Path Planning and Energy Optimization in Optimal Control of Autonomous Wheel Loaders Using Reinforcement Learning

Sardarmehni, Tohid; Song, Xingyong

doi:10.1109/tvt.2023.3257742

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…The method uses the geometry of road and produce different driving styles to support path switching. • A reinforcement learning-based autonomous Wheel Loader (WL) is presented in [27], towards autonomous vehicles support, which uses approximate dynamic programming (ADP). • A fault-tolerant controller for the path-following of independently actuated (IA) electric autonomous vehicles (AVs) with steer-by-wire (SBW) systems is presented in [19].…”

Section: Existing Systemmentioning

confidence: 99%

Directional Optimization Model for Efficient Vehicle Control in Photovoltaic Systems

2024

nano-ntp

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The problem of electric vehicle control with photovoltaic system and maximizing the span of the vehicle has been well studied. The methods estimate the span of vehicle based on the residual energy but does not consider the span maximization by diverting the vehicle according to various factors. This affects the performance of the vehicle and reduces the span expected. To handle this issue, an Directional Optimization Model (DOM) is presented to control the electric vehicle runs with the photovoltaic systems. The model is focused to increase the span of the vehicle than the expected one which is possible by choosing the direction for the vehicle in an optimized way. To perform this, the method uses the route map of the region and collects the traffic data. With the traffic data, the method identifies various routes in the direction of the vehicle and for each of them; it estimates the Directional Span Maximization Support (DSMS). With the value of DSMS, the method identifies the suitable route to reach the destination. The value of DSMS is measured according to number of factors like distance, energy depletion, speed, traffic, number of signals and diversions and so on. By performing directional optimization, the vehicle span has been greatly increased. The proposed method improves the performance of energy utilization and span maximization.

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Section: Existing Systemmentioning

confidence: 99%

Directional Optimization Model for Efficient Vehicle Control in Photovoltaic Systems

2024

nano-ntp

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“…The boom speed dictates the absolute minimum time the navigation can take as the boom has to be raised high enough to make sure the bucket clears the rim of the tipping body before dumping. When examining energy optimization for navigation during the short-loading cycle, boom speed and correct steering movements were found to be essential [50].…”

Section: Navigationmentioning

confidence: 99%

Automating the Short-Loading Cycle: Survey and Integration Framework

Borngrund,

Bodin,

Andreasson

et al. 2024

Applied Sciences

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The short-loading cycle is a construction task where a wheel loader scoops material from a nearby pile in order to move that material to the tipping body of a dump truck. The short-loading cycle is a vital task performed in high quantities and is often part of a more extensive never-ending process to move material for further refinement. This, together with the highly repetitive nature of the short-loading cycle, makes it a suitable candidate for automation. However, the short-loading cycle is a complex task where the mechanics of the wheel loader together with the interaction between the wheel loader and the environment needs to be considered. This must be achieved while maintaining some productivity goal and, concurrently, minimizing the used energy. The main objective of this work is to analyze the short-loading cycle, assess the current state of research in this field, and discuss the steps required to progress towards a minimal viable product consisting of individual automation solutions that can perform the short-loading cycle well enough to be used by early adopters. This is achieved through a comprehensive literature study and consequent analysis of the review results. From this analysis, the requirements of an MVP are defined and some gaps which are currently hindering the realization of the MVP are presented.

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A Location centric Power Factor Based Path Switching Model for Efficient Electric Vehicle Span Maximization

Mahobia

2024

E3S Web of Conf.

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The problem of vehicle span maximization and path switchinghas been well studied. There exists number of approaches towards maximizing the span of electric vehicles. However, the methods suffer to achieve higher performance in maximizing the span of the vehicle. Towards this, an efficient Location centric power factor path switching model (LPPSM) is presented in this article. As the electric vehicles has the limited span which is being affected by various factors like speed, wind, traffic, number of junctions, distance and so on. It is necessary to perform path switching towards maximizing the span of the vehicle. The model fetches the location of the vehicle at all the time; it finds the routes and measures the traffic at each route. According to the factors like number of junctions, traffic, distance, speed of the vehicle, the method estimates the Span Maximization Support (SMS) for various routes. According to the value of SMS, the method selects the most optimal route to reach the destination. Also, the method focused on maximizing the span of the vehicle and performs efficient path switching. The proposed method improves the performance of span maximization and path switching.

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Path Planning and Energy Optimization in Optimal Control of Autonomous Wheel Loaders Using Reinforcement Learning

Cited by 4 publications

References 34 publications

Directional Optimization Model for Efficient Vehicle Control in Photovoltaic Systems

Directional Optimization Model for Efficient Vehicle Control in Photovoltaic Systems

Automating the Short-Loading Cycle: Survey and Integration Framework

A Location centric Power Factor Based Path Switching Model for Efficient Electric Vehicle Span Maximization

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