A Multi-Layered Approach to Autonomous Surface Vehicle Map-Based Autonomy

Dalpe, Allisa J.; Cook, Alexander; Thein, May-Win L.; Renken, M. C.

doi:10.1109/oceans.2018.8604602

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…For demonstration purposes (and without loss of generality), PERFORM incorporates two different autonomous path planning techniques for evaluation and direct comparison: (1) a multilayered Potential Field Method/A-Star (PFM/A*) approach [36] and (2) a Probabilistic Roadmap (PRM) method [37]. Details on both algorithms may be found in Appendix A.…”

Section: Resultsmentioning

confidence: 99%

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PERFORM: A Metric for Evaluating Autonomous System Performance in Marine Testbed Environments Using Interval Type-2 Fuzzy Logic

2021

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Trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The authors introduce the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e., providing direct comparisons and quantitative evaluations between autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g., for surveying, surveillance, search and rescue). Three case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission.

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Section: Resultsmentioning

confidence: 99%

“…The PFM and A* layers are then added together and the PFM layer is updated as needed for new obstacles. Expanded details are found in [36]. The specific gain values used in for the simulation in this paper are given in Table A1.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

PERFORM: A Metric for Evaluating Autonomous System Performance in Marine Testbed Environments Using Interval Type-2 Fuzzy Logic

2021

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“…The obstacle avoidance function is one of the functions that must be available in ASV. Many papers discussing obstacle avoidance use A*, RTT, and PRM for autonomous [26] [27] [28]. The experiment of these three methods is done by comparing the performance using simulation.…”

Section: Research Methods 21 Related Workmentioning

confidence: 99%

“…In manual mode, the ARV is controlled directly using Radio Control, while the auto mode will run autonomously from one waypoint to another way-point determined by the user. Auto mode runs closed-loop control the ARV stays on the path between two way-point [28]. However, when the final destination is reached; the ARV will automatically switch from auto mode to hold mode, then the ARV mode automatically changes directly to manually operated.…”

Section: Figure 1 Arv With Double-hull and Aluminium Frame Orange Ball Is External Sensors To Measure Water Qualitymentioning

confidence: 99%

Experiment of Path Planning Using Multi-way Points with A* Algorithm for Autonomous Surface Vehicle

Erfianto

Adrianne

Arif

2021

KINETIK

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Commonly, surveillance activities on lake waters is mostly carried out by using a surface vehicle as special-designed vehicle, especially to conduct water quality measurements, underwater surveys, and bathymetry mapping. However, conventional survey and monitoring still involves humans on the site. If a survey is conducted during strong wind conditions, it could jeopardize surveyor’s safety. Therefore, a vehicle must have several criteria, e.g., it must be pretty spacious and comfortable to carry surveyors, free from engine vibrations, stabilized and easy to maneuver, and the surveyor's safety can be guaranteed. This paper discusses preliminary research aiming to develop an Autonomous Raft Vehicle (ARV), a type of autonomous unmanned surface vehicle. The ARV is equipped with autonomous control based on multi-way-points with an A* algorithm. Thus, a user only requires giving a command once initially during path planning. A* algorithm over multi-way-point could improve ARV navigation when there are obstacles along the predetermined trajectory. Hence the predetermined trajectory will be maintained throughout the mission. It is a significant contribution to this paper.

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The PRM-A* Path Planning Algorithm For UUVs: An Application To Navy Mission Planning

Chowdhury

Schwartz

2020

Global Oceans 2020: Singapore – U.S. Gulf Coast

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A Multi-Layered Approach to Autonomous Surface Vehicle Map-Based Autonomy

Cited by 8 publications

References 8 publications

PERFORM: A Metric for Evaluating Autonomous System Performance in Marine Testbed Environments Using Interval Type-2 Fuzzy Logic

PERFORM: A Metric for Evaluating Autonomous System Performance in Marine Testbed Environments Using Interval Type-2 Fuzzy Logic

Experiment of Path Planning Using Multi-way Points with A* Algorithm for Autonomous Surface Vehicle

The PRM-A* Path Planning Algorithm For UUVs: An Application To Navy Mission Planning

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