N23B-T031 TITLE: Collaborative Multi-Robot Systems by RF-Optical-Quantum Ultra-Low Latency Wireless Networking
OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): FutureG; Integrated Sensing and Cyber; Trusted AI and Autonomy
OBJECTIVE: Design and develop a fully autonomous robotic solution where a multi-robot team in a communication-degraded and GPS-denied environment can complete a mission with minimal human supervision under extreme environmental conditions.
DESCRIPTION: It is well known that the future battlefield will experience complex artificial intelligence (AI) competition. An automated group of drones, or unmanned ships/submarines, is expected to be a primary form of future weapon systems and surveillance/reconnaissance systems. Technology-wise, based on the collected sensor information, each robot collaboratively acts to accomplish the common mission goal of this multi-robot system (MRS) and multi-agent system (MAS). In the meantime, the adversary will develop similar collaborative MRS to form the "competition". A major focus on AI of a single agent or collective data analytics of battlefield, is desirable to elaborate the collaborative MRS to achieve superiority in the battlefield using intelligent machines and systems, provided there is:
(a) effective artificial intelligence/machine learning (AI/ML) among multi-robot, not just AI for a single robot, so that complex strategy and maneuver for these robots can be facilitated, and
(b) ultra-low latency wireless networking to enable fastest possible response to complicated situations in the battlefield, while maintaining low probability of interception and jamming.
The proposed technology is to dominate the winning edge in such "competitions" through the cyber warfare technology in communication and computation, with feature technologies:
1. Cyber topology control: A fully connected cyber topology (sensor observation and communication among robots) would assist achieving the mission. Smart topology control enhances the performance of collaborative MRS.
2. Predictive machine learning for adversary�s movement: achievable through integrating multiple online machine learning techniques, while deep learning as offline reference may further assist.
3. Strategic maneuver to neutralize adversary�s actions: In addition to AI, with the aid of communication, proper selection of action algorithms for each collaborative robot works.
4. Attack the cyber links of the adversary (both communication and AI), to destroy adversary�s cyber topology control and ensures the success of the mission.
There is interest in innovating the two technological frontiers listed above (cyber topology and AI) and developing an integrated solution, to accomplish superior AI capability in the future battlefield, with the following long-term technologies:
1. An MRS that can accomplish the collective goal or mission in a sophisticated and dynamic policy subject to the dynamics in the battlefield, with the shortest possible response time. For example, (a) to intercept one or multiple hypersonic missile(s) toward an extremely high-value asset by collaborative lower-speed anti-missiles, and (b) a group of collaborative drones to attack an adversary�s high-value asset. This research aims at innovative networked AI for MRS.
2. Current secure data links typically suffer delays in the range up to seconds or even tens of seconds, which is not possible to support any real-time collaboration of robots. The fundamental reason behind this is that the communication links and networks have been designed based on human-to-human (H2H) communication, rather than machine-to-machine (M2M) communication. This research aims at wireless M2M networking of minimal end-to-end latency (i.e., < 1 msec).
3. Given the adversary�s capability of electronic warfare, the wireless network must be resilient against jamming and interception. In addition to post-quantum cryptography, a multimode wireless network shall be innovated, which consists of multi-frequency radio frequency (RF), optical wireless, and quantum optical wireless technologies to form the multimode multipath (M3P) transmissions as a secure and resilient ultra-low latency wireless networking for 2. Possible blockchain management of launching codes, and so forth, allows distributed battlefield management to better fit the efficiency of MRS.
There is interest in utilizing emerging classes of miniature (Group 1) Unmanned Vehicles (UVs) for a variety of surveillance and reconnaissance applications in support of the Department of the Navy�s Strategic Blueprint for the Arctic. This SBIR topic seeks to develop and demonstrate a new class of miniature UVs (air, ground, surface, subsurface or a combination thereof). These systems will be air deployed and have the capability to traverse across difficult terrain such as swamps, desert, tundra, and snow or water bodies to satisfy the most demanding mobility requirements of airborne and expeditionary forces. The end goal is a fully autonomous robotic solution where a multi-robot team in a communication-degraded and GPS-denied environment can complete a mission with minimal human supervision under extreme environmental conditions, such as artic and desert temperatures, high altitudes, sand, rain, sleet, and ice.
System Attributes are:
(a) air, surface and subsurface capable,
(b) each robot/agent in the MRS/MAS has its own AI capability to act, and collaboratively accomplish a goal (or mission),
(c) end-to-end latency: less than 1 m/sec,
(d) operate in a communication-degraded and GPS-denied environment,
(e) real-time data output: longitude, latitude, altitude/height, velocity, roll, pitch, yaw/heading, angular rates, acceleration, health status, and calibrated raw data INS/GNSS (for post-processing)
(f) interfaces: RS422 (UART and HDLC/SDLC) interfaces, CANaero/ARINC825/CAN, ARINC429, Ethernet (TCP/IP and UDP), and SYNC-I/Os, and
(g) output and diagnostic measurement system included (full mission duration storage).
Work produced in Phase II may become classified.
Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret NAVY level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.
PHASE I: Describe offense and defense tactics via collaboration in order to compete against the adversary. Define the architecture and topology for ultra-low latency communications and networked AI/ML methodology and operational features. Identify specific sensors or sensor suites to be included and develop the strategy and design of integration and scale of the autonomous platform and onboard processing/architecture. Describe logistics and maintenance strategy. Define the autonomous behaviors, requirements of software and communications to allow cooperative sensor array technology collaboration. The Phase I effort will include prototype plans to be developed under Phase II.
PHASE II: Develop a multimode wireless network architecture of ultra-low latency prototype platform and validate the component integration in terms of physical implementation: architectures, electronics, and communications to facilitate networked AI MRS. Conceptual demonstration of technology (i.e., networked AI to form the collaborative strategy), with one scenario of field demonstration and another scenario of computer simulations. Develop the autonomous behaviors, swarming software and communications defined in Phase I. Perform potential land/sea trial tests of cooperative swarming activities of multiple vessels. Evaluate performance using both single and swarming deployment. Demonstrate ability to operate in various EM environments.
Work in Phase II may become classified. Please see note in Description section.
PHASE III DUAL USE APPLICATIONS: Complete final testing and perform necessary integration and transition for use in multi-platform operations with appropriate current platforms and agencies, and future combat systems (FCS) under development.
Commercially this architecture and product could be used to enable remote airborne environmental monitoring and surveying.
KEYWORDS: Artificial Intelligence/Machine Learning; AI/ML; Quantum; Communication Architecture; Ultra Low-Latency; Communication; GPS denied
** TOPIC NOTICE **
The Navy Topic above is an "unofficial" copy from the Navy Topics in the DoD 23.B STTR BAA. Please see the official DoD Topic website at www.defensesbirsttr.mil/SBIR-STTR/Opportunities/#announcements for any updates.
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