MOTIVATION: Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics. RESULTS: We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications. AVAILABILITY AND IMPLEMENTATION: MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.

In a graph search algorithm, a given environment is represented as a graph comprising a set of feasible system configurations and their neighboring connections. A path is generated by connecting the initial and goal configurations through graph exploration, whereby the path is often desired to be optimal or suboptimal. The computational performance of the optimal path generation depends on the avoidance of unnecessary explorations. Accordingly, heuristic functions have been widely adopted to guide the exploration efficiently by providing estimated costs to the goal configurations. The exploration is efficient when the heuristic functions estimate the optimal cost closely, which remains challenging because it requires a comprehensive understanding of the environment. However, this challenge presents the scope to improve the computational efficiency over the existing methods. Herein, we propose reinforcement learning heuristic A* (RLHA*), which adopts an artificial neural network as a learning heuristic function to closely estimate the optimal cost, while achieving a bounded suboptimal path. Instead of being trained by precomputed paths, the learning heuristic function keeps improving by using self-generated paths. Numerous simulations were performed to demonstrate the consistent and robust performance of RLHA* by comparing it with the existing methods.

Hand–eye and robot–world calibration is a problem in which the unknown homogeneous transformations$X$and$Y$must be estimated for a loop closure equation$AX = YB$for a set of transformation measurement pairs$\lbrace (A_{i}, B_{i}) \rbrace$. Previous studies on$AX=YB$have mainly relied on linear least-squares minimization followed by nonlinear iterative optimization for solution refinement to minimize the distances between$A_{i} X$and$Y B_{i}$. However, these methods have not been fully clarified, particularly in terms of calibration dependence on the coordination of$A,B,X$, and$Y$along the system loop, as well as the underlying noise distributions of$A_{i}$and$B_{i}$. They also lack flexibility in the noise properties of individual measurements; thus, they cannot incorporate the relative reliability between measurements. To address these limitations, we propose a probabilistic framework for hand–eye and robot–world calibration. The proposed framework clarifies the unclear aspects of existing methods by revealing their underlying assumptions regarding system noise. Consequently, it identifies the applicability of distance minimization to a given calibration problem and provides the optimal coordination of transformations for distance minimization. For cases in which distance minimization is inapplicable, an iterative algorithm for the maximum likelihood estimation is proposed, whereby the different noise properties of individual measurements can be accounted for. An estimation uncertainty analysis is presented for the proposed iterative algorithm to quantify the expected estimation accuracy. The presented theories and the proposed algorithm are validated using a set of numerical and hardware experiments. The code for the iterative algorithm and the estimation uncertainty is available athttps://github.com/hjhdog1/probabilisticAXYB.

The undulatory locomotion of snakes is one unique wonder of mechanical motions in nature. Traditionally, snakes were thought to push surrounding objects to propel their motion. However, this does not explain their forward locomotion and sidewinding on a flat surface. Recent studies have shown that the snake's forward locomotion and sidewinding are aided by a nonuniform ground-contact distribution. In this article, we propose a novel control strategy to achieve nonuniform ground contacts for a robotic snake locomotion using body compliance and uniform body tension. First, we present a set of mechanical analyses for a continuum snake model. Then, we demonstrate that the deflection under gravity resulting from body compliance and uniform body tension naturally yields the contact distributions required for forward and backward locomotion and sidewinding. Finally, a simple control strategy is suggested for a robotic snake locomotion, which was validated through dynamic simulations and physical experiments.

In this paper, we proposed a novel path replanning algorithm on arbitrary graphs. To avoid computationally heavy preprocessing and to reduce required memory to store the expanded vertices of the previous search, we defined the feature vertices, which are extracted from the previous path by a simple algorithm to compare the costs between adjacent vertices along the path once. Proper additional heuristic functions are designed for these feature vertices to work as local attractors guiding the search toward the previous path’s neighbors. To avoid unnecessary expansions and speed up the search, these additional heuristic functions are properly managed to stop intriguing or guiding search toward the feature vertices. The proposed algorithm of Fast Replanning Multi-Heuristic A* is a variation of Shared Multi-Heuristic A* while removing or deactivating the additional heuristic functions during the search. Fast Replanning Multi-Heuristic A* guarantees the bounded suboptimality while efficiently exploring the graph toward the goal vertex. The performance of the proposed algorithm was compared with weighted A* and D* lite by simulating numerous path replanning problems in maze-like maps.

This paper presents a novel continuum robot sheath for use in single-port minimally invasive procedures such as neuroendoscopy in which the sheath is designed to deliver multiple robotic arms. Articulation of the sheath is achieved by using precurved superelastic tubes lining the working channels used for arm delivery. These tubes perform a similar role to push/pull tendons, but can accomplish shape change of the sheath via rotation as well as translation. A kinematic model using Cosserat rod theory is derived which is based on modeling the system as a set of eccentrically aligned precurved tubes constrained along their length by an elastic backbone. The specific case of a two-arm sheath is considered in detail and its relationship to a concentric tube balanced pair is described. Simulation and experiment are used to investigate the concept, map its workspace and to evaluate the kinematic model.

The shape of a concentric tube robot depends not only on the relative rotations and translations of its constituent tubes, but also on the history of relative tube displacements. Existing mechanics-based models neglect all history-dependent phenomena with the result that when calibrated on experimental data collected over a robot's workspace, the maximum tip position error can exceed 8 mm for a 200-mm-long robot. In this paper, we develop a model that computes the bounding kinematic solutions in which Coulomb friction is acting either to maximize or minimize the relative twisting between each pair of contacting tubes. The path histories associated with these limiting cases correspond to first performing all tube translations and then performing relative tube rotations of sufficient angle so that the maximum Coulomb friction force is obtained along the interface of each contacting tube pair. The robot tip configurations produced by these path histories are shown experimentally to bound position error with respect to the estimated frictionless model compared to path histories comprised of translation or mixed translation and rotation. Intertube friction forces and torques are computed as proportional to the intertube contact forces. To compute these contact forces, the standard zero-clearance assumption that constrains the concentrically combined tubes to possess the same centerline is relaxed. The effects of clearance and friction are explored through numerical and physical experiments and it is shown that friction can explain much of the prediction error observed in existing models. This model is not intended for real-time control, but rather for path planning-to provide error bounds and to inform how the ordering of tube rotations and translations can be used to reduce the effect of friction.

Mechanics-based formulations of concentric tube robots incorporate tube bending and twisting, but do not include other phenomena that could model observed hysteretic behavior in which tube configurations reached by rotating tubes in different directions achieve different tip positions. As a step toward incorporating hysteretic tube-on-tube friction, this paper derives a model that enables computation of the contact forces applied by the tubes on each other along their lengths. To do so, it is necessary to include the small, but finite clearances between the tubes. Recasting the constrained energy minimization problem as its dual problem enables numerically efficient solution for the clearance-constrained centerlines of each tube as well as their contact forces. These variables are investigated through numerical examples and it is shown that, even without considering friction, the assumption of zero clearance can introduce tip position errors of several millimeters for clinically relevant robot lengths.

Robotic instruments based on concentric tube technology are well suited to minimally invasive surgery since they are slender, can navigate inside small cavities and can reach around sensitive tissues by taking on shapes of varying curvature. Elastic instabilities can arise, however, when rotating one precurved tube inside another. In contrast to prior work that considered only tubes of piecewise constant precurvature, we allow precurvature to vary along the tube's arc length. Stability conditions for a planar tube pair are derived and used to formulate an optimal design problem. An analytic formulation of the optimal precurvature function is derived that achieves a desired tip orientation range while maximizing stability and respecting bending strain limits. This formulation also includes straight transmission segments at the proximal ends of the tubes. The result, confirmed by both numerical and physical experiment, enables designs with enhanced stability in comparison to designs of constant precurvature.

Although existing mechanics-based models of concentric tube robots have been experimentally demonstrated to approximate the actual kinematics, determining accurate estimates of model parameters remains difficult due to the complex relationship between the parameters and available measurements. Further, because the mechanics-based models neglect some phenomena like friction, nonlinear elasticity, and cross section deformation, it is also not clear if model error is due to model simplification or to parameter estimation errors. The parameters of the superelastic materials used in these robots can be slowly time-varying, necessitating periodic re-estimation. This paper proposes a method for estimating the mechanics-based model parameters using an extended Kalman filter as a step toward on-line parameter estimation. Our methodology is validated through both simulation and experiments.

Robot control requires the rapid computation of robot shape, which for continuum robots typically involves solving complex mechanics-based models. Furthermore, shape computation based on kinematic input variables can be inaccurate due to parameter errors and model simplification. An alternate approach is to compute the shape in real-time from a set of sensors positioned along the length of the robot that provide measurements of local curvature, e.g., optical fiber Bragg gratings. This paper proposes a general framework for selecting the number and placement of such sensors with respect to arclength so as to compute the forward kinematic solution accurately and quickly. The approach is based on defining numerically-efficient shape reconstruction models parameterized by sensor number and location. Optimization techniques are used to find the sensor locations that minimize shape and tip error between a reconstruction model and a mechanics-based model. As a specific example, several reconstruction models are proposed and compared for concentric tube robots. These results indicate that the choice of reconstruction model as well as sensor placement can have a substantial effect on robot shape estimation.

Minimally invasive surgery can involve navigating inside small cavities or reaching around sensitive tissues. Robotic instruments based on concentric tube technology are well suited to these tasks since they are slender and can be designed to take on shapes of high and varying curvature along their length. One limitation of these robots, however, is that elastic instabilities can arise when rotating one pre-curved tube inside another. While prior work has considered tubes of piecewise-constant pre-curvature, this paper proposes varying tube pre-curvature as a function of arc length as a means to enhance stability. Stability conditions for a planar tube pair are derived and used to define an optimal design problem. This framework enables solving for pre-curvature functions that achieve a desired tip orientation range while maximizing stability and respecting bending strain limits. Analytical and numerical examples of the approach are provided.