Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network

Lapusan, Ciprian; Hancu, Olimpiu; Rad, Ciprian

doi:10.3390/s22010373

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…Simulation results demonstrate accurate state estimation during various dynamic motions. (Lapusan et al, 2022) propose a new approach for shape sensing of hyper-redundant robots using an AHRS IMU sensor network integrated into the robot's structure. The method enables real-time feedback systems by directly calculating kinematic parameters in the modules' operational space, reducing computational time.…”

Section: Related Studiesmentioning

confidence: 99%

Accurate Estimation of Robotic Arm Movements for Effective Motion Control: Utilizing Multiple Sensors and Data Fusion

2024

ZJPAS

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The robotic manipulators are highly complex coupling dynamic systems, which require a mathematical model for planning and controlling the robotic motions. It is imperative to calculate the kinematic parameters such as rotational matrix, joint angles, angular velocity, and angular acceleration, which determines the control performance of the models. For this purpose, a multiple-sensor-based Mathematical approach that utilizes inertial measurement unit (IMU) and triple-axis accelerometer is presented in this paper. A combination of one IMU and three triple-axis accelerometers is affixed to each of the two rigid bodies for real-time determination of parameters and the robotic arm orientation. Additionally, the model incorporates an Extended Kalman filter (EKF) fusion technique to combine data from various sensors, mitigate measurement noise, and adapt in real-time to changing environments. To implement this approach, a MATLAB code is developed to read, preprocess sensors data, and simulation of the proposed model. All the results are presented graphically and indicate that the motion parameters and pose measurements are calculated accurately and effectively.

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Section: Related Studiesmentioning

confidence: 99%

Accurate Estimation of Robotic Arm Movements for Effective Motion Control: Utilizing Multiple Sensors and Data Fusion

2024

ZJPAS

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“…Microelectromechanical (MEMs) tracking sensors such as inertial sensors are comprised of a system of accelerometers, a gyroscope, and magnetometers to measure multiaxial orientation measurements. Some examples demonstrate a shape sensing approach for hyper-redundant, snake-like, and pneumatic robots using a network of IMU sensors fixed along the links of the robot, using the sensor information to calculate the tip pose through a kinematic model [5][6][7]. Although these sensor sizes are relatively small, they are not small enough to easily fuse within a soft manipulator.…”

Section: Introductionmentioning

confidence: 99%

Shape Sensing for Continuum Robotics Using Optoelectronic Sensors with Convex Reflectors

Osman,

Du,

Minton

et al. 2024

Electronics

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Three-dimensional shape sensing in soft and continuum robotics is a crucial aspect for stable actuation and control in fields such as minimally invasive surgery, engine repairs and search and rescue operations, as the estimation of complex curvatures while using continuum robotic tools is required to manipulate through fragile paths. This challenge has been addressed using a range of different sensing techniques, for example, Fibre Bragg grating (FBG) technology, inertial measurement unit (IMU) sensor networks, or stretch sensors. Previously, an optics-based method using optoelectronic sensors was explored, offering a simple and cost-effective solution for shape sensing in a flexible tendon-actuated manipulator in two orientations. This was based on proximity-modulated angle estimation and has been the basis for the shape sensing method addressed in this paper. The improved and miniaturised technique demonstrated in this paper is based on the use of a convex shaped reflector with optoelectronic sensors integrated into a tendon-actuated robotic manipulator. Upgraded sensing capability is achieved using optimisation of the convex reflector shape in terms of sensor range and resolution, and improved calibration is achieved through the integration of spherical bearings for friction-free motion. Shape estimation is achieved in two orientations upon calibration of sensors, with a maximum Root-Mean-Square Error (RMS) of 3.37°.

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“…MEMs sensors, due to their size and rigidity, are often difficult to fuse with soft robotic manipulators. Many studies have been undertaken in the design of continuum robots integrated with inertial measurement units [3] [4], accelerometers, and inductive sensors [5]. Sensor Tape [6] for example, that can be attached to larger continuum robot, utilised an array of MEMS sensors, including accelerometers, and inertial measurement units as well as time of flight sensors, to estimate the shape of three-dimensional curvatures.…”

Section: Introductionmentioning

confidence: 99%

Optoelectronic Shape Sensing for Flexible Robotic Applications

Osman,

Noh

2024

Preprint

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Shape sensing in continuum robotics enables stable actuation and control, as estimation of complex curvatures is essential for manoeuvring through complex environments, for applications in the manufacturing, aerospace, and medical industries, as well as space and rescue operations. This paper demonstrates the performance of an optoelectronic based shape sensing system integrated into a two-segment tendon actuated robotic manipulator. The sensing principle is proximity-intensity based sensing and utilises a convex shaped reflector for modulation of proximity during rotation in two degrees of freedom. For improved sensing performance, the shape sensing system utilises a simplified circuit design with features such as power switching properties for elimination of signal interference effects and for significant reduction in consumption of power, as well as inclusion of low friction tubes along the tendon routing paths, for reduced friction during large bending motions. A new streamlined technique for the calibration of the sensors is demonstrated, and a validation of the shape sensing performance shows improved estimates of tip position and orientation as well as shape of the robotic structure.

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Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network

Cited by 9 publications

References 40 publications

Accurate Estimation of Robotic Arm Movements for Effective Motion Control: Utilizing Multiple Sensors and Data Fusion

Accurate Estimation of Robotic Arm Movements for Effective Motion Control: Utilizing Multiple Sensors and Data Fusion

Shape Sensing for Continuum Robotics Using Optoelectronic Sensors with Convex Reflectors

Optoelectronic Shape Sensing for Flexible Robotic Applications

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