ACS has been used for various applications, performance tests, and user experiments.

JIEDDO Patchwork Effort: delivered March, 2011

• About the Project/User Evaluation
• Response to JUONS CC-00333 – Technology Upgrades for EOD and Route Clearance Team Robots
• SPAWAR provided a number of technology upgrades for field testing
• Common Operator Control Unit (OCU) for MTRS MKI and MKII systems
• Common Retro-Traverse System for MKI and MKII systems
• Automatically Deployed Communication Relays
• Handheld OCU for small ("dismounted") EOD UGVs
• Sponsored by JIEDDO and NAVEODTECHDIV
• ACS Participation
• ACS software integrated on an enclosed payload provided a common retro-traverse solution for both MTRS MK1 and MKII systems
• ACS enabled automatic detection of which platform type it was plugged into and configured itself for that platform
• Operation of each system was identical to the user
• Operation not affected by GPS drop-outs
• Expandable for adding new sensors and/or payloads
• Automated retro-traverse was initiated by operator or when UGV lost comms to the operator station
• Possible “return to” locations: start point, stand-off distance from start point, rally point off of previously driven path, or OCU location
• Autonomous capabilities included: adaptive localization (robust to unreliable GPS); waypoint navigation; obstacle avoidance

Army Expeditionary Warrior Experiment (AEWE): Spiral F, April 2009

• About the Experiment
• Army’s Training and Doctrine Command’s live, prototype experimentation campaign
• 4-week long user experiment at Ft. Benning, GA
• Conducted by Army Maneuver Support Center
• Data collection/analysis by Army Test and Evaluation Center
• Complete hand-off of systems to dismounts after 1 day of training
• ACS Participation
• Autonomous capabilities included: obstacle avoidance, GPS and GPS-denied waypoint navigation, path planning, indoor mapping, retro-traverse, return-to-comms
• Provided real-time ground ISR to dismounts: floor plan, robot location, and trajectory plot to operator; robot location and enemy spot reports to soldier network
• Assigned to both Day & Night Missions: Secure & Patrol, Cordon & Search, Defend, and Recon
• Utilized at Battalion level, in both mounted and dismounted configurations
• Integrated on an iRobot PackBot Scout
• Integrated with Raytheon’s GEC2O network architecture (AEWE requirement)

Joint Navy-Army Experiment on the Effects of Progressive Autonomy on Robotic Reconnaissance Tasks, September 2009

• About the Experiment
• Examine the effects of progressive levels of autonomy on robotic reconnaissance task performance
• Each Soldier completed reconnaissance exercises using three different levels of robotic autonomy (teleoperated, semi-autonomous, fully autonomous)
• Automation level and usability were evaluated based on objective performance data, data collector observations, and operator questionnaires (i.e, NASA TLX)
• Building reconnaissance course consisted of three one-story buildings which were similar in size but with different floor plans. Various items were placed in the rooms of each building and arranged to represent a specific enemy activity
• Conducted with ARL:HRED (Human & Research Engineering Directorate), ARL:CISD (Computational and Information Sciences Directorate), and Think-a-Move, Ltd.
• ACS Participation
• Autonomous capabilities included: obstacle detection, warning, and prevention; GPS-denied waypoint navigation; path planning; autonomous exploration; 2-D mapping; retro-traverse
• ACS framework allowed a different mix of behaviors for each level of autonomy tested; enabled real-time switching between autonomy levels
• 90 test runs with 30 soldiers were conducted with no software failures
• Integrated on an iRobot PackBot Scout

Urban Environment Exploration (UrbEE) Performance Testing, FY08 – FY12

• About the Project/Performance Test
• Develop autonomous behaviors with COTS sensors that can reliably perform in challenging urban environments (i.e., unreliable GPS, outdoor/indoor operations, varying structure types)
• Perform objective tests and metric performance
• Conducted over 10 tests at Camp Pendleton training MOUT site since 2009, testing various subsets of autonomous behaviors
• ACS Participation
• Autonomous capabilities included: large-scale, multi-story mapping; stair detection, climbing, and descending; non-GPS navigation; seamless transition between outdoor and indoor environments; return-to-comms; retro-traverse; obstacle detection and avoidance; autonomous exploration for efficient area search
• ACS framework allowed integration and comparison of various sensor types and behavioral algorithms
• Metrics taken: mapping – topological and dimensional accuracy; localization – percentage error of distance traveled

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For more information on ACS, please contact: acs_devel@spawar.navy.mil