A Teleoperated Shared Control Approach with Haptic Feedback for Mobile Assistive Robot

Lin, Zhidong; Luo, Jing; Yang, Chenguang

doi:10.23919/iconac.2019.8895096

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In the application scenario described in this paper, o = F = [ f 1 , f 2 ] T , meaning the observation data are two-dimensional, i.e., ε = 2. The model parameters substituted into (11) are as follows:…”

Section: Hidden State Number Identificationmentioning

confidence: 99%

“…In the above cases, obstacle avoidance problems can be divided into three kinds of rough solutions. The first is based on force feedback, which models the environmental information, namely, the obstacle information, as repulsive force and the target information as attractive force, so as to achieve avoidance [3,[9][10][11][12]. This situation is similar to the modeling idea of artificial potential field function in trajectory planning, which requires an ingenious repulsive function design method to avoid obstacle avoidance failure due to the potential instability between the operator and the interaction force.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unified Human Intention Recognition and Heuristic-Based Trajectory Generation for Haptic Teleoperation of Non-Holonomic Vehicles

Zhang

Zhao

et al. 2023

Machines

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In this paper, a novel bilateral shared control approach is proposed to address the issue of strong dependence on the human, and the resulting burden of manipulation, in classical haptic teleoperation systems for vehicle navigation. A Hidden Markov Model (HMM) is utilized to handle the Human Intention Recognition (HIR), according to the force input by the human—including the HMM solution, i.e., Baum–Welch algorithm, and HMM decoding, i.e., Viterbi algorithm—and the communication delay in teleoperation systems is added to generate a temporary goal. Afterwards, a heuristic and sampling method for online generation of splicing trajectory based on the goal is proposed innovatively, ensuring the vehicle can move feasibly after the change in human intention is detected. Once the trajectory is available, the vehicle velocity is then converted to joystick position information as the haptic cue of the human, which enhances the telepresence. The shared teleoperation control framework is verified in the simulation environment, where its excellent performance in the complex environment is evaluated, and its feasibility is confirmed. The experimental results show that the proposed method can achieve simple and efficient navigation in a complex environment, and can also give a certain situational awareness to the human.

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Section: Hidden State Number Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unified Human Intention Recognition and Heuristic-Based Trajectory Generation for Haptic Teleoperation of Non-Holonomic Vehicles

Zhang

Zhao

et al. 2023

Machines

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show abstract

A human‐robot collaboration method for uncertain surface scanning

Zhao

Zeng

et al. 2023

CAAI Trans on Intel Tech

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Robots are increasingly expected to replace humans in many repetitive and high‐precision tasks, of which surface scanning is a typical example. However, it is usually difficult for a robot to independently deal with a surface scanning task with uncertainties in, for example the irregular surface shapes and surface properties. Moreover, it usually requires surface modelling with additional sensors, which might be time‐consuming and costly. A human‐robot collaboration‐based approach that allows a human user and a robot to assist each other in scanning uncertain surfaces with uniform properties, such as scanning human skin in ultrasound examination is proposed. In this approach, teleoperation is used to obtain the operator's intent while allowing the operator to operate remotely. After external force perception and friction estimation, the orientation of the robot end‐effector can be autonomously adjusted to keep as perpendicular to the surface as possible. Force control enables the robotic manipulator to maintain a constant contact force with the surface. And hybrid force/motion control ensures that force, position, and pose can be regulated without interfering with each other while reducing the operator's workload. The proposed method is validated using the Elite robot to perform a mock B‐ultrasound scanning experiment.

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OD-SLAM: Real-Time Localization and Mapping in Dynamic Environment through Multi-Sensor Fusion

Yang

2020

2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM)

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A Teleoperated Shared Control Approach with Haptic Feedback for Mobile Assistive Robot

Cited by 5 publications

References 16 publications

Unified Human Intention Recognition and Heuristic-Based Trajectory Generation for Haptic Teleoperation of Non-Holonomic Vehicles

Unified Human Intention Recognition and Heuristic-Based Trajectory Generation for Haptic Teleoperation of Non-Holonomic Vehicles

A human‐robot collaboration method for uncertain surface scanning

OD-SLAM: Real-Time Localization and Mapping in Dynamic Environment through Multi-Sensor Fusion

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