@article{Guo2024ProprioceptiveState,

title = {Proprioceptive State Estimation for Amphibious Tactile Sensing},

author = {Ning Guo and Xudong Han and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Jiansheng Dai and Fang Wan and Chaoyang Song},

doi = {10.1109/TRO.2024.3463509},

year  = {2024},

date = {2024-09-18},

urldate = {2024-09-18},

journal = {IEEE Transactions on Robotics},

volume = {40},

issue = {September},

pages = {4684-4698},

abstract = {This article presents a novel vision-based proprioception approach for a soft robotic finger that can estimate and reconstruct tactile interactions in terrestrial and aquatic environments. The key to this system lies in the finger's unique metamaterial structure, which facilitates omnidirectional passive adaptation during grasping, protecting delicate objects across diverse scenarios. A compact in-finger camera captures high-framerate images of the finger's deformation during contact, extracting crucial tactile data in real time. We present a volumetric discretized model of the soft finger and use the geometry constraints captured by the camera to find the optimal estimation of the deformed shape. The approach is benchmarked using a motion capture system with sparse markers and a haptic device with dense measurements. Both results show state-of-the-art accuracy, with a median error of 1.96 mm for overall body deformation, corresponding to 2.1 % of the finger's length. More importantly, the state estimation is robust in both on-land and underwater environments as we demonstrate its usage for underwater object shape sensing. This combination of passive adaptation and real-time tactile sensing paves the way for amphibious robotic grasping applications.},

key = {2024-J-TRO-ProprioceptiveState},

keywords = {Authorship - Corresponding, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)},

pubstate = {published},

tppubtype = {article}

}

@article{Tian2023SamplingBased,

title = {Sampling-Based Planning for Retrieving Near-Cylindrical Objects in Cluttered Scenes Using Hierarchical Graphs},

author = {Hao Tian and Chaoyang Song and Changbo Wang and Xinyu Zhang and Jia Pan},

doi = {10.1109/TRO.2022.3191596},

year  = {2023},

date = {2023-02-01},

urldate = {2023-02-01},

journal = {IEEE Transactions on Robotics},

volume = {39},

number = {1},

issue = {February},

pages = {165-182},

abstract = {We present an incremental sampling-based task and motion planner for retrieving near-cylindrical objects, like bottle, in cluttered scenes, which computes a plan for removing obstacles to generate a collision-free motion of a robot to retrieve the target object. Our proposed planner uses a two-level hierarchy, including the first-level roadmap for the target object motion and the second-level retrieval graph for the entire robot motion, to aid in deciding the order and trajectory of object removal. We use an incremental expansion strategy to update the roadmap and retrieval graph from the collisions between the target object, the robot, and the obstacles, in order to optimize the object removal sequence. The performance of our method is highlighted in several benchmark scenes, including a fixed robotic arm in a cluttered scene with known obstacle locations and a scene, where locations of some objects or even the target object are unknown due to occlusions. Our method can also efficiently solve the high-dimensional planning problem of object retrieval using a mobile manipulator and be combined with the symbolic planner to plan complex multistep tasks. We deploy our method to a physical robot and integrate it with nonprehensile actions to improve operational efficiency. Compared to the state-of-the-art approaches, our method reduces task and motion planning time up to 24.6% with a higher success rate, and still provides a near-optimal plan.},

keywords = {Authorship - Co-Author, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)},

pubstate = {published},

tppubtype = {article}

}

@article{Yi2019Customizable3D,

title = {Customizable Three-Dimensional-Printed Origami Soft Robotic Joint with Effective Behavior Shaping for Safe Interactions},

author = {Juan Yi and Xiaojiao Chen and Chaoyang Song and Jianshu Zhou and Yujia Liu and Sicong Liu and Zheng Wang},

doi = {10.1109/tro.2018.2871440},

year  = {2019},

date = {2019-02-01},

urldate = {2019-02-01},

journal = {IEEE Transactions on Robotics},

volume = {35},

number = {1},

issue = {February},

pages = {114-123},

abstract = {Fast-growing interests in safe and effective robot–environment interactions stimulated global investigations on soft robotics. The inherent compliance of soft robots ensures promising safety features but drastically reduces force capability, thereby complicating system modeling and control. To tackle these limitations, a soft robotic joint with enhanced strength, servo performance, and impact behavior shaping is proposed in this paper, based on novel three-dimensional-printed soft origami rotary actuators. The complete workflow is presented from the concept of origami design and analytical modeling, joint design, fabrication, control, and validation experiments. The proposed approach facilitates a fully customizable joint design towards the desired force capability and motion range. Validation results from models and experiments using multiple fabricated prototypes proved the excellent performance linearity and superior force capability, with 18.5-N·m maximum torque under 180 kPa, and 300-g self-weight. The behavior shaping capability is achieved by a low-level joint-angle servo and a high-level variable-stiffness regulation; this significantly reduces the impact torque by 53% and ensures powerful and safe interactions. The comprehensive guidelines provide insightful references for soft robotic design for wider robotic applications.},

keywords = {Authorship - Co-Author, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)},

pubstate = {published},

tppubtype = {article}

}