Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 h) were conducted based on the DARPA Subterranean Challenge. Missions involved deploying a heterogeneous team of robots to locate and identify artefacts such as climbing rope, drills and a mannequin representing a human survivor. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human interventions included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items ( \(+\) 10.52%), traversed more distance ( \(+\) 12.71%), covered more unique ground ( \(+\) 10.56%), and longer time between safety-related events (34%). In routine conditions, both condition scores were comparable for artefacts, distance and coverage. Human-Robot Teams were faster at finding the first artefact but slower to respond to information from the robot team. Overall, operators contribute to mission-based outcomes, help to overcome environmental situations that can impede progress, and can assist robots to recover faster from difficult events.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Human-Robot Interaction
Published
2024-09-26
DOI
https://doi.org/10.1145/3665319
Citations
2
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
1.29
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge

Alex Pitt, Fletcher Talbot, Brendan Tidd, Nicole Robinson et al.
2 citations
ACM Transactions on Human-Robot Interaction
Human-Automation Interaction and Safety
1.29
article

Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge

Alex Pitt, Fletcher Talbot, Brendan Tidd, Nicole Robinson, Navinda Kottege, Brett Wood, David Howard, Dana Kulić, Jason Williams
article en
2 citations

Abstract

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 h) were conducted based on the DARPA Subterranean Challenge. Missions involved deploying a heterogeneous team of robots to locate and identify artefacts such as climbing rope, drills and a mannequin representing a human survivor. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human interventions included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items ( \(+\) 10.52%), traversed more distance ( \(+\) 12.71%), covered more unique ground ( \(+\) 10.56%), and longer time between safety-related events (34%). In routine conditions, both condition scores were comparable for artefacts, distance and coverage. Human-Robot Teams were faster at finding the first artefact but slower to respond to information from the robot team. Overall, operators contribute to mission-based outcomes, help to overcome environmental situations that can impede progress, and can assist robots to recover faster from difficult events.

ACM Transactions on Human-Robot InteractionVol. 14(1)
Commonwealth Scientific and Industrial Research Organisation (AU), Monash University (AU)
Openalex Percentile: Top 18%
Human-Automation Interaction and Safety
1.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.