Terrain Traversing Device Having a Wheel with Microhooks

A terrain traversing device is described. The device includes an annular rotor element with a plurality of co-planar microspine hooks arranged on the periphery of the annular rotor element. Each microspine hook has an independently flexible suspension configuration that permits the microspine hook to initially engage an irregularity in a terrain surface at a preset initial engagement angle and subsequently engage the irregularity with a continuously varying engagement angle when the annular rotor element is rotated for urging the terrain traversing device to traverse a terrain surface. Improvements related to the design, fabrication and use of the microspine hooks in the device are also described

Systems and Methods for Implementing Flexible Members Including Integrated Tools Made from Metallic Glass-Based Materials

Systems and methods in accordance with embodiments of the invention implement flexible members that include integrated tools made from metallic glass-based materials. In one embodiment, a structure includes: a flexible member characterized by an elongated geometry and an integrated tool disposed at one end of the elongated geometry; where the flexible member includes a metallic glass-based material

Puffer: Pop-Up Flat Folding Explorer Robot

A repeatably reconfigurable robot, comprising at least two printed circuit board (PCB) rigid sections, at least one PCB flexible section coupled to the at least two PCB rigid sections, at least one wheel, hybrid wheel propeller, wheel and propeller, or hybrid wheel screw propeller rotatably coupled to at least one of the at least two PCB rigid sections and at least one actuator coupled to the at least two PCB rigid sections, wherein the at least one actuator folds and unfolds the repeatably reconfigurable robot

Neuromorphic tactile sensor array based on fiber Bragg gratings to encode object qualities

Emulating the sense of touch is fundamental to endow robotic systems with perception abilities. This work presents an unprecedented mechanoreceptor-like neuromorphic tactile sensor implemented with fiber optic sensing technologies. A robotic gripper was sensorized using soft and flexible tactile sensors based on Fiber Bragg Grating (FBG) transducers and a neuro-bio-inspired model to extract tactile features. The FBGs connected to the neuron model emulated biological mechanoreceptors in encoding tactile information by means of spikes. This conversion of inflowing tactile information into event-based spikes has an advantage of reduced bandwidth requirements to allow communication between sensing and computational subsystems of robots. The outputs of the sensor were converted into spiking on-off events by means of an architecture implemented in a Field Programmable Gate Array (FPGA) and applied to robotic manipulation tasks to evaluate the effectiveness of such information encoding strategy. Different tasks were performed with the objective to grant fine manipulation abilities using the features extracted from the grasped objects (i.e., size and hardness). This is envisioned to be a futuristic sensor technology combining two promising technologies: optical and neuromorphic sensing

Architected lattices with adaptive energy absorption

Energy absorbing materials, like foams used in protective equipment, are able to undergo large deformations under low stresses, reducing the incoming stress wave below an injury or damage threshold. They are typically effective in absorbing energy through plastic deformation or fragmentation. However, existing solutions are passive, only effective against specific threats and they are usually damaged after use. Here, we overcome these limitations designing energy absorbing materials that use architected lattices filled with granular particles. We use architected lattices to take advantage of controlled bending and buckling of members to enhance energy absorption. We actively control the negative pressure level within the lattices, to tune the jamming phase transition of the granular particles, inducing controllable energy absorption and recoverable deformations. Our system shows tunable stiffness and yield strength by over an order of magnitude, and reduces the transmitted impact stress at different levels by up to 40% compared to the passive lattice. The demonstrated adaptive energy absorbing system sees wide potential applications from personal protective equipment, vehicle safety systems to aerospace structures

Tactile Sensing and Control of Robotic Manipulator Integrating Fiber Bragg Grating Strain-Sensor

Tactile sensing is an instrumental modality of robotic manipulation, as it provides information that is not accessible via remote sensors such as cameras or lidars. Touch is particularly crucial in unstructured environments, where the robot's internal representation of manipulated objects is uncertain. In this study we present the sensorization of an existing artificial hand, with the aim to achieve fine control of robotic limbs and perception of object's physical properties. Tactile feedback is conveyed by means of a soft sensor integrated at the fingertip of a robotic hand. The sensor consists of an optical fiber, housing Fiber Bragg Gratings (FBGs) transducers, embedded into a soft polymeric material integrated on a rigid hand. Through several tasks involving grasps of different objects in various conditions, the ability of the system to acquire information is assessed. Results show that a classifier based on the sensor outputs of the robotic hand is capable of accurately detecting both size and rigidity of the operated objects (99.36 and 100% accuracy, respectively). Furthermore, the outputs provide evidence of the ability to grab fragile objects without breakage or slippage e and to perform dynamic manipulative tasks, that involve the adaptation of fingers position based on the grasped objects' condition

Neuromorphic tactile sensor array based on fiber Bragg gratings to encode object qualities

Emulating the sense of touch is fundamental to endow robotic systems with perception abilities. This work presents an unprecedented mechanoreceptor-like neuromorphic tactile sensor implemented with fiber optic sensing technologies. A robotic gripper was sensorized using soft and flexible tactile sensors based on Fiber Bragg Grating (FBG) transducers and a neuro-bio-inspired model to extract tactile features. The FBGs connected to the neuron model emulated biological mechanoreceptors in encoding tactile information by means of spikes. This conversion of inflowing tactile information into event-based spikes has an advantage of reduced bandwidth requirements to allow communication between sensing and computational subsystems of robots. The outputs of the sensor were converted into spiking on-off events by means of an architecture implemented in a Field Programmable Gate Array (FPGA) and applied to robotic manipulation tasks to evaluate the effectiveness of such information encoding strategy. Different tasks were performed with the objective to grant fine manipulation abilities using the features extracted from the grasped objects (i.e., size and hardness). This is envisioned to be a futuristic sensor technology combining two promising technologies: optical and neuromorphic sensing

Tactile Sensing and Control of Robotic Manipulator Integrating Fiber Bragg Grating Strain-Sensor

Tactile sensing is an instrumental modality of robotic manipulation, as it provides information that is not accessible via remote sensors such as cameras or lidars. Touch is particularly crucial in unstructured environments, where the robot's internal representation of manipulated objects is uncertain. In this study we present the sensorization of an existing artificial hand, with the aim to achieve fine control of robotic limbs and perception of object's physical properties. Tactile feedback is conveyed by means of a soft sensor integrated at the fingertip of a robotic hand. The sensor consists of an optical fiber, housing Fiber Bragg Gratings (FBGs) transducers, embedded into a soft polymeric material integrated on a rigid hand. Through several tasks involving grasps of different objects in various conditions, the ability of the system to acquire information is assessed. Results show that a classifier based on the sensor outputs of the robotic hand is capable of accurately detecting both size and rigidity of the operated objects (99.36 and 100% accuracy, respectively). Furthermore, the outputs provide evidence of the ability to grab fragile objects without breakage or slippage e and to perform dynamic manipulative tasks, that involve the adaptation of fingers position based on the grasped objects' condition