Multi-objective mobile robot path planning based on A* search

Jeddisaravi, Kossar; Alitappeh, Reza Javanmard; Guimarães, Frederico Gadelha

doi:10.1109/iccke.2016.7802107

Cited by 32 publications

(15 citation statements)

References 20 publications

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“…12a is ''% greater than shortest path''. For a particular Monte Carlo iteration k, the x-axis value is calculated according to (12).…”

Section: B Software Simulationsmentioning

confidence: 99%

“…AnalogSim k pathlength − Dijkstra k shortestPath Dijkstra k shortestPath (12) The y-axis of Fig. 12a is ''% greater than lowest cost''.…”

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%

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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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“…12a is ''% greater than shortest path''. For a particular Monte Carlo iteration k, the x-axis value is calculated according to (12).…”

Section: B Software Simulationsmentioning

confidence: 99%

“…AnalogSim k pathlength − Dijkstra k shortestPath Dijkstra k shortestPath (12) The y-axis of Fig. 12a is ''% greater than lowest cost''.…”

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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“…The same work described planning in belief space, with collision probability and multi-objective, proposing one algorithm that is more accurate but with higher computation costs and another that sacrifices accuracy but is more efficient. Using the Mars Rover scenario (3D), Ref [35] developed a MOPP using A* search with real objectives (minimize difficulty, danger, elevation, and length). Usage of the Pareto front and the Pareto optimal in every step of A* was proposed by [36,37] using memetic algorithms in order to optimize both path length and smoothness.…”

Section: Multi-objective Path Planningmentioning

confidence: 99%

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders

Lucas

Hernández-Sosa

Greiner

et al. 2019

Sensors

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Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires extensive planning to ensure vehicle safety and mission success, particularly when dealing with strong ocean currents. As gliders are often involved on missions that pursue multiple objectives (track events, reach a target point, avoid obstacles, sample specified areas, save energy), path planning requires a way to deal with several constraints at the same time; this makes glider path planning a multi-objective (MO) optimization problem. In this work, we analyse the usage of the non-dominated sorting genetic algorithm II (NSGA-II) to tackle a MO glider path planning application on a complex environment integrating 3D and time varying ocean currents. Multiple experiments using a glider kinematic simulator coupled with NSGA-II, combining different control parameters were carried out, to find the best parameter configuration that provided suitable paths for the desired mission. Ultimately, the system described in this work was able to optimize multi-objective trajectories, providing non dominated solutions. Such a planning tool could be of great interest in real mission planning, to assist glider pilots in selecting the most convenient paths for the vehicle, taking into account ocean forecasts and particular characteristics of the deployment location.

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“…There does not exist an algorithm that provides an exact analytic solution to such a problem. A series of methods have been proposed and widely applied to solve the optimization problem, such as simulated annealing algorithm [5], artificial potential field algorithm [6], A* algorithm [7], RRT algorithm [8], polynomial optimization method [9], and heuristic approaches [10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Xia

Zhang

2018

Algorithms

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Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

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Multi-objective mobile robot path planning based on A* search

Cited by 32 publications

References 20 publications

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

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

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

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