GRASP University of Pennsylvania NRL logo? Autonomous Network of Aerial and Ground Vehicles Vijay Kumar GRASP Laboratory University of Pennsylvania Ron.

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Presentation transcript:

GRASP University of Pennsylvania NRL logo? Autonomous Network of Aerial and Ground Vehicles Vijay Kumar GRASP Laboratory University of Pennsylvania Ron Arkin College of Computing Georgia Tech Autonomous Operations Future Naval Capability Unmanned Systems Technology Review

GRASP University of Pennsylvania NRL logo? Adaptive Autonomous Robot TEAMS for Situational Awareness Vijay Kumar Univ Penn Gaurav Sukhatme USC Ron Arkin Georgia Tech Jason Redi BBN A MARS 2020 project Autonomous Operations Future Naval Capability Unmanned Systems Technology Review

GRASP University of Pennsylvania NRL logo? Future Combat Systems Creation of a network-centric force of heterogeneous platforms that is strategically responsive and sustainable l Adapt to variations in communication performance and strive to maximize suitably defined network-centric measures for perception, control and communication l Provide situational awareness for remotely- located war fighters in a wide range of conditions l Integrate heterogeneous air-ground assets in support of continuous operations over varying terrain

GRASP University of Pennsylvania NRL logo? Air Ground Coordination GROUND VEHICLES AERIAL VEHICLES C2 VEHICLE

GRASP University of Pennsylvania NRL logo? Motivation U A V

GRASP University of Pennsylvania NRL logo? Motivation U A V

GRASP University of Pennsylvania NRL logo? Motivation

GRASP University of Pennsylvania NRL logo? Motivation

GRASP University of Pennsylvania NRL logo? Motivation

GRASP University of Pennsylvania NRL logo? Motivation

GRASP University of Pennsylvania NRL logo? Motivation

GRASP University of Pennsylvania NRL logo? Motivation

GRASP University of Pennsylvania NRL logo? Motivation

GRASP University of Pennsylvania NRL logo? Context Communication Network l 400 MHz (100Kbs), 2.4 GHz (10Mbs), 38 GHz (100 Mbs) l Affected by foliage, buildings, terrain features, indoor/outdoor l Directionality Small Team of Heterogeneous Robots l UGVs with vision, range finders l UAVs (blimp, helicopter)

GRASP University of Pennsylvania NRL logo? Research Thrusts 1.Localization and Control for the Team of Robots 2. Communication Sensitive Mission Planning 3. Communication Sensitive Reactive Behaviors 4. Verification and Validation

GRASP University of Pennsylvania NRL logo? 1. Localization, Tracking and Control for a (small) Network of Robots Decentralized l Each robot computes the others position relative to its reference frame l Each robot shares information with the rest of the team Assumptions l Noise can be modeled by normal distribution l No dynamics l Ad-hoc network l Robots are easily identified

GRASP University of Pennsylvania NRL logo? Global Estimators for Position and Orientation Finding the optimal solution involves a graph search problem l NP-complete or optimization over 3(N-1) vars. l Local minima Suboptimal solution l Each robot calls out its observations l Solve for orientations l Solve for relative positions O(N 3 ), but closed form Breadth-first search enables O(N) approximation R2R2 R3R3 R4R4 R5R5 R1R1 R9R9 R6R6 Self-organization, adaptation based on the spanning tree [Das, Spletzer, Kumar, Taylor 02]

GRASP University of Pennsylvania NRL logo? 2. Communication Sensitive Planning l Provide support for terrain models and other communications relevant topographic features to MissionLab l Use plans-as-resources as a basis for multiagent robotic communication control (spatial, behavioral, formations, etc.) and integrate within MissionLab

GRASP University of Pennsylvania NRL logo? Preliminary Results: Communication Planning l Additional resources in the form of internalized plans aids team communication. l No difference results when using reactive behaviors vs. communication insensitive plans. l Communication planning in serial and parallel result in significant improvement in communication.

GRASP University of Pennsylvania NRL logo? 3. Communication Sensitive Team Behaviors l Reactive communications preserving and recovery behaviors l Communications recovery and preserving behaviors sensitive to QoS l Behaviors in support of line-of-sight and subterranean operations

GRASP University of Pennsylvania NRL logo? Control for Communication Modeling of Communication Networks l Effect of foliage l Buildings l Dependence on frequency, directionality l Statistical models of delays and “hot spots” from experimental data u Neighbors, path costs (delays, power) u Time of last communication QoS metrics l Control/perception tasks l Individual robots vs. end-to-end l Move to improve reliability and network performance

GRASP University of Pennsylvania NRL logo? Simulation Six robots maintaining communication constraints and avoiding each other

GRASP University of Pennsylvania NRL logo? Communication Sensitive Behaviors: Preliminary 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

GRASP University of Pennsylvania NRL logo? Satisfy Constraints Maintain Constraints 4. Verification and Validation Unconstrained Behaviors

GRASP University of Pennsylvania NRL logo? Guarantees for Special Cases Complete Graph Tree l In-degree constraint l Directed [Pereira et al, IROS 03] Hamiltonian Cycle (or Path) l In-degree constraint l Undirected [Pereira et al, NRL MRS 03] 1.Reactive Controllers are Potential Field Controllers 2.Assumptions on “constraint” graph

GRASP University of Pennsylvania NRL logo? Three Robot Experiments: Satisfying Constraints

GRASP University of Pennsylvania NRL logo? Deploying a robot network

GRASP University of Pennsylvania NRL logo? Effect of Robot Failures R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 Control GraphConstraint Graph R3R3 R1R1 R2R2 Formation Graph R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 R3R3 R1R1 R2R2 +=

GRASP University of Pennsylvania NRL logo? Conclusion l A comprehensive model and framework integrating communications, perception, and execution l Automated acquisition of perceptual information for situational awareness l Reactive group behaviors for a team of air and ground based robots that are communications sensitive