Pareto genetic path planning hybridized with multi-objective Dijkstra's algorithm

Ferariu, Lavinia; Cimpanu, Corina

doi:10.1109/icstcc.2014.6982439

Cited by 3 publications

(2 citation statements)

References 14 publications

(22 reference statements)

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“…Although the FPAA hardware uses the weights expressed as conductances, (11), the MATLAB simulation uses these conductances converted into resistances,…”

Section: B Software Simulationsmentioning

confidence: 99%

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Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

Koziol

2020

IEEE Access

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This paper presents a Multi-Objective path planning approach using reconfigurable Analog-Very-Large-Scale-Integrated (AVLSI) circuits. It is significant because it is the first example of floating-gate based analog resistive grid circuits used for Multi-Objective path planning. The two path planning objectives are 1) minimizing path length and 2) minimizing path cost. Three hardware experimental results are presented that implement the approach using a Field Programmable Analog Array (FPAA) circuit. First, an example demonstrates a simple proof-of-concept. Second, an example shows how the FPAA solution compares to an entire solution set for a specific Start and Goal path planning problem. Third, an example shows how the FPAA solution compares to two edge-cases. The edge-cases are the two ideals: ideal lowest cost path, and ideal shortest distance path. Based on these foundational proof-of-concept hardware results, larger environment grids than are currently implementable on the FPAA hardware were simulated to predict performance if a custom FPAA application specific integrated circuit (ASIC) was built for this Multi-Objective path planning purpose. Finally, analysis is presented to address this method's computational complexity.

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“…Although the FPAA hardware uses the weights expressed as conductances, (11), the MATLAB simulation uses these conductances converted into resistances,…”

Section: B Software Simulationsmentioning

confidence: 99%

“…Previous Multi-Objective path planning has been accomplished using techniques such as genetic algorithms [10], Pareto fronts [11], A* [12], Multi-Step A* [13], Multi-Objective D* lite [14], Rapidly Exploring Random Tree (RRT) based algorithms [15], [16], Neuromorphic systems [17], and Dijkstra's algorithm [11], [18].…”

Section: Introductionmentioning

confidence: 99%