Model-less feedback control for soft manipulators

Jin, Yusong; Wang, Yufei; Chen, Xiaotong; Wang, Zhanchi; Liu, Xinghua; Jiang, Hao; Chen, Xiaoping

doi:10.1109/iros.2017.8206124

Cited by 20 publications

(8 citation statements)

References 15 publications

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“…However, overshooting may occur during the control process. Similar to the method adopted in our previous work (Jin et al, 2017), using the scaled deviation as Δ x can make the control process more stable, but the convergence rate became lower (the blue curve in Figure 9A). Adopting PID in the simplified Jacobian controller (the orange curve in Figure 9) can alleviate the problems of the two controllers described previously.…”

Section: Methodsmentioning

confidence: 89%

“…Li et al (2018) implemented a 3D controller in a similar way, the difference is that they used a Kalman filter to handle sensor noise and demonstrated the ability of the controller to handle unknown load. In our previous work (Jin et al, 2017), a local kinematics model of a soft arm was obtained by analysis of the arm structure, and the position and orientation of the tip of the multi-segment soft arm in 3D space with unknown load were controlled by updating the model with actual data in real-time. Different from the methods described previously which were based on updating the Jacobian matrix, Lee et al (2017) used a non-parametric online learning method with real-time update to approximate the local inverse kinematics model and implemented a controller for a single-segment soft arm in 3D space under the condition that there are external disturbances at the middle of the arm.…”

Section: Related Workmentioning

confidence: 99%

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Hierarchical control of soft manipulators towards unstructured interactions

Jiang

Wang

Jin

et al. 2021

The International Journal of Robotics Research

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Performing daily interaction tasks such as opening doors and pulling drawers in unstructured environments is a challenging problem for robots. The emergence of soft-bodied robots brings a new perspective to solving this problem. In this paper, inspired by humans performing interaction tasks through simple behaviors, we propose a hierarchical control system for soft arms, in which the low-level controller achieves motion control of the arm tip, the high-level controller controls the behaviors of the arm based on the low-level controller, and the top-level planner chooses what behaviors should be taken according to tasks. To realize the motion control of the soft arm in interacting with environments, we propose two control methods. The first is a feedback control method based on a simplified Jacobian model utilizing the motion laws of the soft arm that are not affected by environments during interaction. The second is a control method based on [Formula: see text]-learning, in which we present a novel method to increase training data by setting virtual goals. We implement the hierarchical control system on a platform with the Honeycomb Pneumatic Networks Arm (HPN Arm) and validate the effectiveness of this system on a series of typical daily interaction tasks, which demonstrates this proposed hierarchical control system could render the soft arms to perform interaction tasks as simply as humans, without force sensors or accurate models of the environments. This work provides a new direction for the application of soft-bodied arms and offers a new perspective for the physical interactions between robots and environments.

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Section: Methodsmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Hierarchical control of soft manipulators towards unstructured interactions

Jiang

Wang

Jin

et al. 2021

The International Journal of Robotics Research

Self Cite

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“…Other researchers performed model-free control by approximating a Jacobian that is later used to actuate a robot. Researchers in Reference [10] controlled a deformable manipulator and in Reference [11] controlled a soft manipulator by approximating its Jacobian model. Other researchers controlled a dynamically-undefined robot by first estimating the robot's dynamic parameters and then controlled a robot using conventional methods [12].…”

Section: Related Workmentioning

confidence: 99%

Kinematic-Model-Free Redundancy Resolution Using Multi-Point Tracking and Control for Robot Manipulation

Cursi

Kormushev

2021

Applied Sciences

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Robots have been predominantly controlled using conventional control methods that require prior knowledge of the robots’ kinematic and dynamic models. These controllers can be challenging to tune and cannot directly adapt to changes in kinematic structure or dynamic properties. On the other hand, model-learning controllers can overcome such challenges. Our recently proposed model-learning orientation controller has shown promising ability to simultaneously control a three-degrees-of-freedom robot manipulator’s end-effector pose. However, this controller does not perform optimally with robots of higher degrees-of-freedom nor does it resolve redundancies. The research presented in this paper extends the state-of-the-art kinematic-model-free controller to perform pose control of hyper-redundant robot manipulators and resolve redundancies by tracking and controlling multiple points along the robot’s serial chain. The results show that with more control points, the controller is able to reach desired poses in fewer steps, yielding an improvement of up to 66%, and capable of achieving complex configurations. The algorithm was validated by running the simulation 100 times, and it was found that, in 82% of the times, the robot successfully reached the desired target pose within 150 steps.

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“…As an advancement, the research on the second-generation robots consists of trunks with a backbone and multiple discs, with the increased complexity of actuation and manipulation. ese trunks consist of multiple segments with dual actuation, i.e., electric motor and pneumatic [21][22][23][24][25][26][27][28]. In the current research scenario, the third-generation continuum robot, which is known as the bionic handling assistant (BHA) model developed by Festo [29][30][31], has entered the production environment.…”

Section: Structurementioning

confidence: 99%

Continuum Robots for Manipulation Applications: A Survey

Kolachalama

Lakshmanan

2020

Journal of Robotics

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This paper presents a literature survey documenting the evolution of continuum robots over the past two decades (1999–present). Attention is paid to bioinspired soft robots with respect to the following three design parameters: structure, materials, and actuation. Using this three-faced prism, we identify the uniqueness and novelty of robots that have hitherto not been publicly disclosed. The motivation for this study comes from the fact that continuum soft robots can make inroads in industrial manufacturing, and their adoption will be accelerated if their key advantages over counterparts with rigid links are clear. Four different taxonomies of continuum robots are included in this study, enabling researchers to quickly identify robots of relevance to their studies. The kinematics and dynamics of these robots are not covered, nor is their application in surgical manipulation.

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Model-less feedback control for soft manipulators

Cited by 20 publications

References 15 publications

Hierarchical control of soft manipulators towards unstructured interactions

Hierarchical control of soft manipulators towards unstructured interactions

Kinematic-Model-Free Redundancy Resolution Using Multi-Point Tracking and Control for Robot Manipulation

Continuum Robots for Manipulation Applications: A Survey

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