This paper presents the design, analysis, and performance evaluation of an omnidirectional transformable wheel-leg robot called OmniWheg. We design a novel mechanism consisting of a separable omni-wheel and 4-bar linkages, allowing the robot to transform between omni-wheeled and legged modes smoothly. In wheeled mode, the robot can move in all directions and efficiently adjust the relative position of its wheels, while it can overcome common obstacles in legged mode, such as stairs and steps. Unlike other articles studying whegs, this implementation with omnidirectional wheels allows the correction of misalignments between right and left wheels before traversing obstacles, which effectively improves the success rate and simplifies the preparation process before the wheel-leg transformation. We describe the design concept, mechanism, and the dynamic characteristic of the wheel-leg structure. We then evaluate its performance in various scenarios, including passing obstacles, climbing steps of different heights, and turning/moving omnidirectionally. Our results confirm that this mobile platform can overcome common indoor obstacles and move flexibly on the flat ground with the new transformable wheel-leg mechanism, while keeping a high degree of stability.

From a medical standpoint, detecting the size and shape of hard inclusions hidden in soft three-dimensional objects is of great significance for early detection of cancer through palpation. Soft robots, especially soft grippers, substantially broaden robots' palpation capabilities from soft to hard materials without the assistance of a camera. We have recently introduced a CNN-Bayes approach which added a Naïve Bayes classifier to a convolutional neural network (CNN) architecture called SoftTactNet for variable stiffness object recognition on a three-finger FinRay soft gripper. SoftTactNet itself lacks uncertainty estimations though it can reach a certain level of recognition accuracy. In this paper, we further improve the framework by merging Bayes method directly into CNN architectures and build a new Bayes-SoftTactNet for object recognition. The new approach, using a prior distribution instead of point estimation, allows the network to present results with uncertainty estimates. We conduct new experiments using the same soft gripper with tactile sensor arrays to grasp different variable stiffness objects surrounded by non-different soft material and generate tactile images as dataset. The results show that our new algorithm is more efficient than the previous approach and still able to achieve higher recognition accuracy than general deterministic CNNs.

Autonomous Valet Parking (AVP) in an under- ground garage is an emerging smart vehicle solution that the community believes to be solvable with close-to-market sensors. Absence of GPS signals and a high degree of self-similarity however render global visual localization in such environments a highly challenging problem. We present a novel underground parking localization method that relies on text recognition in the wild as well as optical character recognition (OCR) to automatically detect parking slot numbers. The detected numbers are then correlated with both geometric as well as semantic information extracted from an offline map of the environment. The resulting measurement model is embedded into a probabilistic Monte-Carlo localization framework. The success of our method is demonstrated on multiple real-world sequences in one of the largest underground parking garages in Shanghai.

Evolutionary algorithms have previously been applied to the design of morphology and control of robots. The design space for such tasks can be very complex, which can prevent evolution from efficiently discovering fit solutions. In this article we introduce an evolutionary-developmental (evo-devo) experiment with real-world robots. It allows robots to grow their leg size to simulate ontogenetic morphological changes, and this is the first time that such an experiment has been performed in the physical world. To test diverse robot morphologies, robot legs of variable shapes were generated during the evolutionary process and autonomously built using additive fabrication. We present two cases with evo-devo experiments and one with evolution, and we hypothesize that the addition of a developmental stage can be used within robotics to improve performance. Moreover, our results show that a nonlinear system-environment interaction exists, which explains the nontrivial locomotion patterns observed. In the future, robots will be present in our daily lives, and this work introduces for the first time physical robots that evolve and grow while interacting with the environment.

Feline locomotion combines great acrobatic proficiency, unparalleled balance and higher accelerations than other animals. Capable of accelerating from 0 to 100 km h-1in three seconds, the cheetah (Acinonyx jubatus) is still a mystery which intrigues scientists. Aiming for a better understanding of the source of such higher speeds, we develop a biomimetic platform, where musculoskeletal parameters (range of motion and moment arms) from the biological system can be evaluated with air muscles within a lightweight robotic structure. We performed experiments validating the muscular structure during a treadmill walk, successfully reproducing animal locomotion while adopting an EMG based control method.