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Robot teleoperation is an emerging field of study with wide applications in exploration, manufacturing, and healthcare, because it allows users to perform complex remote tasks while remaining distanced and safe. Haptic feedback offers an immersive user experience and expands the range of tasks that can be accomplished through teleoperation. In this paper, we present a novel wearable haptic feedback device for a teleoperation system that applies kinesthetic force feedback to the fingers of a user. The proposed device, called a ‘haptic muscle’, is a soft pneumatic actuator constructed from a fabric-silicone composite in a toroidal structure. We explore the requirements of the ideal haptic feedback mechanism, construct several haptic muscles using different materials, and experimentally determine their dynamic pressure response as well as sensitivity (their ability to communicate small changes in haptic feedback). Finally, we integrate the haptic muscles into a data glove and a teleoperation system and perform several user tests. Our results show that most users could detect detect force changes as low as 3% of the working range of the haptic muscles. We also find that the haptic feedback causes users to apply up to 52% less force on an object while handling soft and fragile objects with a teleoperation system. 

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.