Abstract

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.

Highlights

  • This paper represents the first example of floating-gate-based analog resistive grid circuits used for Multi-Objective path planning

  • An example with forty-two different possible paths from start to goal is presented. This example is used to show how the Field Programmable Analog Array (FPAA) solution compares to the entire solution set for a specific start and goal path planning problem

  • An example of an environment with thirteen obstacles, one goal, and thirty-five starting points is described. This example is used to show how the FPAA solution compares to two edge-cases

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Summary

Introduction

This paper represents the first example of floating-gate-based analog resistive grid circuits used for Multi-Objective path planning. Multi-Objective (MO) decision problems are ubiquitous and encountered in diverse fields such as economics [1], health [2], medicine [3], education [4], transportation infrastructure maintenance [5], energy systems [6], and the topic of this paper, path planning for autonomous mobile robots [7]–[9]. Previous Multi-Objective path planning has been accomplished using techniques such as genetic algorithms [10], Pareto fronts [11], A* [12], Multi-Step A* [13], MultiObjective D* lite [14], Rapidly Exploring Random Tree (RRT) based algorithms [15], [16], Neuromorphic systems [17], and Dijkstra’s algorithm [11], [18].

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