Abstract:Recent efforts on wearable robots have focused on augmenting the motor performance and/or protecting the wearer's body with lightweight structures. However, providing human‐scale force and structural stiffness usually conflicts with the wearability. Inspired by sandwich‐structured composites with high structural strengths, widely employed in both nature and man‐made structures, a mechanism of selectively stiffening garments (SSGs) utilizing anisotropic cellular cores and rubber‐laminated face sheets is propose… Show more
“…It is sometimes useful to recognize objects or humans in proximity that approach to the robot. One possible solution is to use the organogel channels as a proximity sensor by measuring the change in capacitance (Navarro et al, 2021; Kwon et al, 2022). In this way, the robot will be able to more proactively respond to any possible contacts or collisions in advance.…”
This study proposes a modularized soft robotic arm with integrated sensing of human touches for physical human–robot interactions. The proposed robotic arm is constructed by connecting multiple soft manipulator modules, each of which consists of three bellow-type soft actuators, pneumatic valves, and an on-board sensing and control circuit. By employing stereolithography three-dimensional (3D) printing technique, the bellow actuator is capable of incorporating embedded organogel channels in the thin wall of its body that are used for detecting human touches. The organogel thus serves as a soft interface for recognizing the intentions of the human operators, enabling the robot to interact with them while generating desired motions of the manipulator. In addition to the touch sensors, each manipulator module has compact, soft string sensors for detecting the displacements of the bellow actuators. When combined with an inertial measurement unit (IMU), the manipulator module has a capability of estimating its own pose or orientation internally. We also propose a localization method that allows us to estimate the location of the manipulator module and to acquire the 3D information of the target point in an uncontrolled environment. The proposed method uses only a single depth camera combined with a deep learning model and is thus much simpler than those of conventional motion capture systems that usually require multiple cameras in a controlled environment. Using the feedback information from the internal sensors and camera, we implemented closed-loop control algorithms to carry out tasks of reaching and grasping objects. The manipulator module shows structural robustness and the performance reliability over 5,000 cycles of repeated actuation. It shows a steady-state error and a standard deviation of 0.8 mm and 0.3 mm, respectively, using the proposed localization method and the string sensor data. We demonstrate an application example of human–robot interaction that uses human touches as triggers to pick up and manipulate target objects. The proposed soft robotic arm can be easily installed in a variety of human workspaces, since it has the ability to interact safely with humans, eliminating the need for strict control of the environments for visual perception. We believe that the proposed system has the potential to integrate soft robots into our daily lives.
“…It is sometimes useful to recognize objects or humans in proximity that approach to the robot. One possible solution is to use the organogel channels as a proximity sensor by measuring the change in capacitance (Navarro et al, 2021; Kwon et al, 2022). In this way, the robot will be able to more proactively respond to any possible contacts or collisions in advance.…”
This study proposes a modularized soft robotic arm with integrated sensing of human touches for physical human–robot interactions. The proposed robotic arm is constructed by connecting multiple soft manipulator modules, each of which consists of three bellow-type soft actuators, pneumatic valves, and an on-board sensing and control circuit. By employing stereolithography three-dimensional (3D) printing technique, the bellow actuator is capable of incorporating embedded organogel channels in the thin wall of its body that are used for detecting human touches. The organogel thus serves as a soft interface for recognizing the intentions of the human operators, enabling the robot to interact with them while generating desired motions of the manipulator. In addition to the touch sensors, each manipulator module has compact, soft string sensors for detecting the displacements of the bellow actuators. When combined with an inertial measurement unit (IMU), the manipulator module has a capability of estimating its own pose or orientation internally. We also propose a localization method that allows us to estimate the location of the manipulator module and to acquire the 3D information of the target point in an uncontrolled environment. The proposed method uses only a single depth camera combined with a deep learning model and is thus much simpler than those of conventional motion capture systems that usually require multiple cameras in a controlled environment. Using the feedback information from the internal sensors and camera, we implemented closed-loop control algorithms to carry out tasks of reaching and grasping objects. The manipulator module shows structural robustness and the performance reliability over 5,000 cycles of repeated actuation. It shows a steady-state error and a standard deviation of 0.8 mm and 0.3 mm, respectively, using the proposed localization method and the string sensor data. We demonstrate an application example of human–robot interaction that uses human touches as triggers to pick up and manipulate target objects. The proposed soft robotic arm can be easily installed in a variety of human workspaces, since it has the ability to interact safely with humans, eliminating the need for strict control of the environments for visual perception. We believe that the proposed system has the potential to integrate soft robots into our daily lives.
“…Kwon et al [33] created a sandwich jamming construction that consists of a rubberlaminated front sheet and an anisotropic cellular core. When the structure was not jammed, the light anisotropic core's low bending modulus allowed for excellent structural compliance, but when jamming occurred, it became extremely stiff.…”
Laminar jamming (LJ) technology is a hot topic because it allows for the transition from conventionally quick, precise, and high-force rigid robots to flexible, agile, and secure soft robots. This...
“…[10][11][12] Sensors that can be used with robotic skin measure various stimuli, such as proximity, force, and human touch, on the surface. Proximity sensors, which detects an object before physical contacts, were developed using inductive, [13][14][15] capacitive, [12,[16][17][18] and optical [19][20][21][22] mechanisms. Force sensors were implemented by resistive [23][24][25][26][27][28][29][30][31][32][33][34][35][36] and capacitive [37][38][39][40][41][42][43] mechanisms to measure contact forces, and touch sensors were developed using a capacitive mechanism.…”
For safe coexistence between robots and humans, it is important for robots to detect the presence of nearby humans as well as any physical contacts made to its body. The design of a modular textile sensor array and an algorithm for multi‐modal sensing of human touches and other contacts with their contact forces proposed. Each sensor module in the array is capable of multi‐modal sensing, and the entire array with multiple modules requires only two wires to read the outputs from all the modules using band‐stop filter circuits. The proposed sensor system shows the structural modularity, achieved by simple fabrication of sequential lamination of conductive and non‐conductive textile materials, realizing electrical connections through conductive snap buttons that connect the modules to the circuit. The functional modularity is also achieved through the compensation algorithm, derived from the analysis of the transfer function in the frequency domain. The algorithm significantly reduces signal interferences between modules. The multi‐modality, the textile‐based design, and the structural and functional modularity of the proposed system enable practical applications to various robotic systems, including robotic skin for a collaborative robot, a wearable sensor, a robot hand sensor, and a human–computer interface, as demonstrated in this study.
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