Density-aware decentralised multi-agent exploration with energy constraint based on optimal transport theory

Lee, Kooktae; Kabir, Rabiul Hasan

doi:10.1080/00207721.2021.1976305

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𝜆(𝑡𝑓)1

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2022

2024

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Cited by 7 publications

(2 citation statements)

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“…The optimal control (5) will drive the robot to head toward a location where the highest intensity of gas plume is expected. This optimal control will be calculated in a receding-horizon fashion [8][9][10][11][12][13] with a given horizon length , meaning in each time step, the robot measures the gas plume using an onboard sensor, constructing a function , followed by as shown in (5).…”

Section: Resultsmentioning

confidence: 99%

Optimal Gas Leak Localization and Detection using an Autonomous Mobile Robot

Macias¹,

Lee²

2022

International Conference of Control, Dynamic Systems, and Robotics

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Section: Resultsmentioning

confidence: 99%

Optimal Gas Leak Localization and Detection using an Autonomous Mobile Robot

Macias¹,

Lee²

2022

International Conference of Control, Dynamic Systems, and Robotics

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“…Because the costate variable 𝜆(𝑡) is unchanging for both J1 and J2, the problem is defined by four equations that provide the optimal path for J1 and J2 which are shown in equations ( 13) through (16). Note that equation ( 13) is the costate variable definition for J1 and equation ( 14) for J2.…”

Section: 𝜆(𝑡𝑓)mentioning

confidence: 99%

Using Optimal Control Theory to Improve Path Planning For Battery Energy Maximization of an Unmanned Ground Vehicle

Strebe¹,

Lee²

2022

JMIDS

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Solar powered unmanned ground vehicles (SPUGV) can be used to monitor remote points of interest. Heuristic algorithms have been developed for path planning of SPUGVs in known solar environments while considering the battery energy recharge. However, not all environments have detailed solar maps in general, obstructing the energy-efficient path planning. In this paper, a control algorithm is developed to prioritize the battery life of a SPUGV in an unknown solar environment. The algorithm incorporates a switching cost function where one cost function prioritizes the goal position when the battery on the SPUGV is above a set threshold and the other prioritizes finding solar irradiance peaks to charge the battery. Local solar irradiance peaks are identified by a filtering approach from collected data in a local sample area. From simulations, the algorithm results in the SPUGV reaching the point of interest with a higher battery charge than a direct path to the point without any prior solar mapping.

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