An efficient approach for inverse kinematics and redundancy resolution of spatial redundant robots for cluttered environment

Chembuly, V. V. M. J. Satish; Voruganti, Hari K.

doi:10.1007/s42452-020-2825-x

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…To perform agricultural operations, the redundant manipulator must avoid collisions with the surroundings. Collisions can be detected by the bounding box technique [30]. In the present study, different vegetables and guava fruits are considered as task-space locations for picking/plucking operations.…”

Section: Collision Detection Techniquementioning

confidence: 99%

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Collision-free Inverse Kinematics of Redundant Manipulator for Agricultural Applications through Optimization Techniques

2022

IJE

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Section: Collision Detection Techniquementioning

confidence: 99%

“…This algorithm has to be recomputed if the orientation of the object is changed. Chembuly and Voruganti [30] implemented a classical constrained optimization technique to perform inverse kinematics of hyper redundant manipulator along with collision avoidance algorithm. Here, the boundaries of obstacles are enveloped by a 3D bounding box.…”

Section: Introductionmentioning

confidence: 99%

Collision-free Inverse Kinematics of Redundant Manipulator for Agricultural Applications through Optimization Techniques

2022

IJE

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“…Recently, Chembuly and colleagues extended conventional IK optimizers to support collision avoidance. 7 Here, we've taken a data-driven approach, relying on the versatility of neural networks, to propose a robust framework for non-constrained and geometrically constrained IK.…”

Section: Introductionmentioning

confidence: 99%

Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics

et al. 2022

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Summary Inverse kinematics is fundamental for computational motion planning. It is used to derive an appropriate state in a robot's configuration space, given a target position in task space. In this work, we investigate the performance of fully connected and residual artificial neural networks as well as recurrent, learning-based, and deep spiking neural networks for conventional and geometrically constrained inverse kinematics. We show that while highly parameterized data-driven neural networks with tens to hundreds of thousands of parameters exhibit sub-ms inference time and sub-mm accuracy, learning-based spiking architectures can provide reasonably good results with merely a few thousand neurons. Moreover, we show that spiking neural networks can perform well in geometrically constrained task space, even when configured to an energy-conserved spiking rate, demonstrating their robustness. Neural networks were evaluated on NVIDIA's Xavier and Intel's neuromorphic Loihi chip.

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“…For addressing inverse kinematics resolution problems, the commonly used methods are include neural network-based solutions and the pseudo-inverse Jacobian matrix (PIJM) [11][12][13][14]. Different researchers, such as Wiedmeyer et al [16], Zlajpah et al [17], Basso et al [18], Chembuly et al [19], and Aldas et al [4], respectively, studied redundancy resolution using optimization techniques, task decomposition, traditional task priority, and kinematic and dynamics models of trajectory tracking. Farah et al [20] also carried out redundancy resolution of the MM using distributed control systems for trajectory tracking control.…”

Section: Introductionmentioning

confidence: 99%

Redundancy Resolution of a Mobile Manipulator Using Learning Based Algorithm

Tolossa,

Gupta,

Orlando

et al. 2024

Preprint

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A learning-based strategy for the trajectory tracking of redundant mobile manipulators (MM) was presented in this study. A five-degrees-of-freedom (DOF) manipulator is mounted on the differential drive (DD) mobile robot. The advantage of using a redundant system is to avoid joint limits, obstacles and singularities towards desired trajectory tracking. The proposed approach is based on Kohonen Self-Organizing Map (KSOM) enhanced a weighted least norm (WLN) matrix algorithm. This approach is the recommended neural network for inverse kinemat-ics solutions because of its stability, preserved topology, and capacity to optimize the joint space trajectory while producing a smooth minimal joint angle. A proposed method for redundancy resolution in MM has been simulated using MATLAB simulation code and the Gazebo real-time simulation pysical environment. The simulation results are evaluated with the joint limit method of redundancy resolution and other existing controllers for verification purpose. The conventional method of redundancy resolution is local optimum and infeasible for the end-effector motion in the entire workspace. The KSOM uses different steps of error correction that improve the system’s performance as well as ensure the global asymptotical stability of the system.

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An efficient approach for inverse kinematics and redundancy resolution of spatial redundant robots for cluttered environment

Cited by 11 publications

References 33 publications

Collision-free Inverse Kinematics of Redundant Manipulator for Agricultural Applications through Optimization Techniques

Collision-free Inverse Kinematics of Redundant Manipulator for Agricultural Applications through Optimization Techniques

Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics

Redundancy Resolution of a Mobile Manipulator Using Learning Based Algorithm

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