimal design problem. With a rigorous convergence analysis, we show that the proposed algorithm is convergent. Experiments are carried out to confirm the theoretical results and show that the proposed CCS system yields significant improvements over the existing CS systems in terms of the signal recovery accuracy.