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Adaptive Autonomous Robot Teams for Situational Awareness

Adaptive Autonomous Robot Teams for Situational Awareness. Georgia Tech’s Role. Georgia Tech Faculty Prof. Ron Arkin Prof. Tucker Balch Dr. Robert Burridge GRAs Keith O’Hara Patrick Ulam Alan Wagner Matt Powers Mobile Intelligence Inc. Dr. Doug MacKenzie. Personnel.

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Adaptive Autonomous Robot Teams for Situational Awareness

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  1. Adaptive Autonomous Robot Teams for Situational Awareness Georgia Tech’s Role

  2. Georgia Tech Faculty Prof. Ron Arkin Prof. Tucker Balch Dr. Robert Burridge GRAs Keith O’Hara Patrick Ulam Alan Wagner Matt Powers Mobile Intelligence Inc. Dr. Doug MacKenzie Personnel

  3. Impact – GT Role • Provide communication-sensitive planning and behavioral control algorithms in support of network-centric warfare, that employ valid communications models provided by BBN • Provide an integrated mission specification system (MissionLab) spanning heterogeneous teams of UAVs and UGVs • Demonstrate warfighter-oriented tools in three contexts: simulation, laboratory robots, and in the field

  4. Communication Sensitive Planning • Provide support for terrain models and other communications relevant topographic features to MissionLab • Use plans-as-resources as a basis for multiagent robotic communication control (spatial, behavioral, formations, etc.) and integrate within MissionLab

  5. Plans as Resources • Motivated by Payton’s work. • A precompiled map is an enabling resource. • Maps converted to a two dimensional gradient mesh a priori using A*. • Robot queries “internalized plan” for directional “advice” in the form of a vector. • Queries and advice production are near real-time.

  6. Internalized Plan as Behavior • The GoToMapVector assemblage controls retrieval of plan vectors from maps, and consists of the following sub-assemblages: • GetMapVector: Retrieves and injects a map vector • Wander: Inject noise • Avoid Obstacles • MoveToGoal: Only used in experiments of mixed reactive/planning behavior.

  7. Parallel Internalized Plans • Different internalized plans can be combined by fusing individual plans. • Base plan contains only physical objects. • Other plans contain additional constraints. • The robot queries advice from the most constrained plan (pessimistic).

  8. Serial Internalized Plans • Different internalized plans are used one after another. • Each plan offers situation specific advice. • Perceptual triggers transition from only plan to another. • Opportunity for contingency plans.

  9. Initial Results • Additional resources in the form of internalized plans aids team communication. • No difference results when using reactive behaviors vs. communication insensitive plans. • Communication planning in serial and parallel result in significant improvement in communication.

  10. Plans as Resources: Upcoming work • Conduct tests on teams of real robots. • Determine the systems localization and map accuracy requirements. • Develop techniques for dealing with localization errors and map inaccuracies. • Extend the planning to 3D and generalize to other space-time dimensions for multi-robot coordination

  11. Communication-sensitive Team Behaviors • Generation and testing of a new set of reactive communications preserving and recovery behaviors • Creation of communications recovery and preserving behaviors sensitive to QoS • Expansion of behaviors in support of line-of-sight and subterranean operations

  12. Communications Recovery Behaviors • Retrotraverse: Log robot’s position at regular intervals; when comms breaks, move to last N positions logged until comms recovered • Move to Higher Ground: Use inclinometer data to guide ascent to vantage point for communications recovery • Nearest Neighbor: Track the last known position of connected robots; if comms lost, move towards the nearest robot’s last position • Bridging: Couple separated networks by tracking positions and moving towards location of network lesion; currently UAV behavior • Shepherding: Search out robots that have been cut off from the network; once found, guide back (currently UAV)

  13. Experimental Design • Missions run on simulated Quantico map • 20 trials starting at regularly spaced intervals along the western side of the map and moving to a central location on the eastern side of the map • 2 UGVs moving in a line formation with 20m spacing • Recovery behaviors used in isolation of one another • Metrics: Mission Completion Rate, Recovery time

  14. Results Using the Nearest Neighbor Recovery behavior approximately 50% of the trials were finished completely autonomously Retrotraverse and Move to Higher Ground were usually not able to finish the trials autonomously by themselves and will require transitions/planning once communications recovered

  15. Results (2) Retrotraverse results in the most rapid communications recovery of the behaviors tested. Move to higher ground results in the slowest recovery rate, largely due to failure when the terrain was level. Nearest Neighbor was successful in most cases, except in some situations around buildings where the attraction to the lost robot and the repulsion to the building that severed communications causes a local minima

  16. Summary: Communications Recovery • Retrotraverse provides the most rapid communications recovery • Retrotraverse must be augmented with supplementary behaviors or teleoperation to complete mission • Move to Higher Ground and Nearest Neighbor perform effectively in many cases • There are a number of cases where the behavior will perform suboptimally • Supplementary behaviors or a more complex behavioral selection may further improve results

  17. Future Work • Investigate means in which to activate recovery behaviors based on available perceptual features • Integration of cognizant failure (Gat) for recovery behaviors • Evaluate performance of recovery behaviors in the context of larger teams, increased formation size, and disparate goals

  18. Communication-Preserving Behaviors with Limited Memory • Value-Based One-Step Look-Ahead • Uses predictions of communication quality short distances from current position to “hill-climb” to better locations with respect to communication • Currently assumes teammates remain still when predicting communication quality to reduce complexity

  19. Communication-Preserving Behaviors • Operation: • Predict communication quality at locations a small distance away using • Map information • Network attenuation model • Teammates assumed to remain still • Create motion vector based on predicted and current communication quality • Bearing based on predicted quality • Magnitude based on current quality

  20. Communication-Preserving Behaviors Predicted communication qualities (r = .89) Resulting vector X X X X (r = .70) (r = .74) (r = .85) Current communication quality (r = .68)

  21. Communication-Preserving Behaviors Without Look-Ahead Behavior: Obstacle-splitting endangers communication quality

  22. Communication-Preserving Behaviors With Look-Ahead Behavior: Obstacle-splitting phenomena eliminated

  23. Communication-Preserving Behaviors – 1 step • Future work: • Extend behavior to larger groups • Perform quantitative tests • Compare to other communication-preserving behaviors • Identify situations where most effective • Integrate into larger scenarios

  24. Memoryless Communication Preserving BehaviorMaintain-Signal-Strength • Servos on signal strength to preserve communication. • Sum over every “connected” robot • Vector_Magnitude = (T-R)/T when (T-R) > D • Vector_Direction = angle to the robot where T: Target Signal Strength, D: Signal Deadzone, R: Actual Signal strength • Connected can be defined to mean either directly connected or connected via a multi-hop route.

  25. Illustration of Maintain-Signal-Strength g1 g2 Communication Quality Increases Communication Quality Decreases s1 s2

  26. Communication Preservation Experiments • Mission: Each robot navigates to its goal. • Team Sizes: 2, 4, 6, and 8 • Distance separating robots: 10, 20, 40 meters • 25 random worlds • 12% obstacle coverage • 256 x 256 meters • Three behaviors are compared. • No communication behavior (control) • MSS using positions of directly connected robots (single-hop) • MSS using all available positions (multi-hop)

  27. Percentage of Time as One Network • Some communication strategy is needed to keep the network one as you increase the distances or the number of robots. • There doesn’t seem to be a significant difference between the two variations of the behavior.

  28. Mission Completion Time • Both variations of the behavior add a significant amount of time to mission completion.

  29. Communication Models and Fidelity • Working with BBN to incorporate suitable communication models into MissionLab in support of both simulation and field tests

  30. Current Network Model Status • Models wireless communication networks in 3 dimensions. • Integrated into MissionLab • Signal Attenuation • Free-space path-loss • Dependent on distance between robots, frequency of communication band, and antennae height. • Line-of-Sight Obstructions • Absolute signal attenuation. • Obstructions modeled as arbitrary polygons or right cylinders with height. • Terrain map can be used which can occlude LOS.

  31. The Quantico Overlay From a Communications Perspective

  32. Next Steps in Modeling Network • Obstructions will attenuate signal at different magnitudes. • Model buildings and foliage. • Accurate model of signal attenuation over rough terrain. • Mimic capabilities of BBN “black-box” • Understand how different levels of model fidelity impact multi-robot team performance.

  33. Communication-sensitive Mission Specification • MissionLab is a usability-tested Mission-specification software developed under extensive DARPA funding (RTPC / UGV Demo II / TMR / UGCV / MARS / FCS-C programs) • Using MissionLab as a basis: • Adapt to incorporate air-ground communication-sensitive command and control mechanisms • Extend to support physical and simulated experiments for objective air and ground platforms • Incorporate new communication tasks and triggers

  34. MissionLab’s Spatial Planner • Incorporates Navigator Component of the AuRA architecture • - A map of obstacles is read in by the system • - The map is “grown” to represent configuration space • - The free space is partitioned into a collection of convex “meadows” • - Start and End points are selected by the user • - The planner performs A* search to find an initial path • - The path is improved by tautening • Can be invoked from MissionLab’s cfgedit tool • Creates an FSA series of waypoints

  35. Initial Map and Meadow Map

  36. Path Chosen and Formation Run

  37. Technology Integration • Conduct Early-on Demonstrations on Ground Robots at GT • Provide our Hummer Command and Control Vehicle for Team support at Objective Demonstration

  38. Interface Control Document • To explicitly capture all aspects of all interconnections between project components. • Communications protocols, frequencies, and timing • Language and data formats • Experimental communications fault injection • To define new mission description language: CMDL+ • To detail communications-sensitive behaviors developed by project teams. • Communication-preserving • Communication-recovering

  39. BBN PENN (Mounted in GT Hummer) PENN ROCI ICD Ref: 2.3.2 GaTech MLab VIP Display ICD Ref: 2.3.6 XMLRPC ICD Ref:2.3.1 ICD Ref: 2.3.4 ICD Ref: 2.3.7 USC Player USC Helo ICD Ref: 2.3.8

  40. GPS Jammer • Supports evaluation of robot localization methods in challenging environments • White noise centered on selected frequency • Power: 50 to 200mw (about 50-100 meters) • Performance to be characterized in the coming few weeks • Engineered by Daniel Walker (BORG Lab)

  41. Summary - Georgia Tech Contributions • Communications Sensitive Behaviors • Preserving • Recovery • Communications Planning Behaviors • Plans as Resources • One-step planning • Team spatial waypoint planning • Infrastructure • Communications models support • MissionLab as an integration vehicle • ICD Development lead • Hummer base station / Test equipment • Scenario development

  42. Backup Slides

  43. Plans in Serial Demo explained • Seven plans are used in this demo

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