Military will rely much more on distributed drone swarms and autonomous or semi-autonomous vehicles. These may be called on to do actual fighting on the front lines. But as military leaders warn, their preference is to keep some deal of control in the loop, especially when it comes to weapons.
That means that tomorrow’s drone swarms and self-driving truck convoys will have to be able to communicate at high rates of speed and data with one another and potentially with operators afar; millimeter waves, hardened against adversaries, will be critical there as well.
Military will need terminals that work with more than GPS, so plans are in the works to develop a prototype receiver that can use GPS and other GNSS signals, which could increase the resilience of the military’s position, navigation and timing equipment.
The primary source of the military’s PNT data is the Global Positioning satellite system. But with adversaries developing GPS jamming technology and anti-satellite weapons that could potentially knock out one or more of those satellites, leaders want a receiver capable of utilizing other global navigation satellite systems.
We need to develop a prototype receiver capable of utilizing multiple global navigation satellite systems in addition to GPS so if the GPS signal is degraded or denied, war fighters could switch to one of those other systems to get the PNT data they need
If commercial 5G millimeter-wave gear can be hardened against jamming, Army thinks it might gain a real battlefield edge.
You may not have heard of millimeter waves, but they will play a critical role in tomorrow’s super fast and capable 5G wireless internet environment. Because the 30-to-300-gigahertz band has only recently been harnessed for use by new antennae and other technology, bandwidth is plentiful. Because millimeter waves are inherently directional, they make signals hard to intercept. All this has drawn the attention of Army, which may put them to use for swarming drones, rapid maneuvering, and a battlefield network
That’s where the service’s mm-wave project comes in.
Phase one will focus on defining the architecture for millimeter-wave networks that offer multi-gigabit-per-second links across hundreds of meters, “the range of maybe a convoy of vehicles.
Other applications might be data centers and peer-to-peer networks.
“Could be maybe on-soldier; could be back on a base.”
But the stakes are a lot higher than a single soldier. In a fast-moving confrontation between great powers, millimeter waves might just help determine victory or defeat. Military planners expect that future warfare will be incredibly fast and deadly. Enemy forces will require a lot less time to target their adversaries’ critical elements. That means the ability to move quickly, and especially command and control nodes, will be essential.
A typical modern command post, even one set up relatively quickly, still requires connecting cables, erecting towers, installing servers, etc. Such posts take “a platoon of roughly 30 soldiers a day to install or dismantle. This is far too slow for the command post of the future that requires agile deployment and even continuously mobile operation.”
“A high capacity, high bandwidth, wireless [Local Area Network] will eliminate the cables and connections and would allow for quick installation/dismantling, and for headquarters elements to be dispersed, thus decreasing their visual footprint and vulnerability to attack.
Cutting the time schedule necessary to stand up or dismantle command hubs will allow the military to make such nodes more mobile, allowing for faster-coordinated attacks and forcing the adversary to make faster, and presumably more predictable, decisions.
Multi agent systems approach is a branch in artificial intelligence providing a new way for solving distributed, dynamic problems. Agent technology has been widely accepted and developed in implementation of scheduling and distributed control system. Multi agent-based platforms are usually equipped with distributed intelligent functions, and are becoming a key technology in new manufacturing systems built in a distributed manner.
Agent-based systems have advantages of less sensitive to fluctuations in demand or available vehicles than more traditional transportation planning factors like local control and serial scheduling, providing a lot of flexibility by solving local problems .
Manufacturing control systems using Distributed Artificial Intelligence techniques have so far not achieved practical implementation in real world factory cases due to lack of standards so there is a need for conducting more inquiry in this field. Agent based systems have provided an excellent opportunity for modeling and solving dynamic scheduling problems.
Here we present a multi agent based scheduling decision-making system for automated service process flexible command post assembly line considering customer demand. Dynamic behaviours in service company such as diversification of production and reconfiguration are taken into consideration.
Best product for customers has a tremendous advantage. One of the most effective means to determine the features that customers like is to turn out as many different product variations as quickly as possible, sampling customer response and adjusting new offerings accordingly.
The multi agent scheduling system is developed based on general-purpose design agent systems not tied to any specific model of agency in platform. The multi agent based scheduling system is completely designed mainly for the work cell with time-based constraints, although it is applicable of keeping the work cell free from time-based constraints.
Rescheduling decision-making problem is an important issue in modern manufacturing system with the feature of combinational computation complexity. Must introduce a multi-agent based approach of detailed process. used for the design of a simultaneous rescheduling decision making for flexible flow line manufacturing system working under dynamic customer demand.
An automated fabrication process is composed of several components with deferent shape and size requires many operations with entrant flow consists of some workstations and each contains one or even more machines.
Flow line manufacturing system schedule scheduling problem with time based customer demand constraints limitations depends on both logical and time-based correctness. The logical correctness refers to the satisfactions of resource capacity constraints and precedence limitation of operations. The time-based correctness, namely timeliness, refers to the satisfactions of the time-based constraints such as interoperation time-based constraints and due dates.
The scheduling techniques of real time systems are divided into static offline scheduling and dynamic scheduling. Static scheduling techniques are applicable to real time systems in which jobs are periodic. They perform offline feasibility or schedule ability assessments.
Dynamic scheduling techniques are advantageous in systems with uncertainty such as taking into consideration periodic jobs and machine failures. Dynamic scheduling techniques are divided into planning-based and best effort approaches. In planning-based approaches, schedule ability is checked at run time when a job arrives, and the job is accepted only if timeliness is guaranteed.
On the other hand, best effort approaches do not check schedule ability at all. So planning-based approaches are adequate for the real-time systems with hard deadlines, whereas the best effort approaches are adequate for those with soft deadlines.
Proposed architecture is designed for handling machine breakdown and optimising machine utility focused on reconfiguration of control system but scheduling and customer demand is not considered.
Agent-based models consist of rule-based agents with many interactions. The agents within the systems with interacts, can create real-world-like complexity. Based on that observation, we provide context schedule the machine and material handling system by means of emphasising flexibility in a flow line manufacturing system through Multi-Agent System approach.
Agent-based modeling and design differs from the conventional systems design. System is not tied to any specific model of agency platform defining a detailed process for specifying, designing, implementing and testing/debugging agent-oriented systems. In addition to detailed processes and many practical tips, it defines a range of artifacts that are produced along the way.
The system specification phase focuses on identifying the goals and basic functionalities of the system, along with input/output actions. The architectural design phase uses the outputs from the previous phase to determine agent types the system will contain and how they will interact. The detailed design phase looks at the internals of each agent and how it will accomplish its tasks within the overall system.
Distributed problem solving artificial intelligence facilitate agent cooperation work where distribution of capability, information, and expertise make no single agent solution to tasks possible.
Many techniques of distributed problem solving have been used e.g., on distributed constraint satisfaction, multi-agent planning or agent-based simulation, but there are not many specifically aiming at developing formulas for solving distributed configuration problems.
Must create infrastructure to model and solve a variety of problems from the area of Multi-agent Systems and distributed Artificial Intelligence including distributed resource allocation, scheduling or verification maintenance. Modeling and solving distributed configuration problems, in which several agents jointly and in a loosely coupled, non-parallel manner cooperate in the problem solving process.
Distributed event simulation is particularly suitable for modeling systems with inherent uncoupled parallel characteristics, such as agent based systems. However the efficient simulation of multi-agent systems presents particular challenges which are not addressed by standard parallel discrete event simulation models and techniques.
Several motivations for application of distributed planning include using distributed resources concurrently to speed-up problem solving by agents thanks to determine what degree problem is characterised by parallel mechanisms.
Problem is to find sequence of moves with capacity to achieve the goal state. Another motivation for distributed problem solving and planning requires distributed agent expertise or other problem-solving capabilities.
Site Visit Executive is able to maximise readiness and overcome equipment shortfalls by manipulating the timing and sequencing of tasks/subtasks involved in operational scenarios. Can involve reordering certain tasks over others or staggering tasks rather than attempting to execute them concurrently.
High-level programming techniques have been created for next generation sequence alignment tools for both productivity and well-defined performance. Sequences are lists of tasks that changes according to some pattern. Pattern-based programming framework provides agents with high-level parallel patterns.
Must differentiate between sequence numbers and unique sortable identifiers by a specific criteria typically generation time. True sequence numbers imply knowledge of what all other agents have done, so a shared state is required. It is virtually impossible to do this in a distributed, high-scale manner.
There is the problem of parallel alignment so you must review how popular alignment tools function in single high-level parallel strategy. By using a high-level approach, you don't need to be concerned with complex aspects of parallel programming, such as linking task scheduling, so you can achieve seamless performance tuning.
Approach introduced into artificial intelligence typically considered distributed systems where each individual in such systems possesses potential for action based on events. By having separate modules for coordination and local scheduling, we can take advantage of advances in real-time scheduling to produce cooperative distributed problem solving systems responding to real-time deadlines.
Critical decision point in simulation training action commits organisational resources to a specific product, sustainment profile, choice of supplier/design contract terms, schedule & sequence of events leading to mission field deployment in theatre.
1. Manufacturing system schedule is static scheduling
2. Machine stations have no autonomous scheduling unit for operations
3. System lacks real time scheduling
4. Customer demand is not flexible
5. Difficult to schedule in dynamic environment
6. Justification of Developing multi agent based dynamic decision system
7. Decision making system schedule when customer demand accrues
8. Development of multi agent decision system scheduling in machine fail disturbance.
9. System makes autonomous station level
10. Multi-agent structure creates real time communication in system