Integrated optimization of planetary rover layout and exploration routes

Lee, Dongoo; Ahn, Ji-Whan

doi:10.1080/0305215x.2017.1299718

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Specifically, optimization algorithms have been proposed to maximize the robot's time spent in sunlight [39,40], promote the robot's exploration of novel locations [41], and minimize the risk of mechanical failures due to terrain [42]. Contemporary modifications to these optimization algorithms allow the IA to plan routes optimally to goal locations (e.g., Reference [43]) or decrease run times through the use of heuristics [44]. While these algorithms are capable of producing least-cost solutions, the inherent problem with these types of optimization algorithms is that solutions can be counterintuitive to human users, who may prefer simpler routes to more complex ones that offer trivial advantages in the optimization criterion (e.g., travel time).…”

Section: Route Planning Mechanismsmentioning

confidence: 99%

Where Do You Think You're Going?

Perelman

Evans

Schaefer

2020

J. Hum.-Robot Interact.

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Route planning is a critical behavior for human-intelligent agent (H-IA) team mobility. The scientific community has made major advances in improving route planner optimality and speed. However, human factors, such as the ability to predict and understand teammates' actions and goals, are necessary for trust development in H-IA teams. Trust is especially critical when agents' behaviors do not match human team members' expectations, or the human cannot understand the agent's underlying reasoning process. To address this issue, the artificial intelligence community has pushed toward creating human-like agent behaviors using machine learning. The problem with this approach is that we do not yet have a clear understanding of what constitutes human-like behavior across the breadth of tasks that H-IA teams undertake. This article describes an investigation and comparison of human and agent route planning behaviors, the interplay between humans and agents in collaborative planning, and the role of trust in this collaborative process. Finally, we propose a data-driven methodology for characterizing and visualizing differences among routes planned by humans and agents. This methodology provides a means to advance compatible mental model metrics and theory by informing targeted transparency manipulations, thereby improving the speed and quality of routes produced by H-IA teams. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Planning and scheduling; • Computer systems organization → Embedded and cyber-physical systems; Robotics;

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Section: Route Planning Mechanismsmentioning

confidence: 99%

Where Do You Think You're Going?

Perelman

Evans

Schaefer

2020

J. Hum.-Robot Interact.

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“…The design of the routes for a rover exploring the surface of a planet (e.g. Mars) is selected as the first case study subject (Ahn et al 2008;Lee and Ahn 2017). We can imagine various different stakeholder groups interested in the planetary surface exploration, and each of the groups may have its own objective associated with the surface exploration mission.…”

Section: A Case 1: Routing For Planetary Surface Explorationmentioning

confidence: 99%

Vehicle routing problem with vector profits with max-min criterion

Lee

Ahn

2018

Engineering Optimization

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This paper introduces a new routing problem referred to as the vehicle routing problem with vector profits. Given a network composed of nodes (depot/sites) and arcs connecting the nodes, the problem determines routes that depart from the depot, visit sites to collect profits, and return to the depot. There are multiple stakeholders interested in the mission and each site is associated with a vector whose k th element represents the profit value for the k th stakeholder. The objective of the problem is to maximize the profit sum for the least satisfied stakeholder, i.e., the stakeholder with the smallest total profit value. An approach based on the linear programming relaxation and column-generation to solve this max-min type routing problem was developed. Two cases studiesthe planetary surface exploration and the Rome tour caseswere presented to demonstrate the effectiveness of the proposed problem formulation and solution methodology.

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Multi-disciplinary and Multi-objective Design Optimization of a Lunar Rover System with Operational Considerations

Kim

Ahn

2021

Int. J. Aeronaut. Space Sci.

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Integrated optimization of planetary rover layout and exploration routes

Cited by 8 publications

References 11 publications

Where Do You Think You're Going?

Where Do You Think You're Going?

Vehicle routing problem with vector profits with max-min criterion

Multi-disciplinary and Multi-objective Design Optimization of a Lunar Rover System with Operational Considerations

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