Adaptability is a fundamental yet challenging requirement for mobile robot locomotion. This article presents$\alpha$-WaLTR, a new adaptive wheel-and-leg transformable robot for versatile multiterrain mobility. The robot has four passively transformable wheels, where each wheel consists of a central gear and multiple leg segments with embedded spring suspension for vibration reduction. These wheels enable the robot to traverse various terrains, obstacles, and stairs while retaining the simplicity in primary control and operation principles of conventional wheeled robots. The chassis dimensions and the center of gravity location were determined by a multiobjective design optimization process aimed at minimizing the weight and maximizing the robot's pitch angle for obstacle climbing. Unity-based simulations guided the selection of the design variables associated with the transformable wheels. Following the design process,$\alpha$-WaLTR with an embedded sensing and control system was developed. Experiments showed that the spring suspension on the wheels effectively reduced the vibrations when operated in the legged mode and verified that the robot's versatile locomotion capabilities were highly consistent with the simulations. The system-level integration with an embedded control system was demonstrated via autonomous stair detection, navigation, and climbing capabilities.

Robots capable of traversing flights of stairs play an important role in both indoor and outdoor applications. The capability of accurately identifying a staircase is one of the vital technical functions in these robots. This paper presents a vision-based ascending stair detection algorithm using RGB-Depth (RGB-D) data based on an interpretable model. The method follows the four steps: 1) pre-processing of RGB images for line extraction by applying the dilatation and Canny filters followed by the probabilistic Hough line transform, 2) defining the regions of interests (ROIs) via K- mean clustering, 3) training the initial model based on a support vector machine (SVM) using three extracted features (i.e., gradient, continuity factor, and deviation cost), and 4) building an interpretable model for stair classification by determining the decision boundary conditions. The developed method was evaluated for its performance using our dataset, and the results showed 85% sensitivity and 94% specificity. When the same model was tested on a different test set, the sensitivity and specificity slightly decreased to 80% and 90%, respectively. By shifting the boundary conditions using only a small subset of the new dataset without rebuilding the model, performance was improved to 90% sensitivity and 96% specificity. The presented method is also compared with existing SVM- and neural- network- based methods.

This article presents a new cable-driven underactuated robotic gripper, called TWISTER Hand. It is designed for adaptable grasping of objects in different shapes, weights, sizes, and textures. Each finger of the gripper is made of a compliant and continuum mechanism inspired by an origami design. This design is converted into a computer-aided design (CAD) model and 3-D printed using flexible and rigid polymer composite materials. Two CAD modeling methods for this design are compared in terms of structural stiffness and durability in the printed outcomes. For each design, two soft materials are used for preliminary evaluation of the material effect in these properties. The best combination of the model and material is selected to fabricate the three fingers of the robotic gripper. Each finger has a single cable routed along the structure. All three cables are tied and actuated simultaneously using a single servo motor to generate closing and opening motions in the gripper. TWISTER Hand's adaptable grasping capability is tested using 36 different objects. The robot's grasping performance under object pose uncertainties is also experimentally tested and analyzed. This compact fully integrated gripper can be attached to a robotic arm for various manipulative tasks.

This paper presents a new passive wheel-leg transformation mechanism and its embodiment in a small mobile robot. The mechanism is based on a unique geared structure, allowing the wheel to transform between two modes, i.e., wheel or leg, potentially adapting to varying ground conditions. It consists of a central gear and legs with partial gears that rotate around the central gear to open or close the legs. When fully closed, the mechanism forms a seamless circular wheel; when opened, it operates in the leg mode. The central gear actuated by the driving motor generates opening and closing motions of the legs without using an additional actuator. The number of legs, their physical size, and the gear ratio between the central gear and the partial gears on the legs are adjustable. This design is mechanically simple, customizable, and easy to fabricate. For physical demonstration and experiments, a mobile robotic platform was built and its terrainability was tested using five different sets of the transformable wheels with varying sizes and gear ratios. For each design, the performance with successful wheel-leg transformation, obstacle climbing, and locomotion capabilities was tested in different ground conditions.