Search for: All records

Recent work in fluid-driven soft robots has demonstrated the potential to achieve high power-to-weight ratios, low fabrication costs, and improved safety, making them well suited for interactive tasks. However, the low speed of pneumatic actuation prevents use of these robots in more dynamic tasks. This paper aims to design, characterize, and model a bistable elastomeric actuator for swift tasks (BEAST). This actuator enables both fast actuation and mechanical compliance, and is designed by integrating silicone and polyethylene terephthalate (PET) in a bendy straw structure. The BEAST contains three states - compressed, natural, and stretched states. Two operation modes - compressed and stretched modes, are defined to model the continuous elongation dynamics before and after the quickly switching around the natural state. A set of design rules and a novel fabrication method are presented to develop the BEAST. The actuator characterization shows that the maximum extension ratio, snapping speed, and output force of the BEAST to be 0.58, 1.5m/s, and 48N, respectively. A hybrid linear parameter varying (HLPV) model is developed to describe the pressure-dependent dynamics of the actuator. The actuators are evaluated in an object sorting task where both fast and gentle behaviors are demonstrated. 

Snake robotics is an important research topic with a wide range of applications, including inspection in confined spaces, search-and-rescue, and disaster response. Snake robots are well-suited to these applications because of their versatility and adaptability to unstructured and constrained environments. In this paper, we introduce a soft pneumatic robotic snake that can imitate the capabilities of biological snakes, its soft body can provide flexibility and adaptability to the environment. This paper combines soft mobile robot modeling, proprioceptive feedback control, and motion planning to pave the way for functional soft robotic snake autonomy. We propose a pressure-operated soft robotic snake with a high degree of modularity that makes use of customized embedded flexible curvature sensing. On this platform, we introduce the use of iterative learning control using feedback from the on-board curvature sensors to enable the snake to automatically correct its gait for superior locomotion. We also present a motion planning and trajectory tracking algorithm using an adaptive bounding box, which allows for efficient motion planning that still takes into account the kinematic state of the soft robotic snake. We test this algorithm experimentally, and demonstrate its performance in obstacle avoidance scenarios.