Recent advances in miniaturised, dynamically actuated robots have opened new pathways for non-visual, in-situ disease diagnosis. This study explores a novel method for early bowel cancer detection using a self-propelled robotic capsule that navigates the bowel and detects lesions based on variations in tissue stiffness. The approach capitalises on the sensitivity of the capsule’s dynamic responses to surrounding tissue properties. A dual-phase machine learning framework is proposed. The first phase uses regression models including multilayer perceptron (MLP), support vector regression (SVR), and Gaussian process regression (GPR) to predict tissue stiffness from displacement signal features. The second phase uses a Gaussian mixture model (GMM) to cluster the predicted stiffness values into different categories. Unlike our previous work, this study emphasises the robustness of the models under varying data conditions using both accuracy and reliability-oriented metrics. Based on our studies, MLP provided the most reliable regression results for simulated data and downstream clustering, though GPR performed better on experimental datasets. SVR consistently underperformed, especially on experimental data. The GMM achieved over 89% clustering accuracy across both simulated and experimental datasets, with improved results when predictions from more accurate regression models are used as the inputs. This work demonstrates a promising step toward dynamic, in-situ lesion characterisation and highlights the potential for integrating lesion biomechanics into future endoscopic diagnosis.

This paper presents a novel design and experimental investigation for a self-propelled capsule robot that can be used for painless colonoscopy during a retrograde progression from the patient's rectum. The steerable robot is driven forward and backward via its internal vibration and impact with orientation control by using an electromagnetic actuator. The actuator contains four sets of coils and a shaft made by permanent magnet. The shaft can be excited linearly in a controllable and tilted angle, so guide the progression orientation of the robot. Two control strategies are studied in this work and compared via simulation and experiment. Extensive results are presented to demonstrate the progression efficiency of the robot and its potential for robotic colonoscopy.

As a rotary-percussion system, the vibro-impact drilling (VID) system utilises resonantly induced high frequency periodic impacts alongside existing drill-string rotation to cut through downhole rock layers. Due to the inhomogeneous nature of the rock layers, the system often experiences multi-stability which generates different categories of impact motions as drilling continues downhole. Some impact motions yield better drilling performance in terms of rate of penetration (ROP) and bit life-span when compared to others. As an optimisation strategy, the present study adopts feature-based classification algorithms including multi-layer perceptron, support vector machine and long short-term memory network as intelligent models for categorising impact motions from a one-degree-of-freedom impact oscillator representing the percussive bit-rock impacts of the VID system. This way, high-performance impacts can be easily detected and maintained while undesirable low-performance impacts are well avoided to increase ROP, improve bit life-span and save cost. In this study, scarce and limited classes of experimental impact data are merged with inexhaustibly simulated impact data to train different network models. By means of cross-validation, the trained networks were tested on separate sets of only-simulation and only-experimental data. Results show that extracting appropriate features from raw impact data is essential for optimising the performance of each network model. About 42% of the feature-based networks yield accuracies greater than 91% while about 67% yield accuracies greater than 77% on both simulation and experimental impact motion data.

In this paper, we use an example - a pendulum-driven cart-pole system studied in Li et al. (2005) to investigate the tracking control of underactuated dynamic systems. In this paper, we propose a six-step motion strategy of the pendulum driven cart-pole system. We design a desired profile of the pendulum joint velocity based on the proposed six-step motion strategy. Based on the desired joint velocity profile, we can compute the desired joint position and acceleration. The desired joint trajectories will be used in the proposed control approach. We also propose a closed-loop control approach using the partial feedback linearization technique. Extensive simulation studies are conducted to demonstrate the proposed approaches.