Adaptive Autonomous Robot Teams for Situational Awareness Georgia

Adaptive Autonomous Robot Teams for Situational Awareness Georgia

Adaptive Autonomous Robot Teams for Situational Awareness Georgia Techs Role University of Pennsylvania GRASP Personnel Georgia Tech Faculty Prof. Ron Arkin

Prof. Tucker Balch Dr. Robert Burridge GRAs Keith OHara Patrick Ulam Alan Wagner Matt Powers Mobile Intelligence Inc. Dr. Doug MacKenzie University of Pennsylvania

GRASP 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 University of Pennsylvania

GRASP 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 University of Pennsylvania GRASP Plans as Resources Motivated by Paytons 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. University of Pennsylvania GRASP 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. University of Pennsylvania GRASP 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). University of Pennsylvania GRASP 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. University of Pennsylvania

GRASP University of Pennsylvania GRASP 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. University of Pennsylvania

GRASP 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 University of Pennsylvania GRASP 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-ofsight and subterranean operations University of Pennsylvania GRASP Communications Recovery Behaviors Retrotraverse: Log robots 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 robots 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) University of Pennsylvania GRASP University of Pennsylvania GRASP 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 University of Pennsylvania GRASP Results

Trials Completed Number of Trials Completed 20 18 16 14 12 10 8 6 4 2 0 19 18 No Preserving, No Recovery No Preserving, Higher Ground No Preserving, Nearest Neighbor

11 8 No Preserving, Retrotraverse Maintain Signal Strength, No Recovery Maintain Signal Strength, Higher Ground 2 0 0 0 Maintain Signal Strength, Retrotraverse Maintain Signal Strength, Nearesr Neighbor Communication Sensitive Behaviors 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 University of Pennsylvania GRASP Results (2) Communications Recovery Time 600 500 Time No Preservation, No Recovery 400 No Preservation, Higher Ground

300 No Preservation, Nearest Neighbor No Preservation, Retrotraverse M aintain Signal Strength, No Recovery M aintain Signal Strength, Higher Ground 200 M aintain Signal Strength, Retotraverse M aintain Signal Strength, Nearest Neighbor 100 0 Communications Behaviors 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 University of Pennsylvania GRASP 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 University of Pennsylvania GRASP 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

University of Pennsylvania GRASP 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 University of

Pennsylvania GRASP 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 University of Pennsylvania GRASP Communication-Preserving Behaviors X Predicted communication qualities (r = .89)

Resulting vector X (r = .74) Current communication quality X (r = .70) (r = .68) X University of Pennsylvania GRASP (r = .85)

Communication-Preserving Behaviors Without Look-Ahead Behavior: Obstacle-splitting endangers communication quality University of Pennsylvania GRASP Communication-Preserving Behaviors With Look-Ahead Behavior: Obstacle-splitting phenomena eliminated University of

Pennsylvania GRASP 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 University of Pennsylvania GRASP Memoryless Communication Preserving Behavior Maintain-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. University of Pennsylvania GRASP Illustration of Maintain-Signal-Strength g1 g2 Communication Quality Increases Communication Quality Decreases s1 University of

Pennsylvania GRASP s2 University of Pennsylvania GRASP 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) University of Pennsylvania GRASP 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 doesnt seem to be a

significant difference between the two variations of the behavior. University of Pennsylvania GRASP Mission Completion Time Both variations of the behavior add a significant amount of time to mission completion. University of Pennsylvania GRASP Communication Models and Fidelity Working with BBN to incorporate suitable communication models into MissionLab in support of both simulation and field tests

University of Pennsylvania GRASP 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. University of Pennsylvania GRASP The Quantico Overlay From a Communications Perspective University of Pennsylvania

GRASP 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. University of Pennsylvania GRASP 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 communicationsensitive command and control mechanisms Extend to support physical and simulated experiments for objective air and ground platforms Incorporate new communication tasks and triggers University of Pennsylvania

GRASP MissionLabs 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 MissionLabs cfgedit tool Creates an FSA series of waypoints University of Pennsylvania GRASP Initial Map and Meadow Map University of

Pennsylvania GRASP Path Chosen and Formation Run University of Pennsylvania GRASP Technology Integration Conduct Early-on Demonstrations on Ground Robots at GT Provide our Hummer Command and Control Vehicle for Team support at Objective Demonstration

University of Pennsylvania GRASP 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 University of Pennsylvania GRASP (Mounted in GT Hummer) .3 .2 ef: 2 R ICD .4 USC

3. 1 f: 2 .3 .6 XM L 2 .3 USC f:2 .

Re f: Re Displa y Re IC D IC D D IC VIP

ROCI RP C GaTec h MLab BBN Playe r Helo ef: 2.3 ICD R .8

University of Pennsylvania GRASP PENN PEN N ICD R .7 2.3 : f e 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) University of Pennsylvania GRASP 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

University of Pennsylvania GRASP Backup Slides University of Pennsylvania GRASP Plans in Serial Demo explained Seven plans are used in this demo University of Pennsylvania GRASP

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