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1. Position Control and Variable-Height Trajectory Tracking of a Soft Pneumatic Legged Robot

   https://doi.org/10.1109/IROS51168.2021.9635966  

   Liu, Zhichao ; Karydis, Konstantinos ( September 2021 , 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Soft pneumatic legged robots show promise in their ability to traverse a range of different types of terrain, including natural unstructured terrain met in applications like precision agriculture. They can adapt their body morphology to the intricacies of the terrain at hand, thus enabling robust and resilient locomotion. In this paper we capitalize upon recent developments on soft pneumatic legged robots to introduce a closed-loop trajectory tracking control scheme for operation over flat ground. Closed-loop pneumatic actuation feedback is achieved via a compact and portable pneumatic regulation board. Experimental results reveal that our soft legged robot can precisely control its body height and orientation while in quasi-static operation based on a geometric model. The robot can track both straight line and curved trajectories as well as variable-height trajectories. This work lays the basis to enable autonomous navigation for soft legged robots. 

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2. Implementation of a Cosserat Rod-Based Configuration Tracking Controller on a Multi-Segment Soft Robotic Arm

   https://doi.org/10.1109/IROS55552.2023.10342160  

   Doroudchi, Azadeh ; Qiao, Zhi ; Zhang, Wenlong ; Berman, Spring ( December 2023 , 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Controlling soft continuum robotic arms is challenging due to their hyper-redundancy and dexterity. In this paper we experimentally demonstrate, for the first time, closed-loop control of the configuration space variables of a soft robotic arm, composed of independently controllable segments, using a Cosserat rod model of the robot and the distributed sensing and actuation capabilities of the segments. Our controller solves the inverse dynamic problem by simulating the Cosserat rod model in MATLAB using a computationally efficient numerical solution scheme, and it applies the computed control output to the actual robot in real time. The position and orientation of the tip of each segment are measured in real time, while the remaining unknown variables that are needed to solve the inverse dynamics are estimated simultaneously in the simulation. We implement the controller on a multi-segment silicone robotic arm with pneumatic actuation, using a motion capture system to measure the segments' positions and orientations. The controller is used to reshape the arm into configurations that are achieved through combinations of bending and extension deformations in 3D space. Although the possible deformations are limited for this robot platform, our study demonstrates the potential for implementing the control approach on a wide range of continuum robots in practice. The resulting tracking performance indicates the effectiveness of the controller and the accuracy of the simulated Cosserat rod model. 

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3. Learning to Locomote with Artificial Neural-Network and CPG-based Control in a Soft Snake Robot

   https://doi.org/10.1109/IROS45743.2020.9340763  

   Liu, Xuan ; Gasoto, Renato ; Jiang, Ziyi ; Onal, Cagdas ; Fu, Jie ( October 2020 , 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   In this paper, we present a new locomotion control method for soft robot snakes. Inspired by biological snakes, our control architecture is composed of two key modules: A reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns. The two modules are interconnected into a closed-loop system: The RL module, analogizing the locomotion region located in the midbrain of vertebrate animals, regulates the input to the CPG system given state feedback from the robot. The output of the CPG system is then translated into pressure inputs to the pneumatic actuators of the soft snake robot. Based on the fact that the oscillation frequency and wave amplitude of the Matsuoka oscillator can be independently controlled under different time scales, we further adapt the option-critic framework to improve the learning performance measured by optimality and data efficiency. The performance of the proposed controller is experimentally validated with both simulated and real soft snake robots. 

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4. Validation of an Extensible Rod Model for Soft continuum Manipulators

   https://doi.org/10.1109/ROBOSOFT.2019.8722721  

   Gilbert, Hunter B. ; Godage, Isuru S. ( April 2019 , 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft))

   Numerous soft and continuum robotic manipulators have demonstrated their potential for compliant operation in highly unstructured environments or near people. Despite their recent popularity, modeling of their smooth bending deformation remains a challenge. For soft continuum manipulators, the widespread, constant curvature approach to modeling is inadequate for modeling some deformations that occur in practice, such as combined bending and twisting deformations. In this paper, we extend the classical Cosserat rod approach to model a variable-length, pneumatic soft continuum arm. We model the deformation of a pneumatically driven soft continuum manipulator, and the model is then compared against experimental data collected from a three degree of freedom, pneumatically actuated, soft continuum manipulator. The model shows good agreement in capturing the overall behavior of the bending deformation, with mean Euclidean error at the tip of the robot of 2.48 cm for a 22 cm long robot. In addition, the model shows good numerical stability for simulating long duration computations. 

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5. A pneumatic random-access memory for controlling soft robots

   https://doi.org/10.1371/journal.pone.0254524  

   Hoang, Shane ; Karydis, Konstantinos ; Brisk, Philip ; Grover, William H. ( July 2021 , PLOS ONE)

   Mattoli, Virgilio (Ed.)

   Pneumatically-actuated soft robots have advantages over traditional rigid robots in many applications. In particular, their flexible bodies and gentle air-powered movements make them more suitable for use around humans and other objects that could be injured or damaged by traditional robots. However, existing systems for controlling soft robots currently require dedicated electromechanical hardware (usually solenoid valves) to maintain the actuation state (expanded or contracted) of each independent actuator. When combined with power, computation, and sensing components, this control hardware adds considerable cost, size, and power demands to the robot, thereby limiting the feasibility of soft robots in many important application areas. In this work, we introduce a pneumatic memory that uses air (not electricity) to set and maintain the states of large numbers of soft robotic actuators without dedicated electromechanical hardware. These pneumatic logic circuits use normally-closed microfluidic valves as transistor-like elements; this enables our circuits to support more complex computational functions than those built from normally-open valves. We demonstrate an eight-bit nonvolatile random-access pneumatic memory (RAM) that can maintain the states of multiple actuators, control both individual actuators and multiple actuators simultaneously using a pneumatic version of time division multiplexing (TDM), and set actuators to any intermediate position using a pneumatic version of analog-to-digital conversion. We perform proof-of-concept experimental testing of our pneumatic RAM by using it to control soft robotic hands playing individual notes, chords, and songs on a piano keyboard. By dramatically reducing the amount of hardware required to control multiple independent actuators in pneumatic soft robots, our pneumatic RAM can accelerate the spread of soft robotic technologies to a wide range of important application areas. 

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