Power-efficient adaptive behavior through a shape-changing elastic robot

The adaptive morphology of a robot, such as shape adaptation, plays a significant role in adapting its behaviors. Shape adaptation should ideally be achieved without considerable cost, like the power required to deform the robot’s body, and therefore, it is reasonably considered as the last resort in classical rigid robots. However, the last decade has seen an increasing interest in soft robots: robots that can achieve deformability through their inherent material properties or structural compliance. Nevertheless, the dynamics of these types of robots is often complex and therefore it is difficult to substantiate whether the cost like the required power for changing its shape will be worthwhile to achieve the desired behavior. This article presents an approach in the development and analysis of a shape-changing locomoting robot, which relies on the ability of elastic beams to deform and vibrate. Through a proper use of elastic materials and the robot’s vibration-based dynamics, it will be shown both analytically and experimentally how shape adaptation can be designed such that it leads to desirable behaviors, with better power efficiency compared to when the robot solely relies on changing its control input. The results encourage emerging direction in robotics that investigates approaches to change robots’ behaviors through their adaptive morphology.

Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement

By virtue of high-resolution remote sensing satellites, there is a possibility to analyze remote sensing images on water bodies through digital image processing (DIP). In many remote sensing images, however, the water bodies have similar gray values as other ground objects. To effectively distinguish water bodies from other ground objects in these images, this paper proposes a logarithmic enhancement method for remote sensing images on water bodies based on adaptive morphology. The proposed method can filter the noise of non-target area, and enhance the water body in the original image. On this basis, a morphology-based segmentation method was designed for remote sensing images on water bodies. Experimental results show that our method achieved a high segmentation accuracy, controlling the mean segmentation error at below 1.32%.

Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions

This paper proposes a way of better approximating continuous, two-dimensional morphology in the discrete domain, by allowing for irregularly sampled input and output signals. We generalize previous work to allow for a greater variety of structuring elements, both flat and non-flat. Experimentally we show improved results over regular, discrete morphology with respect to the approximation of continuous morphology. It is also worth noting that the number of output samples can often be reduced without sacrificing the quality of the approximation, since the morphological operators usually generate output signals with many plateaus, which, intuitively do not need a large number of samples to be correctly represented. Finally, the paper presents some results showing adaptive morphology on irregularly sampled signals.

PedestriANS: a bipedal robot with adaptive morphology

In diverse situations, humans produce natural and adaptable bipedal locomotion by cooperatively manipulating the interactions among the different parts of their bodies and the environment. Therefore, to realize a robot with adaptable behavior, it should be enabled to adjust its morphology accordingly in response to environmental changes. From this perspective, this study introduces the development of a bipedal robot with adaptive morphology. By implementing an actuator network system (ANS), the robot is able to manipulate the physical characteristics of its legs and the way they interact with each other. Two experiments have been conducted: main and supplementary experiments. The main experiment examined how effective is adjusting the robot’s morphology on changing the robot’s behavior. The experiment was conducted on different ground materials and under different connection patterns between the robot’s legs. During the experiment, the robot’s behavior was evaluated in reference to four aspects: walking style, stability, speed, and moving direction. The supplementary experiment took the results of the main experiment and used it to improve the robot’s behavior during locomotion. The robot was enabled to automatically switch between the different connection patterns of the ANS, which in turn changed the interaction between the robot’s legs and generated a more suitable dynamics for the surrounding environment.