Recently an active locomotive capsule endoscope (CE) for diagnosis and treatment in the digestive system has been widely studied. However, real-time localization to achieve precise feedback control and record suspicious positioning in the intestine is still challenging owing to the limitation of capsule size, relatively large diagnostic volume, and compatibility of other devices in clinical site. To address this issue, we present a novel robotic localization sensing methodology based on the kinematics of a planar cable driven parallel robot (CDPR) and measurements of the quasistatic magnetic field of a Hall effect sensor (HES) array. The arrangement of HES and the Levenberg-Marquardt (LM) algorithm are applied to estimate the position of the permanent magnet (PM) in the CE, and the planar CDPR is incorporated to follow the PM in the CE. By tracking control of the planar CDPR, the position of PM in any arbitrary position can be obtained through robot forward kinematics with respect to the global coordinates at the bedside. The experimental results show that the root mean square error (RMSE) for the estimated position value of PM was less than 1.13 mm in the X, Y, and Z directions and less than 1.14° in the $\theta$ and $\phi$ orientation, where the sensing space could be extended to ±70 mm for the given 34 × 34 mm² HES array and the average moving distance in the Z-direction is 40 ± 2.42 mm. The proposed method of the robotic sensing with HES and CDPR may advance the sensing space expansion technology by utilizing the provided single sensor module of limited sensible volume.

A Cable-Driven Parallel Robot (CDPR) is a special type of parallel robot actuated by elastic cables instead of rigid links, which brings uncertainties of model nonlinearities and structural friction depending on the payload variation. A precise motion with minimum control effort against an unknown payload has been a challenging task. To conquer it, we propose an adaptive hybrid control approach for the fully-constrained CDPR with an unknown payload in this paper. A Cartesian space position control and a joint space cable force control are designed through inertia estimation to encounter the effect of the uncertain payload. And then, an artificial neural network (ANN) is employed to estimate the end-effector force in Cartesian space for precise force control and consecutive uncertainties compensation. The stability of the proposed controller is proven based on the Lyapunov method and the efficacy is verified by the experiments. As for feasibility results, the proposed adaptive hybrid control-based cable force estimation was evaluated on the Mini CDPR. Compared to the traditional control scheme, the tracking error was decreased approximately 56% and the utilized tension was significantly reduced for the given trajectory tracking test. Furthermore, the investigation of free movements of the helix trajectory and pick-and-place tasks showed that the proposed hybrid adaptive control can control the position and cable force altogether while being able to estimate the inertia of the end-effector. Note to Practitioners —The study was motivated by the control problem of adjusting cable tensions of a cable-driven parallel robot (CDPR) against varying payloads at the end-effector. Several advanced control approaches were proposed for but limited to CDPR model parameter estimation and did not consider varying payload. This paper suggests a practical methodology capable of both self-adaptation to unknown payload inertia and consequently improving tracking control performance for a CDPR. In this paper, we derived an adaptive control model and proved stability to ensure safety. In order to encounter unmodeled uncertainties such as pulley friction and cable characteristics, we utilized an artificial neural network (ANN) trained by experiments. The experimental results could confirm that the proposed scheme can estimate the payload inertia and generate minimized cable tension while maintaining good trajectory tracking performance. In future research, we will show more practical validation for a scale-up CDPR with heavy objects toward logistics or pick-and-place application in practice.

This paper presents a compact-sized haptic device based on a cable-driven parallel robot (CDPR) mechanism for teleoperation. CDPRs characteristically have large workspaces and lightweight actuators. An intuitive and user-friendly remote control has not yet been achieved, owing to the unfamiliar multiple-cable configuration of CDPRs. To address this, we constructed a portable compact-sized CDPR with the same configuration as that of a larger fully constrained slave CDPR. The haptic device is controlled by an admittance control for stiffness adjustment and implemented in an embedded microprocessor-based controller for easy installation on an operator’s desk. To validate the performance of the device, we constructed an experimental teleoperation setup by using the prototyped portable CDPR as a master and larger-size CDPR as a slave robot. Experimental results showed that a human operator can successfully control the master device from a remote site and synchronized motion between the master and slave device was performed. Moreover, the user-friendly teleoperation could intuitively address situations at a remote site and provide an operator with realistic force during the motion of the slave CDPR.

A capsule endoscope (CE) provides noninvasive access to the gastrointestinal tract, offering diagnostic information that cannot be obtained through external imaging alone. However, during the examination inside the stomach, the CE’s posture may change rapidly as it moves within a dynamically deforming organ, making it difficult to determine its orientation using only the onboard camera feedback. To address this problem, this study proposes a method that employs an external array of Hall Effect Sensors (HES) to estimate the capsule’s position and orientation in real time, based on the magnetic field generated by a permanent magnet (PM) embedded inside the capsule, without the need for any additional internal sensors. This approach introduces a unified magnetic actuation and localization framework that enables real-time 5-degree-of-freedom posture estimation using only the internal PM of the capsule. Furthermore, the proposed system features an integrated architecture capable of simultaneous actuation and localization. To enhance system practicality, the sensor module and communication board were combined into a single unit that employs a digital serial communication scheme, eliminating the need for analog to digital conversion of sensing signals. By avoiding additional onboard sensors and employing a PM-based actuation system, the proposed system simplifies hardware configuration by preserving capsule miniaturization and by eliminating the high power consumption and thermal issues associated with electromagnet-based actuation, while maintaining accurate real-time tracking performance. Through an optimization process, the system achieved a position error of less than 2 mm and an angular error within 2° over a sensing range of up to 60 mm. Repeated experiments further validated the system’s effectiveness and reliability under realistic operating conditions, demonstrating its feasibility for compact and clinically applicable active capsule endoscopy systems.