@article{Li2023CalibratedAnalytical,

title = {Calibrated Analytical Model for Magnetic Localization of Wireless Capsule Endoscope based on Onboard Sensing},

author = {You Li and Zhuokang Huang and Xiaobo Liu and Yu Jie and Chaoyang Song and Chengzhi Hu},

doi = {10.1017/S0263574722001849},

year  = {2023},

date = {2023-01-12},

urldate = {2023-01-12},

journal = {Robotica},

volume = {41},

number = {5},

pages = {1500-1514},

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 Q3, Jour - Robotica (ROBO)},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2017SoftRigid,

title = {Soft-Rigid Interaction Mechanism towards a Lobster-inspired Hybrid Actuator},

author = {Yaohui Chen and Fang Wan and Tong Wu and Chaoyang Song},

doi = {10.1088/1361-6439/aa9e25},

year  = {2017},

date = {2017-12-15},

urldate = {2017-12-15},

issuetitle = {Special Issue on Soft Robotics and Smart System Technologies},

journal = {Journal of Micromechanics and Microengineering},

volume = {28},

number = {1},

issue = {December},

pages = {014007},

abstract = {Soft pneumatic actuators (SPAs) are intrinsically light-weight, compliant and therefore ideal to directly interact with humans and be implemented into wearable robotic devices. However, they also pose new challenges in describing and sensing their continuous deformation. In this paper, we propose a hybrid actuator design with bio-inspirations from the lobsters, which can generate reconfigurable bending movements through the internal soft chamber interacting with the external rigid shells. This design with joint and link structures enables us to exactly track its bending configurations that previously posed a significant challenge to soft robots. Analytic models are developed to illustrate the soft-rigid interaction mechanism with experimental validation. A robotic glove using hybrid actuators to assist grasping is assembled to illustrate their potentials in safe human-robot interactions. Considering all the design merits, our work presents a practical approach to the design of next-generation robots capable of achieving both good accuracy and compliance.},

keywords = {Authorship - Corresponding, JCR Q3, Jour - J. Micromech. Microeng. (JMM)},

pubstate = {published},

tppubtype = {article}

}