Enabling reaching capabilities in highly redundant continuum robot arms is an active area of research. Existing solutions comprise of task-space controllers, whose proper functioning is still limited to laboratory environments. In contrast, this work proposes a novel plant-inspired behaviour-based controller that exploits information obtained from proximity sensing embedded near the end-effector to move towards a desired spatial target. The controller is tested on a 9-DoF modular cable-driven continuum arm for reaching multiple setpoints in space. The results are promising for the deployability of these systems into unstructured environments.
This document presents novel datasets, constructed by employing the iCub robot equipped with an additional depth sensor and color camera. We used the robot to acquire color and depth information for 210 objects in different acquisition scenarios. At this end, the results were large scale datasets for robot and computer vision applications: object representation, object recognition and classification, and action recognition.
Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate.
Designing soft robots poses considerable challenges: automated design approaches may be particularly appealing in this field, as they promise to optimize complex multi-material machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution in order to let soft robots (both morphologies and controllers) spontaneously evolve within physically-realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this paper a powerful evolutionary system is put in place in order to perform a broad investigation on the free-form evolution of walking and swimming soft robots in different environments. Three sets of experiments are reported, tackling different aspects of the evolution of soft locomotion. The first two sets explore the effects of different material properties on the evolution of terrestrial and aquatic soft locomotion: particularly, we show how different materials lead to the evolution of different morphologies, behaviors, and energy-performance tradeoffs. It is found that within our simplified physics world stiffer robots evolve more sophisticated and effective gaits and morphologies on land, while softer ones tend to perform better in water. The third set of experiments starts investigating the effect and potential benefits of major environmental transitions (land - water) during evolution. Results provide interesting morphological exaptation phenomena, and point out a potential asymmetry between land-water and water-land transitions: while the first type of transition appears to be detrimental, the second one seems to have some beneficial effects.
Soft actuators are receiving increasing attention from the engineering community, not only in research but even for industrial applications. Among soft actuators, fibre-reinforced Bending Fluidic Actuators (BFAs) became very popular thanks to features such as robustness and easy design and fabrication. However, an accurate modelling of these smart structures, taking into account all the nonlinearities involved, is a challenging task. In this effort, we propose an analytical mechanical model to capture the quasi-static response of fibre-reinforced BFAs. The model is fully 3D and for the first time includes the effect of the pressure on the lateral surface of the chamber as well as the non-constant torque produced by the pressure at the tip. The presented model can be used for design and control, while providing information about the mechanics of these complex actuators.