Energy Aware Particle Swarm Optimization as search mechanism for aerial micro-robots

Mostaghim, Sanaz; Steup, Christoph; Witt, Fabian

doi:10.1109/ssci.2016.7850263

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…Mai et al [15] use the travelled distance to approximate energy consumption of a robot. Mostaghim et al [13] assume that energy is spent for certain manoeuvres like starting and landing and that mission time is a key factor for the energy consumption of quadcopters. Bartashevich et al [14] only approximate the additional energy spend in the presence of external influences like wind and assume a fixed baseline consumption.…”

Section: State Of the Artmentioning

confidence: 99%

“…Unfortunately, the paper uses a very restrictive movement model. However, the disambiguate between idle state and hovering consumption is very beneficial for swarm behaviour, because shutting down movement can be used by the decision-making to save energy (see Mostaghim et al [13]).…”

Section: State Of the Artmentioning

confidence: 99%

“…However, the critical problem of energy efficiency is seldom investigated. Mostaghim et al [13] designed an energy-efficient modification of the Particle Swarm Optimisation (PSO) Algorithm, which can be applied to quadcopter search missions. Bartashevich et al [14] worked on energy-efficient quadcopter movement under environmental influences (especially wind).…”

Section: Introductionmentioning

confidence: 99%

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Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles

Steup

Parlow

Mai

et al. 2020

Drones

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The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and take appropriate decisions during the mission, a reliable, accurate and adaptive energy model is of utmost importance. However, most existing approaches either use very generic models or ones that are especially tailored towards a specific UAV. We present a generic energy model that is based on decomposing a robotic system into multiple observable components. The generic model is applied to a swarm of quadcopters and evaluated in multiple flights with different manoeuvres. We additionally use the data from practical experiments to learn and generate a mission-agnostic energy model which can match the typical behaviour of our quadcopters such as hovering; movement in x, y and z directions; landing; communication; and illumination. The learned energy model concurs with the overall energy consumption with an accuracy over 95% compared to the training flights for the indoor use case. An extended model reduces the error to less than 1.4%. Consequently, the proposed model enables an estimation of the energy used in flight and on the ground, which can be easily incorporated in autonomous systems and enhance decision-making with reliable input. The used learning mechanism allows to deploy the approach with minimal effort to new platforms needing only some representative test missions, which was shown using additional outdoor validation flights with a different quadcopter of the same build and the originally trained models. This set-up increased the prediction error of our model to 4.46%.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles

Steup

Parlow

Mai

et al. 2020

Drones

Self Cite

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“…Reducing energy consumption to increase mission life is another important research area in swarm robotics, focusing on a diverse set of topics, such as efficient decision making [20] , minimization of traveling distance [21] , energy efficient communication for swarm robot coordination [22] , decreasing the usage of ranging sensors [23] , and autonomous recharging [24] . In this paper, we present a novel approach to avoid congestion that may occur due to the overpopulation in either of the available gaps between the obstacles, resulting in delays and consequently higher energy consumption of the agents as well as the swarm as a whole.…”

Section: Introductionmentioning

confidence: 99%

Swarm formation morphing for congestion-aware collision avoidance

Haghbayan

Yasin

Plosila

2021

Heliyon

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The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the proposed approach results in substantial energy savings as compared with the traditional methods.

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“…While PSO has been shown to be a very efficient optimization algorithm for many optimization problems [ 6 , 7 , 8 , 9 ], where its performance relies on the existence of a non-zero gradient in a search space. In contour search, existence of a gradient can not be guaranteed, with a search space consisting of a flat fitness landscape of equal values and non-monotonic jumps from one fitness value to the other.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Contour Search Algorithm

Weikert

Mai

Mostaghim

2020

Entropy

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In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

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Energy Aware Particle Swarm Optimization as search mechanism for aerial micro-robots

Cited by 13 publications

References 8 publications

Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles

Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles

Swarm formation morphing for congestion-aware collision avoidance

Particle Swarm Contour Search Algorithm

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