首页|Recent Studies from University Sains Malaysia Add New Data to Robotics [Comprehensive Technical Review of Recent Bio-Inspired Population-Based Optimization (BPO) Algorithms for Mobile Robot Path Planning]
Recent Studies from University Sains Malaysia Add New Data to Robotics [Comprehensive Technical Review of Recent Bio-Inspired Population-Based Optimization (BPO) Algorithms for Mobile Robot Path Planning]
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NETL
NSTL
IEEE
New study results on robotics have been published. According to news originating from Pulau Pinang, Malaysia, by NewsRx editors, the research stated, “Over recent decades, the field of mobile robot path planning has evolved significantly, driven by the pursuit of enhanced navigation solutions. The need to determine optimal trajectories within complex environments has led to the exploration of diverse path planning methodologies.” Financial supporters for this research include Collaborative Research in Engineering, Science, And Technology. Our news reporters obtained a quote from the research from University Sains Malaysia: “This paper focuses on a specific subset: Bio-inspired Population-based Optimization (BPO) methodologies. BPO methods play a pivotal role in generating efficient paths for path planning. Amidst the abundance of optimization approaches over the past decade, only a fraction of studies has effectively integrated these methods into path planning strategies. This paper’s focus is on the years 2014-2023, reviewing BPO techniques applied to mobile robot path planning challenges. Contributions include a comprehensive review of recent BPO methods in mobile robot path planning, along with an experimental methodology to compare them under consistent conditions. This encompasses the same environment, initial conditions, and replicates. A multi-objective function is incorporated to evaluate optimization methods. The paper delves into key concepts, mathematical models, and algorithm implementations of examined optimization techniques. The experimental setup, methodology, and benchmarking performance results are discussed. Based on the proposed experimental methodology, Improved Sparrow Search Algorithm (ISpSA) shows the best cost improvement percentage (7.87%), but suffers in terms of optimization time.”
University Sains MalaysiaPulau PinangMalaysiaAsiaAlgorithmsEmerging TechnologiesMachine LearningRobotRobotics
NETL
NSTL
IEEE
