Magnetic drug targeting is a promising technique that can deliver drugs to the diseased region, while keeping the drug away from healthy parts of body. Introducing a human in the control loop of a targeted drug delivery system and using inherent bilateralism of a haptic device at the same time can considerably improve the performance of targeted drug delivery systems. In this paper, we suggest a novel intelligent haptic guidance scheme for steering a number of magnetic nanoparticles (MNPs) using forbidden region virtual fixtures and a haptic rendering scheme with multi particles. Forbidden region virtual fixtures are a general class of guidance modes implemented in software, which help a human-machine collaborative system accomplish a specific task by constraining a movement into limited regions. To examine the effectiveness of our proposed scheme, we implemented a magnetic guided drug delivery system in a virtual environment using a physics-based model of targeted drug delivery including a multi-branch blood vessel and realistic blood dynamics. We performed user studies with different guidance modes: unguided, semi virtual fixture and full virtual fixture modes. We found out that the efficiency of targeting was significantly improved using the forbidden region virtual fixture and the proposed haptic rendering of MNPs. We can expect that using intelligent haptic feedback in real targeted drug delivery systems can improve the targeting efficiency of MNPs in multi-branch vessels.

This paper investigates the track‐to‐track state estimation for a class of linear time‐varying multisensory systems. We propose a novel low‐complexity reduced‐order filter (ROF) under the Kalman filtering framework. Unlike the majority of previous track‐to‐track strategies, the proposed fusion strategy applies only to special variables or required components that contain critical information about a target system of interest. Also, unlike existing suboptimal fusion filters such as the covariance intersection, the proposed ROF algorithm makes use of nonzero cross‐covariances between local filters that greatly improve its estimation accuracy. The theoretical aspect of ROF application to multisensory systems with identical sensors is also thoroughly investigated. Finally, we show the effectiveness and accuracy of the ROF when applied to objects (including a drone) performing a two‐dimensional maneuver using numerical simulations.

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