Animal behavior is an essential element in neuroscience study and noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. However, extracting detailed poses without markers in dynamically changing backgrounds has been a challenge. Transparent Omnidirectional Locomotion Compensator (TOLC), a tracking device, was recently developed to investigate longitudinal studies of a wide range of behavior in an unrestricted walking Drosophila without tethering and the conventional image segmentation method has been used to identify the centroids of the walking Drosophila. Since the shape or morphological features of the pixel-wise mask may vary depending on the captured images, however, the centroid calculation errors could occur when segmenting the walking Drosophila. To solve the problem, DeepLabCut, an open-source deep-learning toolbox performing markerless pose estimation on a sequence of images for quantitative behavioral analysis, was utilized to find the centroids of Drosophila melanogaster in a video recorded by TOLC. One hundred labeled images with centroids were created for the training of ResNet50 among 60,984 images and used for predicting 5,000 images in the experiment. The results of the experiment showed that the centroids predicted by the deep learning model are more accurate than the centroids from the morphological features in a specific part of the sequence of the images. Additionally, we created 200 labeled images with legs for the training of ResN et50 and predicted 5,000 images to investigate the difference between the centroids of a Drosophila melanogaster over the locations of the legs. The centroids generated from morphological features often provide incorrect information when the Drosophila melanogaster stretches out the front legs for some regions. Detailed analysis of experiment results and the future research direction with more extensive experiments are discussed.

We demonstrate three-dimensional control with the eukaryotic cell Tetrahymena pyriformis (T. pyriformis) using two sets of Helmholtz coils for xy-plane motion and a single electromagnet for vertical motion. T. pyriformis is modified to have artificial magnetotaxis with internalized magnetite. Since the magnetic fields exerted by electromagnets are relatively uniform in the working space, the magnetite exerts only torque, without translational force, which enabled us to guide the cell's swimming direction while the swimming force is exerted only by the cell's motile organelles. A stronger magnetic force was necessary to steer cells to the z-axis, and, as a result, a single electromagnet placed just below our sample area is utilized for vertical motion. To track the cell's positions in the z-axis, intensity profiles of non-motile cells at varying distances from the focal plane are used. During vertical motion along the z-axis, the intensity difference from the background decreases while the cell size increases. Since the cell is pear-shaped, the eccentricity is high during planar motion, but lowers during vertical motion due to the change in orientation. The three-dimensional control of the live organism T. pyriformis as a cellular robot shows great potential to be utilized for practical applications in microscale tasks, such as target transport and cell therapy.

The use of live microbial cells as microscale robots is an attractive premise, primarily because they are easy to produce and to fuel. In this paper, we study the motion control of magnetotactic Tetrahymena pyriformis cells. Magnetotactic T. pyriformis is produced by introducing artificial magnetic dipole into the cells. Subsequently, they can be steered by using an external magnetic field. We observe that the external magnetic field can only be used to affect the swimming direction of the cells, while the swimming velocity depends largely on the cells' own propulsion. Feedback information for control is obtained from a computer vision system that tracks the cell. The contribution of this paper is twofold. First, we construct a discrete-time model for the cell dynamics that is based on first principle. Subsequently, we identify the model parameters using the Least Squares approach. Second, we formulate a model predictive approach for feedback control of magnetotactic T. pyriformis. Both the model fitness and the performance of the feedback controller are verified using experimental data.

In this paper, we present a control strategy using real-time feedback combined with feasible path planning to manipulate a type of microorganism, Tetrahymena pyriformis (T. pyriformis), as a micro-bio-robot using artificial magnetotaxis. Artificially magnetotactic T. pyriformis cells were created by the internalization of iron oxide nano particles. Following the magnetization of the internalized particles, the cells become controllable using an external time-varying magnetic field. The behavior of artificially magnetotactic T. pyriformis under a magnetic field has been investigated in a manual control experiment. A feasible path planner called rapidly-exploring random tree (RRT) and a feedback control scheme are implemented to guide the cell to a desired position and orientation. Since the motion of T. pyriformis is nonlinear like that of a car, combining the RRT and feedback control allows the cell to be controlled in 3-dimensional (x, y, ¸) space. In the results, real-time feedback control of T. pyriformis in 3-dimensional space demonstrated the potential of utilizing T. pyriformis as a micro-bio-robot for microscale tasks.

Automatic parking assist system is one of the key technologies in future automobiles. A car-like robot is difficult to control due to the nonholonomic constraints. In this paper, a path planning algorithm based on the slice projection technique, optimized particularly for car-parking is proposed. Collision-free region of a nonholonomic motion can be easily computed by using the slice projection technique. Sufficient collision-free, nonholonomic feasible paths can be directly obtained by computing reachable regions by using the slice projection technique for nonholonomic motions from the initial pose. The proposed planning scheme provide not a single solution, but a candidate solution set. Therefore, the parking path can be easily optimized with respect to performance criteria such as safety, maneuvering, and so on. The simulation results clearly show that the proposed scheme provides various practical solutions.