Advanced EOD Robotic Systems based in Open Systems and JAUS Standards

Assignment 2.4 - Research Blog 1: Unmanned Ground Vehicles

Miguel H. Quine

UNSY 501 Applications of Unmanned Systems

Embry Riddle Aeronautical University  - ERAU

 

Advanced EOD Robotic Systems based in Open Systems and JAUS Standard

This Blog was based in the research project "Advanced EOD Robotic Systems based in Open Systems and JAUS Standards" by Miguel H. Quine on October 9 of 2016 for the ERAU - MS Unmanned Systems - ASCI 531-Robotics and Control Course. 

The Department of Defense (DOD), according the unmanned system roadmap for the years 2013-2038, has envisioned the development of unmanned systems with the capacity of interoperability as critical point for the needs of the fleet of the US Forces. The lack of interoperability of the current fleet of unmanned ground vehicle (UGV) occurs because the architectures of hardware and software are based in proprietary systems of different manufacturers with interfaces and software non-compatibles, non–modular design of hardware, different communication systems design for data transmission and processing data. To drive the need of integration of technology of the UGV’s, the Advanced Explosive Ordnance Disposal Robotic System (AEODRS) program was created, which has as main objective the development of a common modular architecture based in Open Systems and the standards of the Joint Architecture for Unmanned Systems (JAUS) to enable capacities of interoperability, modularity and interchangeability of modules.

AEODRS program is a Joint Service program sponsored by the Navy, with the support of the John Hopkins University Applied Physics Laboratory (APL) and industries partners, created with the purpose “to provide joint forces with an improved and modular EOD capability to respond to unexploded ordnance, counter improvised explosive devices and WMD missions” (DOD, 2013). The main objective points to the development of advanced EOD robotic systems with capacities of modularity and interoperability by the definition of a common architecture at physical, electrical, and logical levels of interfaces.

Design Overview with AEODRS Common Architecture:

The categories of the critical capabilities of AEODRS can be grouped in Mobility, Manipulation, Vision, Auditory, and Power.

Mobility of the platform, capacity of manipulation of objects, ability or capacity of vision of the environment, auditory capacity to hear or emit sounds, and capacity of power to enable all functionalities of the system.  

The implementation of these categories lets to identify the potential modules of control CMs for the new architecture of the AEODRS systems. Therefore the control modules identified are: Robotic Mobility CM which is responsible to manage the propulsion system, Power System CM which provides of energy or power for the work of all modules of the robotic system.

Robotic Master Capability Module which provides “common system-level services, including support for configuration (detection, registration, publication, and subscription to services provided by the UGV modules) and communications management” (Jhuapl, 2015).

The Communication module or subsystem of communications enables a data link among the AEODRS or UGV and the Operation Control Unit (OCU). The Robotic Visual sensor is another control module which can handle and control multiple systems of sensors.

The Robotic Manipulator Capability Module provides the grasping of the objects of interest and generally is “implemented with a multi-segment jointed arm, the module provides for control and operation of the arm” (Jhuapl, 2011). “The End-effector CM attaches to the distal end of the Manipulator arm and provides the means to grasp or manipulate an object of interest” (Jhuapl, 2015). The “Autonomous Behaviors CM (CM-AB) implement autonomous navigation, high-level manipulation behaviors, and other autonomous and semiautonomous control behaviors.” (Jhpl, 2011)  

 Figure 1. AEODRS Distributed Architecture Concept and Capability Modules. Copyright 2011 by Navy – Jhuapl.  

                                    

    

 Figure 2. AEODRS-Dismounted Operations vehicle. Copyright 2011 by NAVY – JHUAPL

 

 Figure 3. Components AEODRS-DismountedCopyright 2011 by NAVY – JHUAPL

Logic Design

The logic design of AEODRS is mainly based in the term Capability Module which consists of components (Electrical, mechanical, logical) that are needs to get specific capabilities of a system. Capability modules of AEODRS compact a “fundamental capability and presents a standard set of interfaces (logi­cal, electrical, and physical) to the robot platform while preserving the native interfaces to each sensor, actuator, or other device on which it relies.” (Navy-Jhuapl, 2011)

The AEODRS is a distributed system with capability modules connected in physical and logical manner in a single whole intra-network. This network is logically interfaced with the Operation Control Unit system which at the same time is an intra-network for remote control functionalities.

            The “AEODRS program has adopted the Joint Architecture for Unmanned Systems (JAUS) protocols, services, and messages as the core of its inter-module communications architecture” (JAUS, 2010). The standards of JAUS, adopted by AEODRS, enable interoperability capacities in the AEODRS. The migration to JAUS standards is coordinated with the help of the Society of Automotive Engineers (SAE).

The logical JAUS architecture define the communication services communication services between subsystems of AEODRS through the inter subsystems network that include the messaging inter networks subsystems of the AEODRS vehicle and the OCU and the communications between capability modules of the intranet of the AEODRS vehicle. 

                   Figure 4. Logical Distributed Architecture. Copyright 2013 by NAVY- JHUAPL

References:

Advanced Explosive Ordnance Disposal Robotic System (2011). Retrieved from

            http://techdigest.jhuapl.edu/TD/td3003/30_3-Hinton.pdf 

An Integrated System for Autonomous Robotics Manipulation. (2012, October). Retrieved

from http://repository.cmu.edu/cgi/viewcontent.cgi?article=1861&context=robotics

Integration of Advanced Explosive Ordnance Disposal ... (2013). Retrieved August 17, 2016,

from http://www.jhuapl.edu/techdigest/TD/td3203/32_03-Hinton.pdf 

DoD. (2014). Unmanned systems integrated roadmap FY2013-2038. Retrieved from

http://www.defense.gov/Portals/1/Documents/pubs/DOD-USRM-2013.pdf

“JAUS Service Interface Definition Language”, AS-4C Information Modeling and Definition

Committee, July 2010 

Joint Architecture for Unmanned Systems. (2006). Retrieved August 17, 2016, from

http://dtic.mil/ndia/2006targets/Wade.pdf 

Quine, Miguel H. (2016, October 09). Advanced EOD Robotic Systems based in Open Systems and JAUS Standards- ERAUMS Unmanned Systems - ASCI 531-Robotics and Control Course.

US Air Force Multi Sensors Data Integration for Autonomous Sense and Avoid

MS Unmanned Systems - Embry Riddle Aeronautical University

7.4 - Research Assignment: Sense and Avoid Sensor Selection 

  US Air Force Multi Sensors Data Integration for Autonomous Sense and Avoid

US Air Force, through its sponsored Northrop Grumman Corporation, have developed and tested autonomous systems with sense and avoid (SAA) capacity. The design is scalable and based in “a comprehensive sensor suite comprising Traffic Alert and Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B) for detecting cooperative intruders as well as radar and electro-optical (EO) sensors for detecting non-cooperative intruders” (US Air Force, 2011).

The research is focused on the integration of data of the sensors, as part of the whole sense and avoids system.   

Integration of sensor data

“Multi-Sensor Integrated Conflict Avoidance (MuSICA) and consists of four major modules: Sensor Input Management (SIM), Sensor Data Integration (SDI), Jointly Optimal Conflict Avoidance (JOCA), and Flight Control Interface (FCI)” (USAF, 2011)

 

The module SIM consists in a pre-processing stage from the sensor of SA; the module SDI receives the data from the SIM and after processing, the output is an integrated tracking data about intruder. The module JOCA receives the processed data from SDI to analyze the situation and generate maneuver commands if it’s necessary to control the collision avoidance. These commands are received by the FCI which acts as a human pilot over the Flight control system of the vehicle.  The architecture of the SAA shows that the sense is addressed by the modules SIM and SDI, and the modules JOCA and the flight control interface addresses the avoid process.

Characteristics of the sensors for SAA

The suite of sensors for SAA involves different types of sensors such as “cooperative and non-cooperative as well as active and passive” (USAF, 2011). Cooperative sensors are considered omnidirectional and includes sensors TCAS and ADS-B, Non-cooperative are considered directional and at the same time can be divided in active (Radar/ LIDAR) or passive (EO/IR). “TCAS is an airborne secondary surveillance radar (SSR) system with surveillance and collision avoidance functions; An ADS-B-equipped aircraft broadcasts its own position and associated accuracy and integrity information to other ADS-B-equipped aircraft and ground receivers” (USAF, 2011).

Normally, the positions of UAVs are determined by a Global Positioning System (GPS) and altimeter by sensors of pressure; therefore, ADS-B is not strictly required for all national space of the USA. TCAS expects for acknowledgements from the receiver vehicle unlike the ADS-B, but both are cooperative sensors. At commercial level, the active radar sensors for UAVs are not available yet, but some laboratories such as MIT Lincoln Laboratory, has developed the “Airborne Sense and Avoid (ABSAA) Radar Panel” (MIT, 2014). EO/IR sensors are non-cooperative passive sensors which are mainly used for ground operations. 

 SDI- Objective of the Design

The objective of the design of sensors data integration, as its name states, is the integration of the multiple sensors with dissimilar characteristics with the purpose to provide a high precision and hardy tracking of intruder for conflict and avoidance of collision. Sub goals or specific objectives are focused on integration of the all-suite of sensors, generation of integrated tracking, getting better “features of dissimilar sensors in terms of accuracy, integrity, continuity, and availability (AICA) in the integrated tracks, and Achieve modular and efficient real-time software implementation” (USAF, 2011).

SDI - Architecture

Sense data integration architecture is composed of four functional modules “Extended Kalman Filter (EKF), Data Association, False Track Filtering (FTF), and Track Manager“(USAF, 2011).

The internal flow starts with the input of measurements from multiple sensors to the Track Manager; the module EKF is the responsible to propagate the tracking which were predicted and update them by addition of the updated tracking; the data association module synchronize measures with predicted tracks; then, the Track Manager analyze and establish the new data base of tracks and the initiation of new tracks or termination of old tracks; the FTF track establishes if the detection or sensing of some track is true or false; and finally the output of the updated track is declared to the function of avoidance of the collision.

 References

MIT Lincoln Lab's Airborne Sense and Avoid (ABSAA) Radar Panel Wins R&D Award - UAS VISION. (2014). Retrieved July 18, 2016, from http://www.uasvision.com/2014/08/27/mit-lincoln-labs-airborne-sense-and-avoid-absaa-radar-panel-wins-rd-award/

Multi-Sensor Data Integration for Autonomous Sense and Avoid. (2011). Retrieved July 18, 2016, from http://arc.aiaa.org/doi/pdfplus/10.2514/6.2011-1479