A Novel Potential Field Controller for Use on Aerial Robots

Woods, Alexander C.; La, Hung Manh

doi:10.1109/tsmc.2017.2702701

Cited by 44 publications

(29 citation statements)

References 50 publications

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“…In the future, we plan to make our system viable for non-static environment such as when the map changes as well as collecting more data to update the new areas in the dirt model. In addition, we aim to have each of our iRobot be able to do path replanning in case of a moving obstacle like [5], [22] and avoiding trapped by convex shaped obstacles by using a combination of rotational force field [23] and repulsive artificial potential force field [24].…”

Section: Discussionmentioning

confidence: 99%

A Multi-Robotic System for Environmental Dirt Cleaning

Pham

La³

et al. 2020

2020 IEEE/SICE International Symposium on System Integration (SII)

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There is a lot of waste in an industrial environment that could cause harmful effects to both the products and the workers resulting in product defects, itchy eyes or chronic obstructive pulmonary disease, etc. While automative cleaning robots could be used, the environment is often too big for one robot to clean alone in addition to the fact that it does not have adequate stored dirt capacity. We present a multi-robotic dirt cleaning system algorithm for multiple automatic iRobot Creates teaming to efficiently clean an environment. Moreover, since some spaces in the environment are clean while others are dirty, our multi-robotic system possesses a path planning algorithm to allow the robot team to clean efficiently by spending more time on the area with higher dirt level. Overall, our multi-robotic system outperforms the single robot system in time efficiency while having almost the same total battery usage and cleaning efficiency result.

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Section: Discussionmentioning

confidence: 99%

A Multi-Robotic System for Environmental Dirt Cleaning

Pham

La³

et al. 2020

2020 IEEE/SICE International Symposium on System Integration (SII)

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“…g t best is brought in formula (3-11) to get the optimized individual g 0 new at the next moment. That is the increased optimizing population operation, which is shown in formula (3)(4)(5)(6)(7)(8)(9)(10)(11).…”

Section: A Pre-dmpc-de Algorithm For Dmpc Modelmentioning

confidence: 99%

“…The computational efficiency of these algorithms is low, but real-time performance in real application determines the stability of the formation control. Woods [11] et al proposed a control method based on potential energy function. However, with the increasing in quantity of drones and the rising of formation size, the drones added later will have an undesirable consequence on the potential energy function of the formation.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Bian

Sun

2019

IEEE Access

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In this paper we proposed the circle trajectory assembly algorithm to control the multi-UAVs circular assembly formation. CTFAP solution provides rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance. Proposed Distributed model prediction control framework improves the optimization ability and reduces the computation consumption with the better convergence ability of the UAV formation. Firstly, a circular trajectory following algorithm with an adaptive parameter is proposed to complete the rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance during formation forming. Then, in the stage of formation reconfiguration, with distributed model prediction control framework (DMPC), the proposed method gets the prediction information of DMPC to optimize the population of classical differential evolution (DE) algorithm and improve the iterative optimization ability of DE algorithm. Experiments show that the proposed differential evolution algorithm greatly improves the efficiency of solving the formation reconfiguration problem under the DMPC framework and overcomes the disadvantages of random population of classical DE. For the proposed rapid forming method, assembly range is reduced by 41% compared with direct linearly formation assembly, and the formation forming time is reduced by approximately 21%. Compared with other optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and DE, the proposed differential evolution algorithm reduces instruction response time of single-drones by 16%-30%.

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“…All the variables and functions of the dual muscle system are normalized to simplify the dynamics. 40 2 ≤ L S j < 2.04 0.5 + 19.2308(L S j − 2.04) L S j ≥ 2.04 (17) whose graph is shown in Fig. 2.…”

Section: Problem Formulationmentioning

confidence: 99%

“…A tracking extension for the dual muscle system, which includes activation dynamics, is reported in [39]. A control method based on an artificial field approach can be derived as in [40]. Our goal is to design a stable feedback tracking controller for the position of the mass, in which u 1 and u 2 are control inputs.…”

Section: A Backstepping Controllermentioning

confidence: 99%

On the State Estimation of an Agonistic-Antagonistic Muscle System

Nguyen

Warner

Mohammadi

et al. 2017

Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Cont

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Abstract-Research on assistive technology, rehabilitation, and prosthesis requires the understanding of human machine interaction, in which human muscular properties play a pivotal role. This paper studies a nonlinear agonistic-antagonistic muscle system based on the Hill muscle model. To investigate the characteristics of the muscle model, the problem of estimating the state variables and activation signals of the dual muscle system is considered. In this work, parameter uncertainty and unknown inputs are taken into account for the estimation problem. Three observers are presented: a high gain observer, a sliding mode observer, and an adaptive sliding mode observer. Theoretical analysis shows the convergence of the three observers. To facilitate numerical simulations, a backstepping controller is employed to drive the muscle system to track a desired trajectory. Numerical simulations reveal that the three observers are comparable and provide reliable estimates in noise free and noisy cases. The proposed schemes may serve as frameworks for estimation of complex multi-muscle systems, which could lead to intelligent exercise machines for adaptive training and rehabilitation, and adaptive prosthetics and exoskeletons.

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A Novel Potential Field Controller for Use on Aerial Robots

Cited by 44 publications

References 50 publications

A Multi-Robotic System for Environmental Dirt Cleaning

A Multi-Robotic System for Environmental Dirt Cleaning

Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

On the State Estimation of an Agonistic-Antagonistic Muscle System

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