Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

Malik, Aryslan; Henderson, Troy; Prazenica, Richard J.

doi:10.3390/robotics10040127

Cited by 13 publications

(8 citation statements)

References 26 publications

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“…e research contributed to quality assessment from the error reduction perspective [34]. Malik et al [35] defined the trading market and achieved unified pricing and task allocation in mobile crowdsourcing by reaching the Walras equilibrium. e incentive mechanism has always been an important research area in mobile SI perception, and the integration with interdisciplinary has always been an effective means.…”

Section: Discussionmentioning

confidence: 99%

“…e incentive mechanism has always been an important research area in mobile SI perception, and the integration with interdisciplinary has always been an effective means. e future research trend might shift from traditional classical economics to relevant disciplines that reflect human behavior characteristics and psychologies, such as behavioral economics and consumer psychology [35]. e above two works optimized MTA from the perspective of mobile SI perception.…”

Section: Discussionmentioning

confidence: 99%

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Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

Wang

2022

Security and Communication Networks

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The objective is to optimize the multitask assignment (MTA) in mobile crowdsensing (MCS) scenarios. From the perspective of reinforcement learning (RL), an Internet of Things (IoT) devices-oriented MTA model is established using MCS, IoT technology, and other related theories. Then, the data collected by the University of Cambridge and the University of St. Andrews are chosen to verify the three MTA algorithms on IoT devices. They are multistage online task assignment (MOTA), average makespan-sensitive online task assignment (AOTA), and water filling (WF). Experiments are designed by considering different algorithms’ MTA time consumption and accuracy in simple and complex task scenarios. The research results manifest that with a constant load or task quantity, the MOTA algorithm takes the shortest time to assign tasks. In simple task scenarios, MOTA is compared with the WF. The MOTA algorithm’s total moving distance is relatively short, and the task completion degree is the highest. AOTA algorithm lends best to complex tasks, with the highest MTA accuracy and the shortest time consumption. Therefore, the research on IoT devices’ MTA optimization based on RL in the MCS scenario provides a certain theoretical basis for subsequent MTA studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

Wang

2022

Security and Communication Networks

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“…Apart from these categories, the application of Machine Learning (ML), Artificial Intelligence (AI), and Artificial Neural Network (ANN) techniques has gained popularity in recent years. Various RL methods, such as Genetic Algorithm (GA) and Swarm Intelligence (SI) have proven to be effective in solving IK even for robotic manipulators with high-DOFs [4,5]. Deep Deterministic Policy Gradient (DDPG) and Normalized Advantage Function (NAF) algorithms have shown their usefulness in continuous action spaces and, more specifically, in robotic manipulation [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement-Learning Approach for Inverse Kinematics Solution of a High Degree of Freedom Robotic Manipulator

et al. 2022

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The foundation and emphasis of robotic manipulator control is Inverse Kinematics (IK). Due to the complexity of derivation, difficulty of computation, and redundancy, traditional IK solutions pose numerous challenges to the operation of a variety of robotic manipulators. This paper develops a Deep Reinforcement Learning (RL) approach for solving the IK problem of a 7-Degree of Freedom (DOF) robotic manipulator using Product of Exponentials (PoE) as a Forward Kinematics (FK) computation tool and the Deep Q-Network (DQN) as an IK solver. The selected approach is architecturally simpler, making it faster and easier to implement, as well as more stable, because it is less sensitive to hyperparameters than continuous action spaces algorithms. The algorithm is designed to produce joint-space trajectories from a given end-effector trajectory. Different network architectures were explored and the output of the DQN was implemented experimentally on a Sawyer robotic arm. The DQN was able to find different trajectories corresponding to a specified Cartesian path of the end-effector. The network agent was able to learn random Bézier and straight-line end-effector trajectories in a reasonable time frame with good accuracy, demonstrating that even though DQN is mainly used in discrete solution spaces, it could be applied to generate joint space trajectories.

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“…The influence of forces (Petrescu, 2022) is actually what determines the dynamic, real operation of all mechanical processes, including robots. In this way, it is possible to control the trajectory of robots and/or spacecraft (or drones) (Alpers, 2021;Caruso et al, 2021;Ebel et al, 2021;Thompson et al, 2021;Vatsal and Hoffman, 2021;Al Younes and Barczyk, 2021;Pacheco-Gutierrez et al, 2021;Stodola et al, 2021;Raviola et al, 2021;Medina and Hacohen, 2021;Malik et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Study of the Dynamics of a Cebâşev Manipulator

Petrescu¹

2022

American Journal of Engineering and Applied Sciences

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The paper presents a new method for determining the dynamic parameters of a Cebâşev conveyor mechanism, solvable in two steps. In the first step, the classic method of preserving the kinetic energy of the entire mechanism is used to determine the rotating mass of the Cebâşev mechanism denoted by J*, this being the dynamic rotating mass of the entire mechanism reduced to the driving element 1 (of the crank type; classical rotating mass is called the mechanical or mass moment of inertia). In the second step (with the help of a new method), the dynamic angular speed of the Cebâşev mechanism's crank is directly determined, by using the mechanism's kinetic energy conservation a second time. This eliminates the need to use a more laborious classical method in the second step, such as differential Newton, Lagrange of type 1, Laplace transform, or Fourier, a finite difference method.

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Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

Cited by 13 publications

References 26 publications

Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes

A Deep Reinforcement-Learning Approach for Inverse Kinematics Solution of a High Degree of Freedom Robotic Manipulator

The Study of the Dynamics of a Cebâşev Manipulator

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