Effects of Egocentric and Exocentric Supervisor Viewpoint Perspectives on Motion Plan Legibility and Decision Support in Automated Spacecraft Docking Maneuvers

Abstract

Objective

This work analyzes the influence of egocentric versus exocentric viewpoint on the ability of a human supervisor to appropriately takeover or handoff manual control during a spacecraft’s automated rendezvous and docking (ARD) motion.

Background

Previous work showed that automated spacecraft motion factors of initial condition and path curvature influenced supervisor perception of the system and subsequent decision to take over manual control. It was hypothesized that viewpoint may influence the human supervisor’s interpretation of motion trajectories.

Methods

A simulated automated docking procedure was monitored using a Virtual Reality headset by n = 42 participants from three viewpoints (one egocentric and two exocentric perspectives). Monitoring path motion with no prior knowledge of the spacecraft’s targeted dock, participants determined which of two docks the spacecraft was targeting and either asserted manual control or relied on the automation to complete the maneuver. Decision outcomes were analyzed between viewpoints to assess if differences in perspective influence operational decision making on spacecraft paths.

Results

The egocentric viewpoint supported correct takeover and handoff decisions further from the dock. Path characteristics that enabled better or worse decision-making performance differed between egocentric and one exocentric viewpoint but were similar to the other exocentric view.

Conclusion

In addition to existing factors of initial condition and path curvature, supervisor viewpoint significantly influences ARD operational decision making. However, these factors did not account for all differences in outcomes between viewpoints.

Application

Findings inform human-aware motion plan algorithm design for automated spacecraft systems. Locations of cameras used by observers of ARD maneuvers can also be informed.

Keywords

human–automation interaction autonomous agents decision making control theory virtual environments

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