CONCEPTS FOR AUTONOMOUS COMMAND AND CONTROL

Fernando Escobar and John McDonnell

Innovation center, Spawar sys ctr – San Diego, San Diego, CA 92152, U.S.A.

Keyword: Autonomous, Unmanned Systems, Self Organizing, MANET, Operational Orders, Multi-objective

Optimization.

Abstract: The new Department of Defense (DOD) transformational doctrines for future battlefield operations

emphasizes the need to more aggressively pursue program developments with unmanned systems

technologies. Currently, there are ongoing Battle Experiments testing and assessing the operational

performance of these technologies. These experiments in turn are uncovering current and future capability

gaps that need to be fulfilled with aggressive research, engineering, test and evaluation. The Innovation

Center at SPAWAR Systems Center, San Diego, has established a research and development process to

better address Future Naval Capability gaps in the areas of both, Intelligent Autonomy and Autonomous

Command and Control for Unmanned Systems. In this paper we report our research on two important

components concepts for AC2: 1) Autonomous Resource Allocation, 2) Autonomy and Commanders Intent,

and 3) A discussion on Self organizing C2.

1 INTRODUCTION

Sea Power 21 is a Naval vision that seeks to

transform defense processes and modernize

technologies for the battlefields of the future. The

greatest challenges to transforming Naval doctrines

from the industrial age to the information age has

been the development of a clear notion of the value

that distributed command architectures bring to

modern combat Fig1. Distributed command

architectures bring increased update speed of

situational awareness. Each modernization step in

C4ISR technology that enables faster horizontal

integration is one step closer to a fully distributed

command structure allowing for near real-time

transmission of intent from the Commander on

downwards resulting in better Situational Awareness

of the Battlefield. Intention awareness is therefore an

integral part of distributed command architecture

and must be properly established in the information

environment where faster and optimum execution of

mission objectives is needed.

The fundamental infrastructure e

nabling

command and control (C2) is undergoing a

revolutionary change. The assumptions embedded in

traditional C2 such as a centralized decision

authority and well-defined hierarchy are being

reassessed, especially in light of mission areas that

involve coalition operations and the emergence (and

dependence) on a ubiquitous IT capability (Alberts,

2007). While moving away from traditional C2 to a

net-centric environment represents unique

challenges, the prevalence of unmanned systems

must also be considered within the context of

emerging architectures and concepts. If properly

architected, unmanned C2 systems should meld

seamlessly into the operational environment

augmenting and working in concert with C2 for

manned units. Most investment in autonomy is being

made at the platform level. This work focuses on the

next level of autonomy- that is, the autonomous

interaction of autonomous platforms to achieve pre-

specified objectives.

The DoD Definition for C2 i

s given (Joint

Publication, 2002) as the exercise of authority and

direction by a properly designated commander over

assigned and attached forces in the accomplishment

of the mission.

Autonomous i

s defined as not controlled by others or

by outside forces; independent and independent in

mind

or judgment; self-directed.

Considering these definitions, Autonomous

ommand and Control (AC2) can be defined as the

independent, self-governed exercise of authority and

direction over the assigned forces in the

accomplishment of the mission.

322

Escobar F. and McDonnell J. (2008).

CONCEPTS FOR AUTONOMOUS COMMAND AND CONTROL.

In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics, pages 322-329

DOI: 10.5220/0001507403220329

 SciTePress



Figure 1: Unmanned Systems in DODs Transformational Information Architecture. (http://www.army.mil/fcs/).

Figure 2: Autonomous Command & Control (AC2) for Self Organizing Unmanned Systems.

The prevalence of unmanned systems has increased

dramatically across the DoD services in recent

engagements. In addition, user acceptance has

become well established over this time ensuring that

unmanned platforms will remain pervasive in future

conflicts. Recently released Master Plans for both

USVs (UUV, 2007) and UUVs (UUV, 2004) allude

to the need for autonomous group/cooperative

behavior to achieve the desired mission objectives

for these types of systems. Fig. 2 illustrates the self

organizing concepts of a disparate set of unmanned

platforms.

The capabilities required to achieve AC2 include:

• Self-Organizing C2

• Translate Commander Intent to Executable

Missions

• Autonomous Allocation and Management

of Resources

• Machine Learning from

Training/Experience

• Near Real-Time Analysis for predicting

future C2 actions

• Seamless Interoperability of C4ISR

Systems

CONCEPTS FOR AUTONOMOUS COMMAND AND CONTROL

323

• Sufficient BW and communications

• Autonomous Platforms and Sensing

• Level 3, 4 Fusion

The first three bullets are elaborated on in the

following sections. While critical to achieving AC2,

the remaining topics are advancing under a myriad

of other efforts. For example, the seamless

interoperability of C4ISR systems is being addressed

under next-generation C2 efforts which are focused

on providing a Service Oriented Architecture (SOA)

to the warfighter. In addition to architecture, mobile

add-hoc networks (MANET) are being studied to

determine the best methodologies to achieve self-

forming/self-healing networks and provide desired

QoS levels. Bandwidth utilization will continue to

improve with spectrum management, compressed

sensing, along with novel routing and radio

capabilities. Higher levels of sensor fusion are being

rigorously investigated in order to ascertain enemy

course-of-action analysis, turn data into

understanding and wisdom, and autonomously

improve sensor fusion capability. Autonomous

sensing is also in the critical path as that dovetails

with the allocation and management algorithms that

are incumbent in AC2. Finally, significant

investment continues in imbuing individual

platforms with autonomy and analyzing the benefits

of shared information/awareness.

2 SELF ORGANIZING C2

The key attributes of next-generation C2 include

agility, focus, and convergence (Alberts, 2007).

Agility is the ability of distributed platforms to self-

synchronize and organize into an appropriate C2

topology in a dynamic manner. Self-synchronization

will determine the decision rights across the

platforms, and, in effect, serve as part of the cost

function in the formation of the C2 topology. It is

imperative that any self-organizing C2 topology

yield deterministic behavior(s)

. The salient features

that should be used to automatically determine an

appropriate C2 topology remain to be discovered.

Intuitively, the decision space could include the

number of assets, the information capacity of the

assets, the connectivity bandwidth between assets,

and mission and environmental complexities. For

purposes of discussion, C2 topologies are

characterized in (Figure 3) as centralized, localized,

and distributed. If, for example, a key component for

determining C2 topology is the number of assets in

the area of interest, then thresholds could be

configured to trigger the formation a different

topologies as exemplified in Figure 4. In addition to

determining the salient factors, there is significant

challenge is in determining the threshold functions.

A more effective approach may consider

decomposing the problem such that these lower-

level categories are mapped into the higher levels

characterizations of information distribution,

interaction patterns, and allocation of decision rights

such as discussed by Alberts (Alberts, 2007). This

hierarchical decomposition may serve to simplify

the complexities involved in determining effecting

AC2 topologies.

3 COMMANDER’S INTENT

The understanding of Commanders Intent (CI)

clearly demonstrates that although the concept of

intent has been in our doctrine for quite a while,

confusion still exists and there is little empirical

investigation into the process of communicating

intent.

Figure 3: C2 Topologies: (a) Centralized, (b) Localized, and (c) Distributed.

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Figure 4: A notional mapping between the number of assets in the region of interest and the most effect C2 Topology.

CI has long been used to guide the actions of

subordinates, but has only recently been formally

included in doctrine. CI first appeared in US Army

Field Manual in 1982 (GPO, 1982). During the

1970s, when the military tended to centralize

decision making; however, failed hostage missions

and similar events signaled the need to empower

subordinate players on the scene. A model of today

concept of CI can be traced to Army doctrine writers

that used the German army’s Aftragstaktik (Silva,

1989) first introduced in the early 19

century. The

word means “mission-oriented” reflecting the

developments in response to the French Revolution.

This mission oriented methodology was the

realization that battle is marked by confusion and

ambiguity and that trust between superior and

subordinate is the cornerstone of mission-oriented

combat. Today, CI consists of a brief directive,

usually in written format with a purpose, a method,

and an endstate for any given operation. It is also the

single unifying focus for all subordinates elements

or groups of a command structure which are

dedicated to different activities (communication,

Intelligence, surveillance…) but which

cooperate/collaborate to achieve mission

effectiveness and success.

3.1 Concepts for Automating

Commanders Intent (CI)

Automating Commanders Intent (CI) and military

courses of action are very complex and difficult

activities. These activities should take into

consideration environmental information,

predictions, the end state targeted and resource

constraints. Automating Commanders Intent

involves solving simultaneously planning and

scheduling problems. In this section we provide 1)

an approach to transforming CI objectives into an

algebraic form, 2) a discussion on task scheduling,

optimization, and resource allocation.

3.1.1 Algebraic Representations of CI

An approach to transforming CI into an algebraic

form can best be described by the flow diagram

Figure 5. As mentioned above, a CI consists of a

brief directive containing objective statements. The

first transformation (formalization) of these

statements is done by utilizing a formal specification

language such as the one provided by Berzin & Luqi

(Berzins and Luqi, 1991). Formal statements of

objectives and constraints are then stored

permanently on a database. A Natural Language

Processing (parsing) function aided by a Naval

Lexicon provides formal unambiguous objective

statements for encoding; the encoder creates an

algebraic representation of these objectives creating

what we call elementary actions. The elementary

actions together with proper task scheduling

algorithms, multi-objective optimization functions,

and resource allocation methods provide a

framework for automating Commanders Intent.

3.1.2 Task Scheduling & Optimization

We suggest a task (course of action) approach to

automating Commanders Intent based on

evolutionary algorithms that use multi-objective

optimization methods and support resource

constrained CI development with both cardinal and

ordinal objectives.

During the development step, the commander

analyses the relative combat power of friendly and

enemy forces, and generates the CI.

CONCEPTS FOR AUTONOMOUS COMMAND AND CONTROL

325

Figure 5: An approach for transforming CI into a formal algebraic representation.

During the mission analysis, the objectives are

identified, assigned, and tasks (courses of action) are

implemented to perform the mission. These tasks

can be decomposed into sub-tasks. Tasks and sub-

tasks can be represented by means of a hierarchical

structure –a Graph. Synchronization analyses leads

to identifying temporal and spatial relationships

between elementary tasks. The automating algorithm

must consider all available resources and capabilities

and assign them to tasks. Synchronizing tasks then

requires scheduling of all tasks according to resource

availability, deployment constraints, and task

relationships. We provide a task (courses of action)

planning model as a multiple mode resource-

constrained scheduling problem (MRCPS) since,

from a methodological point of view planning and

scheduling are not much different. Our model

consists of representing generic activities (tasks with

specific combinations of resources) into elementary

(or primitive) actions interrelated to accomplish the

mission objectives. This process implies the

identification of the tasks (when and where),

precedence relationships, the pool of available

resources with their localization, and the objectives

of the mission. An objective is then represented as

an oriented time-space graph of tasks. Figure 6.

Figure 6: An Objective represented as a task.

Depending on the combination of resources

allocated and the actions in the scheduler, different

courses of action networks could be obtained, such

as the one above. They constitute variants (or

alternatives) of a mission with different evaluations

on objectives. Solving CI and courses of action

planning problems is NP-Hard. But a feasible

process for automating CI with respect to multiple

objectives for resource allocation may include

evolutionary algorithms (EA) with meta-heuristic

approaches or a method that addresses the multi-

objective aspect of resource-constrained scheduling

problems in which all objectives are combined into

one single scalar value by using weighted

aggregating functions. The search is then performed

several times to find a compromise solution that

reflects these preferences. Another approach is to

generate the set of compromise solutions in a single

execution of the optimization such as done by

multiple-objective Evolutionary Algorithms. In this

section, we provide a construct for the tasking and

resource allocation associated with a CI that can be

implemented using multiple-objective EAs.

Evolutionary Algorithms are able to deal

simultaneously with multiple solutions for solving

multi-objective optimization problems allowing a set

of potential Pareto optimal solutions to be found in

the same iteration.

Here is our construct: Multi-objective CI can be

characterized by a set of tasks, a set of resources,

precedence relationships, resources, constraints and

global performance functions F

shown in Figure 7.

Once a CI has been decomposed into its requisite

tasks, the question of which autonomous unmanned

system should be responsible for executing each

particular task still remains. Many techniques for

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326

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Figure 7: A construct for multi-objective task optimization for low size problem (~10actions).

Figure 8: Summary of the main concepts needed for Commanders Intent Automation.

CONCEPTS FOR AUTONOMOUS COMMAND AND CONTROL

327

multi-robot task allocation are included in the works

of Parker (Parker, 1998), LePape (Le Pape, 1990),

and others (Botelho and Alami, 1999). Mataric

(Gerkey and Mararic) provides a thorough review of

several Multi-Robots Task Allocation Frameworks.

4 AUTONOMOUS RESOURCE

ALLOCATION

Another key attribute of next-generation C2 is

convergence (Alberts, 2007). Convergence is the

ability for independent actors to achieve operational

coherence in a deterministic manner. The emergence

of platforms with multiple modalities (eg. sensing,

SAR, strike, etc….) in the manned and unmanned

arenas allows for additional flexibility in the

allocation of resources at the added cost of an

increasing complexity in the search space. The

resource allocation problem for AC2 must be able to

consider any platform for any task based upon the

platform’s capabilities. Optimizing across any

modality (COMMS, strike, sensing, etc…) is an NP-

hard problem. The AC2 resource allocation must

consider all modalities simultaneously in assigning

assets to objectives.

As stated above, the AC2 resource allocation

problem is a combinatorial optimization problem

that must consider the dynamic environment; a

nonlinear, multi-modal objective function; nonlinear

constraints; and binary decision variables.

Algorithms which address resource allocation

problems of this nature tend to be based on heuristic

methods. The extreme team methods (Scerri et al.,

2005) are effective in the presence of

communications limitations where global decision

support is not a viable option. Extreme teams have

the following characteristic:

• Near real-time assignments

• Platforms may perform more than one task

• Inter-task constraints may be present

Extreme teams are largely based on distributed

constraint optimization problems (DCOP) methods.

These types of algorithms can be applied to either

end of the C2 topology spectrum or can be used in a

complementary fashion for a localized topology

shown in Table 1.

Table 1: Recommended Resource Allocation Algorithms

for C2 Topologies.

The AC2 resource allocation performance must be

considered in light of scalability, satisficing behavior

(GPO, 1982), robustness, and generality. It is

important that the resource algorithm scale for large

numbers of assets and mission objectives. If the

solutions are near-optimal and generated in a

reasonable timeframe, the performance can be

considered to meet the satisficing criteria. In

addition, the algorithm must be stable, converge

rapidly, and insensitive to initial conditions. Finally,

the algorithm must be able to accommodate the

general nature of the objective described above.

The objective function under consideration by the

optimization engine should consider the following

components;

• Mission Effectiveness

• Mission Risk

• Mission Persistence

• Information Utility

The Mission Effectiveness considers all aspects

sensing communications and weapons required to

meet mission goals. The risk component considers

items such as METOC enemy defenses,

deconfliction and energy consumption. The

Persistence parameter may be required to minimize

global change in the solution set. For example, if a

global optimizer is used, then the results could be

dramatically varied at every solution step.

Persistence will reduce this variability. Finally, the

Information component is must be incorporated as a

metric to ensure that the right data gets to the right

place and platforms. For Autonomous C2 the

ramifications of automated subtask generation

should also be considered. Mission planners

generate many subtasks to satisfy the overall mission

objectives to achieve the desired effect(s). AC2 must

also be able generate sub-goals in a parsimonious

manner so that objectives can be accomplished and

new constraints generated by these sub-goals are

readily satisfied. The process of introducing sub-

goals and their associated constraints introduces a

complexity versus performance issue that should be

bounded within the AC2 construct. This notion is

analogous to Akaike’s Information Criterion (AIC)

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Figure 9: Notional depiction of an AC2 Turing test in a mixed manned/unmanned systems environment.

where the number of parameters and the log-

likelihood of the error in the function being fitted are

balanced.

5 CONCLUSIONS

Command and control in the ether represents a shift

away from traditional C2 constructs. AC2 represents

the ubiquitous nature of C2 in the distributed realm

where emergent behaviors are manifested by large

groups of platforms that are more complex than

those emulating ants and birds in colony and

flocking models, respectively. The potential

collaborative behaviors that would emerge under

different information management strategies should

be addressed as part of an integrated investigation

incorporating the C2 topology and resource

allocation ideas described here.

While C2 of UxVs will be a driver in developing

AC2, the evolutionary step of mixed manned and

unmanned missions can be considered as an AC2

Turing Test. This notion is exemplified in Figure. 8

where the manned platforms under direction of the

AC2 system do not know whether they are under

direction of manned or unmanned systems.

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