Energy Consumption-Based Trajectory Planning for Manipulators

Ata, Atef A.

doi:10.3233/atde220577

Cited by 6 publications

(8 citation statements)

References 3 publications

(5 reference statements)

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“…First, we introduce a biologically plausible, activity-dependent facilitation of the synaptic weights [65] to maximize the smoothness of the motion. The efficacy f (t) ij of the synaptic connection between the the jth ePPC and the ith motor neuron increases with each presynaptic spike until saturating at a maximum value and decays otherwise with a factor d fac , as shown in equation (4).…”

Section: Weight Adaptationmentioning

confidence: 99%

“…After demonstrating that the proposed SNN controller is indeed applicable to real-world robotic arms with multiple DOF, we evaluate the approach by comparing it with a state-of-the-art PID controller that is widely used in similar applications. Specifically, we use as the baseline the operational space controller proposed in [32], which is optimized for the control of the Jaco arm in the Applied Brain Research control library 4 . The set of tuned PID parameters are K p = 20 for the proportional component, K v = 30 for the derivative component, and K i = 0.001 for the integral component of the position, while K o = 180 is set for the proportional control of the orientation.…”

Section: Comparison With Conventional Pid Controlmentioning

confidence: 99%

“…Modern robot arms perform a wide variety of complex and repetitive tasks, both in isolated industrial environments [7] and in close interaction with humans [2]. The efficiency of the arms' performance requires the determination of the best trajectory for the execution of the task [4,23]. This trajectory can be determined by optimizing several variables.…”

Section: Introductionmentioning

confidence: 99%

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Bioinspired smooth neuromorphic control for robotic arms

Polykretis

Supic

Danielescu

2023

Neuromorph. Comput. Eng.

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Beyond providing accurate movements, achieving smooth motion trajectories is a long-standing goal of robotics control theory for arms aiming to replicate natural human movements. Drawing inspiration from biological agents, whose reaching control networks effortlessly give rise to smooth and precise movements, can simplify these control objectives for robot arms. Neuromorphic processors, which mimic the brain's computational principles, are an ideal platform to approximate the accuracy and smoothness of biological controllers while maximizing their energy efficiency and robustness. However, the incompatibility of conventional control methods with neuromorphic hardware limits the computational efficiency and explainability of their existing adaptations. In contrast, the neuronal subnetworks underlying smooth and accurate reaching movements are effective, minimal, and inherently compatible with neuromorphic hardware. In this work, we emulate these networks with a biologically realistic spiking neural network for motor control on neuromorphic hardware. The proposed controller incorporates experimentally-identified short-term synaptic plasticity and specialized neurons that regulate sensory feedback gain to provide smooth and accurate joint control across a wide motion range. Concurrently, it preserves the minimal complexity of its biological counterpart and is directly deployable on Intel's neuromorphic processor. Using the joint controller as a building block and inspired by joint coordination in human arms, we scaled up this approach to control real-world robot arms. The trajectories and smooth, bell-shaped velocity profiles of the resulting motions resembled those of humans, verifying the biological relevance of the controller. Notably, the method achieved state-of-the-art control performance while decreasing the motion jerk by 19\% to improve motion smoothness. Overall, this work suggests that control solutions inspired by experimentally identified neuronal architectures can provide effective, neuromorphic-controlled robots.

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Section: Weight Adaptationmentioning

confidence: 99%

Section: Comparison With Conventional Pid Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bioinspired smooth neuromorphic control for robotic arms

Polykretis

Supic

Danielescu

2023

Neuromorph. Comput. Eng.

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“…In this paper, conventional fourth-order S-trajectory planning was used [11][12]. Due to non-linear and strongly coupling of electromagnetic linear actuator, in order to improve the tracking accuracy of the motion trajectory and enhance the system performance, a two-degree-of-freedom control system consisting of inverse system feed-forward and error PD feedback control was designed, which complements the advantages of the two control methods to improve the trajectory tracking control accuracy [14][15][16][17][18].…”

Section: Trajectory Tracking Control Based On Inverse System Feedforw...mentioning

confidence: 99%

Digital twin-driven motion trajectory tracking control of electromagnetic linear actuator

Guo,

Chang,

et al. 2023

International Conference on Mechatronics and Intelligent Control (ICMIC 2023)

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“…Trajectory generation for a complex, compliant hand-arm system is a significant challenge [1][2][3]. This is due to the interdependence between the trajectory of the arm, and the physical, passive interactions between the hand and the environment [4].…”

Section: Introductionmentioning

confidence: 99%

Wrist-driven passive grasping: interaction-based trajectory adaption with a compliant anthropomorphic hand

2021

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The structure of the human musculo-skeletal systems shows complex passive dynamic properties, critical for adaptive grasping and motions. Through wrist and arm actuation, these passive dynamic properties can be exploited to achieve nuanced and diverse environment interactions. We have developed a passive anthropomorphic robot hand that shows complex passive dynamics. We require arm/wrist control with the ability to exploit these. Due to the soft hand structures and high degrees of freedom during passive-object interactions, bespoke generation of wrist trajectories is challenging. We propose a new approach, which takes existing wrist trajectories and adapts them to changes in the environment, through analysis and classification of the interactions. By analysing the interactions between the passive hand and object, the required wrist motions to achieve them can be mapped back to control of the hand. This allows the creation of trajectories which are parameterized by object size or task. This approach shows up to 86% improvement in grasping success rate with a passive hand for object size changes up to ±50%.

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Energy Consumption-Based Trajectory Planning for Manipulators

Cited by 6 publications

References 3 publications

Bioinspired smooth neuromorphic control for robotic arms

Bioinspired smooth neuromorphic control for robotic arms

Digital twin-driven motion trajectory tracking control of electromagnetic linear actuator

Wrist-driven passive grasping: interaction-based trajectory adaption with a compliant anthropomorphic hand

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