Comparative analysis of selected path-planning approaches in large-scale multi-agent-based environments

Tučník, Petr; Nacházel, Tomáš; Čech, Pavel; Bureš, Vladimír

doi:10.1016/j.eswa.2018.07.001

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…The algorithm can be regarded as a combination of methods of Dijkstra's search algorithm and heuristic methods such as best-first search used to guide the search, consequently resulting in performance improvement, mainly a reduction in computation time while the algorithm preserves optimality and completeness [48]. A fundamental concept in the discussed algorithm is the evaluation function of the "total cost" containing two parts, defined [49] as follows :…”

Section: Path Planning Algorithm Runtime Analysismentioning

confidence: 99%

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

et al. 2021

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The utilization of edge-enabled cloud computing in unmanned aerial vehicles has facilitated advances in autonomous control by employing computationally intensive algorithms frequently related to traversal among different locations in an environment. A significant problem remains in designing an effective strategy to offload tasks from the edge to the cloud. This work focuses on creating such a strategy by employing a network evaluation method built on the mean opinion score metrics in concoction with machine learning algorithms for path length prediction to assess computational complexity and classification models to perform an offloading decision on the data provided by both network metrics and solution depth prediction. The proposed system is applied to the A* path planning algorithm, and the presented results demonstrate up to 94% accuracy in offloading decisions.

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Section: Path Planning Algorithm Runtime Analysismentioning

confidence: 99%

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

et al. 2021

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“…One-step look-ahead strategy is quite simple and suitable for real-time system. However, the performance is not as good as multi-step look-ahead strategy which is proved by many researchers [24][25][26][27][28]. Although multi-step look-ahead strategy has better performance, it works under some conservative hypotheses the performance turns bad when the model is uncorrect.…”

Section: Active CL Algorithm For Mmrsmentioning

confidence: 99%

An A2CL Algorithm based on Information Optimization Strategy for MMRS

Dong¹,

Li²,

Sun³

et al. 2020

KSII TIIS

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Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A 2 CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A 2 CL algorithm's performance finally. Results proved that the presented A 2 CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.

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“…In general, the autonomy, collaboration, and reactivity of agent-based models are the significant advantages to use for modeling different behaviors and interactions such as perception, reasoning, and decision-making, therefore performing different policies from the traffic planning, control, and management perspective. For instance, in transportation study areas, agent-based approaches have been widely used in traffic management frameworks [8][9][10][11], congestion management [12], traffic policy [13], traffic control, and especially traffic signal control [3,[14][15][16][17][18][19] in order to explore the complexity, interdependencies, and systematic structural evolution pattern under complex urban traffic environment [20].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Agent-Based Models for Transport Simulation and Analysis

Huang

Cui

Zhang

et al. 2022

Journal of Advanced Transportation

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This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.

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Comparative analysis of selected path-planning approaches in large-scale multi-agent-based environments

Cited by 10 publications

References 42 publications

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

An A2CL Algorithm based on Information Optimization Strategy for MMRS

An Overview of Agent-Based Models for Transport Simulation and Analysis

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