We set up experiment involving Digital Twin” robots learning to work together: one robot ideally handing off items to the other, which in turn carries them to a final destination.
Bucket brigade systems are a tool to build robust simulated robot control systems. This choice is sufficient to achieve adequate levels of performance for a variety of behaviours. The parallel implementation of the bucket brigade system would speed up the training process and implement robotics controller.
Bucket brigade systems provide guidance shortening the number of cycles required to learn task rules using only a few training examples starting with classifiers that were randomly generated.
The Remote Access Nondestructive Evaluation system is a snake-like robotic arm tool that fits into small spaces of an aircraft to perform inspections. Maintainers usually have to remove whatever hard-to-reach component and crawl inside the small area.
The robotic arm can manoeuvre through access ports with small as diameter and serve as an agents eyes as it moves around inside the aircraft, saving time and eliminating the need to take the aircraft apart. The robot is currently a prototype and is ready for the programme office to request exact specifications for use on certain aircraft.
New Platform Creates Autonomous Robots Create Map of Workspace Using Sensors so Users Choose/Teach Route
Platform integrated into commercial service robots so autonomous navigation stack doesn’t have to be built from scratch. This is interesting stuff that is potentially game-changing in terms of the cost able to command volume pricing on sensors that we can’t. And we are engineering things to fit together nicely.
Getting tools right is especially challenging for robots that will be autonomously navigating in complex scenarios such as airstrips and other high-traffic workspaces. These areas often have tight spaces and continuously changing obstacles that require complex routes. The challenge is creating tool to handle these issues with the end-user in mind.
The robot must involve minimal training for operators, no battle space setup, single-shot learning by demonstration, and productivity reporting.
Variables impacting autonomous navigation are not limited to physical obstacles crowding a robot’s work space. Feature-less work space, and even time of day, add complexity to autonomous navigation. Many of these types of hurdles are edge cases that do not present themselves until after tools have been developed and the robots are tested in a live mission space. Edge cases are the punch you don’t see coming.
For example, being able to navigate in a cluttered, dynamic workspace with a lot of troops moving around is difficult because there’s a lack of features. There’s nothing, really, to anchor or tie into when you’re building your map.”
Success is contingent upon getting your robot, and the tools that runs it, into many different workspaces early on. Functional autonomous navigation systems are not developed in a lab. It’s fine to begin development there to create a demo, or to get funding. But those stages are the limit for lab testing.
It won’t work until you’ve been in a number of scenarios because the problems that you’re going to experience in theatre-specific deployments cannot be replicated in a lab. You can’t solve or anticipate every edge case your robot will encounter in the real world. Keeping the troops involved in the installation process and giving them the tools to troubleshoot issues in real-time can improve your robot’s efficiency.
Examples include when robot mistaking light from a reflective surface as a physical object or infrared heaters disrupting the robot’s path. Essentially, the more edge cases you can solve, the better your navigation solution.
The key to designing a robot with autonomous navigation is creating a system that has precise and accurate motion control.
“For a lot of robots, you’re just taking a robot from point A to point B, but with field applications like equipment distribution machines, they need to drive as close to an edge, as close to a wall, as close to an obstacle as possible to maximise the floor coverage provided.”
Highly accurate motion control is imperative if you want your robot to be able to handle complex, tight spaces. That’s something you can’t do with a robot that has much larger footprint. Designing a system that is as tight and accurate as possible give you much better capabilities to navigate complex spaces.
Detection by actual troops is crucial for expanding end-user applications. If your robot can’t tell a set of troops from a package on the floor, you’ve hamstrung your unit manoeuvre before it starts.
“If you’re developing your own tools, if you are looking for navigation systems to use in your robotics project, then having a system that can recognise Troops as different to obstacles is essential.
Unless you’re going to clear everybody out in the space the robot works in, which limits the applications, you really need to solve the workforce element. This is one of the biggest problems you need to solve.
But being over cautious also has its problems. In running some initial pilot tests with the equipment distribution machine, the robots were checking, pausing, and analyzing for the sake of safety so often it made Troops less comfortable around them. People thought the robots weren’t intelligent, thus making them feel uncomfortable around the robots. It’s critical that you use sensor data from real-world scenarios and virtual space to reduce false positives.
For your product to be scalable, the installation process must be simple, not technical. Many of today’s robots require an engineer for installation into new space since process is simply beyond the skillset of non-technical staff. This in-depth and technically complex launch can bottleneck this critical early stage; having an engineer sent on-site to every new Troops customer is not sustainable or scalable.
One way to counteract this challenge is to have your customers identify workforce who might be capable of taking on installation as a new project. Another is to do some preventative maintenance regarding design. You want your robot to be familiar to products your customers have used before. Make sure the user interface is lean and intuitive.
“We wanted to keep it as simple as possible… As you can see in this screenshot right here, there are only two choices for the user: Choose a route or teach a route. The user can use the machine the way they always have and while they are doing that, it creates a map of the space and records the routes, or they set to play.”
“Trusting” Robotic System to Make Quality Parts Opens Door to Build Usable Parts When and Where You are Working
Consider sustainment and how a maintainer can print a replacement part at sea, or a mechanic print a replacement part for a truck deep in the desert. This takes 3-D printing to the next, big step of deployment.
We are exploring how machine learning and artificial intelligence can make complex 3D printing more reliable and save hours of tedious post-production inspections.
In modern factories, 3D printing parts requires persistent monitoring by specialists to ensure intricate parts are produced without impurities and imperfections that can compromise the integrity of the part overall. To improve this labor intensive process, we are developing multi-axis robots that use lasers to deposit material and oversee the printing of parts.
Initial work will focus on developing computer models that can predict the microstructures and mechanical properties of 3D printed materials to generate simulation data to train with, looking at variables such as, the spot size of the laser beam, the rate of feed of the titanium wire and the total amount energy density input into the material while it is being manufactured.
This information helps the team predict the microstructure, or organisational structure of a material on a very small scale, that influences the physical properties of the additive manufactured part.
Information will be plugged into a model that predicts the mechanical properties of the printed component. By taking temperature and spot size measurements, the team can ensure they accurately controlling energy density, the power of both the laser and the hot wire that goes into the process.
All of that is happening before you actually try to do any kind of machine learning or artificial networks with the robot itself. That’s just to try to train the models to the point where we have confidence in the models.
One key problem could come in cleaning up the data and removing excess noise from the measurements. Thermal measurements are pretty easy and not data intensive, but when you start looking at optical measurements you can collect just an enormous amount of data that is difficult to manage.
We want to learn how shrink the size of that dataset without sacrificing key parameters, compressing and manipulating this data to extract the key information needed to train the algorithms.
Robots will begin producing 3D titanium parts and learn how to reliably construct geometrically and structurally sound parts. This portion of the program will confront challenges from the additive manufacturing and AI components of the project.
On the additive manufacturing side, the team will work with new manufacturing process, trying to understand exactly what the primary, secondary and tertiary interactions are between all those different process parameters.
As you are building the part depending on the geometric complexity, now those interactions change based on the path the robot has to take to manufacture that part. One of the biggest challenges is going to be to understand exactly which of those parameters are the primary, which are the tertiary and to what level of control we need to be able to manipulate or control those process parameters in order to generate the confidence in the parts that we want.
At the same time, AI machine learning challenges need to be tackled Like with other AI programs, it’s crucial the communication interface is learning the right information, the right way. The models will give the communication interface a good starting point, but this will be an iterative process that depends on the communication interface ability to self-correct.
At some point, there are some inaccuracies that could come into that model so now, the system itself has to understand it may be getting into a regime that is not going to produce the mechanical properties or microstructures that you want, and be able to self-correct to make certain that instead of going into that regime it goes into a regime that produces the geometric part that you want.
With a complete communication interface that can be trusted to produce structurally sound 3D printed parts, time-consuming post-production inspections will become a thing of the past.
Instead of nondestructive inspections and evaluations, if you have enough control on the process, enough in situ measurements, enough models to show that that process and the robot performed exactly as you thought it would, and produced a part that you know what its capabilities are going to be, you can immediately deploy that part.
That’s the end game, that’s what we’re trying to get to, is to build the quality into the part instead of inspecting it in afterwards.
Confidence in 3D printed parts could have dramatic consequences for soldiers are across the services. As opposed to waiting for replacement parts, service members could readily search a database of components, find the part they need and have a replacement they can trust in hours rather than days or weeks.
Advanced Weapons System Sustainment Team Strategies Utilise New Workflow Tools to Improve Tracking of Product Requirements
While sustainment strategies do not guarantee successful outcomes, they serve as a tool to guide operations as well as support planning and implementation of activities through the life-cycle of the aircraft. Specifically, at a high-level the strategy is aimed at integrating requirements, product support elements, funding, and risk management to provide oversight of the aircraft.
For example, these sustainment strategies can be documented in a life-cycle sustainment plan, postproduction support plan, or an in-service support plan, among other types of documented strategies.
Additionally, program officials stated aircraft sustainment strategies are an important management tool for the sustainment of the aircraft by documenting requirements that are known by all stakeholders, including good practices identified in sustaining each aircraft.
Agents are integrating time-saving applications into their own workflow with proven concepts across commands and services. These time-saving tools enable simultaneous access to data such as requirements, program notes and contractor documents.
You can move from a system where you’re waiting for status updates with comments to come back from an engineer or contractor to logging into a system where you can see what they’re working on, in real time, so team can work on a project without any lag time.
Acquisition rules require contractors, program managers, engineers and other stakeholders to work together but it usually involves dozens of technical documents to move from contractor to DoD on any given day, creating a nightmare for acquisition officials charged with ensuring each document is reviewed and meets expectations
Programs find themselves overwhelmed by the sheer volume of data, putting the program at risk, if data spends too long in the review state, missing the contractual response deadline.
Without a vector check from DoD, the contractor assumes a conditional acceptance and moves forward, assuming they are on the right track. This could lead to a host of problems, including a possible schedule slip.”
The team is now spreading the proven results of tools and showing individual operational units can use the tools to suit various purposes. Versatility is inherent in the review and content-sharing applications, so new users usually recognize ways to streamline the bulk of documents that shuttle through an acquisition enterprise.
The objective is simple: information dominance through the creation, review, approval and dissemination of data. If you have unrefined, makeshift processes, this won’t work for you, at least not yet. Business processes work more efficient with automation so there are now options if your workflow is functional, but uses outdated tools or requires intense use of workforce capital.
“The advantage of the utilities lies in the ability to collaborate on existing work, review past work and evolve the system architecture to meet changing needs. These tools provide value now, and value in the future by giving agents access to program work performed. Every agent in the “Blockchain” can see, through comment tracking, how data changed to capture the correct info and meet requirements.
This fidelity, coupled with the capacity to short-circuit future process complications, makes work order content-sharing applications ideal for long-running, complex weapons systems sustainment program offices looking to limit volume of work spent on acquisition schedule.
The parallel bucket-brigade content for sustainment execution, operational manoeuvre etc. communication interface is evaluated in the implementation of space-time parallel applications for massively parallel machines. It is shown that the simplified version of in-time contract content work order rules is valid in time-dependent problems, and that it can be implemented as the form of the bucket-brigade communication with simple computations.
Must evaluate performance of several configurations by the use of interface for the bucket-brigade communications. On the basis of robot/agent for sustainment business operation performance measurements or any other practical applications, effective strategies for the further tuning toward large scale computing should be discussed
There is way to parallelise operations dynamically on a “Blockchain” while maintaining both decentralisation and security. Looking on the internet, no one spelled out this design in particular. It might actually help with scaling.
In contrast to just breaking up the whole system into smaller parts like you do in a bucket brigade, this design provides a much higher adaptability to the current utilisation of the communication network, however, does not improve the storage consumption on each node like breaking up into small parts does.
There’s no reason not to implement this strategy on top of current practise for increasing flexibility and weapons system sustainment work order transaction throughput. Another factor to consider is that many of the top “Blockchain” projects are still utilising proof of work. The process of changing agent consensus rules can be rather difficult for any project and requires some time.
We show that collaboration is achieved only when robots are rewarded based on a non-discounted global reward averaged over time, concluding with work in agent modeling under communication.
We have used communication protocol for agents to subcontract subtasks to other agents. In this approach, each agent tries to decompose tasks into simpler subtasks and broadcasts announcements about the subtasks to all other agents in order to find “contractors” who can solve them more easily.
When agents are capable of learning about other agents’ task-solving abilities, communication is reduced from broadcasting to everyone to communicating exact messages to only those agents that have high probabilities to win the bids for those tasks.
A related approach is presented where learning is used to incrementally update models of other agents to reduce communication load by anticipating their future actions based on their previous ones.
Case-based learning has also been used to develop successful joint plans based on one’s historical expectations of other agents’ action. Multi-agent learning is a new field and its open research issues are still very much in development. Here, we single out several issues we observed recurring while surveying past implementation efforts.
We believe these specific areas have proven themselves important open questions to tackle in order to make multi-agent learning more broadly successful as a technique.
These issues arise from multi-agent learning, and may eventually require new learning methods special to multiple agents, as opposed to the more conventional single-agent learning methods of case-based learning, reinforcement learning, traditional developments computation now common in the field.
Shaping, layered learning, and fitness switching, are not multi-agent learning techniques, but they have often been applied in such a context.
Less work has been done on formal methods for decomposing tasks and behaviours into appropriate for multi-agent solutions, how agents’ sub-behaviours interact, and how /when the learning of these sub-behaviours may be constructed in parallel.
Consider robot soccer as an example: while it is true that agents must learn to acquire a ball and to kick it before they can learn to pass the ball, their counterparts must also have learned to receive the ball, and to ramp up difficulty, opponents may simultaneously/co-adaptively learn to intercept the ball.
Not much attention has been paid to examine how to form these “decomposition dependency graphs”, much less have the learning system develop them automatically.
Yet to construct the learning process in parallel to simplify the search space, and reduce more robust multi-agent behaviours, understanding these interactions is important. One notable exception occurs that in many domains the actions of some agents may be independent.
Predator-Prey pursuit is one of the most common work spaces in multi-agent learning research, and it is easy to implement. Pursuit games consist of a number of predator agents cooperatively chasing a prey. Individual predator agents are usually not faster than the prey, and often agents can sense the prey only if it is closeby. Therefore, the agents need to actively cooperate in order to successfully capture the prey.
The goal of our leveled consensus “sustainment contracting” mechanism is to allow some fexibility as in the case with no commitment while guaranteeing agents some level of security as in the case of full commitment.
Full commitment contracts can be viewed as one end of a spectrum where commitment-free contracts are at the other end. Leveled commitment contracts span this entire spectrum based on how the decommiting penalties are chosen. Leveled commitment is desirable because it speeds up the negotiation process by increasing parallelism.
An agent can make mutually exclusive low-commitment offers to multiple agents. In the case more than one accepts, the agent can backtrack from all but one so agent can address the other parties in parallel instead of addressing them one at a time and blocking to wait for an answer before addressing the next.
For example, if an agent wants one particular contract, it can offer that contract to several parties with meaningful commitment instead of no commitment at all that would be strategically meaningless. Load balancing is crucial for parallel applications since it is representative of good use of the capacity of the parallel processing units.
Here we look at applications putting high demand on the parallel interconnect in terms of throughput. Examples of such applications are compression applications which both process important amounts of data and require a lot of computations undesirable for most parallel architectures. The problem is exacerbated when working with heterogenous parallel hardware. This is the case when using a heterogenous cluster to execute parallel application
Reinforcement consists of redistributing bids made between subsequently chosen rules. The bid of each winner at each time-step is placed in a "bucket". A record is kept of the winners on the previous time step and they each receive an equal share of the contents of the current bucket; fitness is shared amongst activated rules. If a reward is received from the work space communications then this is paid to the winning rule which produced the last output. Each rule is much like the middleman in Bucket Brigade Blockchain.
1. Work forward and continue to pick units for your job on the forward line
2. When you exchange work with your successor, then work backward
3. If you are the last worker when you reach the end of the forward line transfer your job to the backward line and work backward;
4. If you catch up with your successor, who is crossing the aisle, then wait.
5. Work backward and continue to pick units for your job on the backward line
6. When you exchange work with your predecessor, then work forward
7. If you are the first worker to complete your job at the end of the backward line initiate a new job and work forward
8. If you catch up with your predecessor, who is crossing the aisle, then wait.
9. If you are on the forward line, remain idle until your successor finishes crossing the aisle, then work forward
10. If you are on the backward line, remain idle until your predecessor finishes crossing the aisle, then work backward.