Human-automated path planning optimization and decision support

Path planning is a problem encountered in multiple domains, including unmanned vehicle control, air traffic control, and future exploration missions to the Moon and Mars. Due to the voluminous and complex nature of the data, path planning in such demanding environments requires the use of automated...

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Veröffentlicht in:International journal of human-computer studies Jg. 70; H. 2; S. 116 - 128
Hauptverfasser: Cummings, M.L., Marquez, J.J., Roy, N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.02.2012
Elsevier
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer science; control theory; systems
Decision support systems
Exact sciences and technology
Human–automation interaction
Information systems. Data bases
Memory organisation. Data processing
Path planning
Software
Theoretical computing
Human–automation interaction
Decision support systems
Path planning
Complex function
Human
Visualization
Automation
Search strategy
Sensitivity analysis
Decision support system
Assisted planning
Human-automation interaction
Optimization
Global local method
Regeneration
Social interaction
Optimal trajectory
Cost function
Field potential
Numerical algorithm
ISSN:1071-5819, 1095-9300
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Zusammenfassung:Path planning is a problem encountered in multiple domains, including unmanned vehicle control, air traffic control, and future exploration missions to the Moon and Mars. Due to the voluminous and complex nature of the data, path planning in such demanding environments requires the use of automated planners. In order to better understand how to support human operators in the task of path planning with computer aids, an experiment was conducted with a prototype path planner under various conditions to assess the effect on operator performance. Participants were asked to create and optimize paths based on increasingly complex path cost functions, using different map visualizations including a novel visualization based on a numerical potential field algorithm. They also planned paths under degraded automation conditions. Participants exhibited two types of analysis strategies, which were global path regeneration and local sensitivity analysis. No main effect due to visualization was detected, but results indicated that the type of optimizing cost function affected performance, as measured by metabolic costs, sun position, path distance, and task time. Unexpectedly, participants were able to better optimize more complex cost functions as compared to a simple time-based cost function. ► An experiment was conducted to understand automation-aided path planning under time pressure. ► Results show that overly salient path changes due to small input changes can lead to poor solutions. ► If automation is imperfect and operators know this, they can effectively adjust their strategies.
ISSN:1071-5819
1095-9300
DOI:10.1016/j.ijhcs.2011.10.001