Questions about autonomous warfare address artificial intelligence understanding the context of its actions, its predictability, ability to transfer lessons from one task to another and durability,
Autonomy is generally defined as a machine having the ability to execute tasks with limited to no human intervention. Advances in autonomy are driven by converging technologies such as AI, robotics, dig data, and advanced motion sensors. Autonomous systems can involve a built-in human control mechanism -- human-in-the-loop, a human override mechanism-- human-on-the-loop, or minimal-to-no human involvement-- human-out-of-the-loop.
Autonomous systems can be conceptually divided into two categories: processes and assets. Autonomous processes include those capabilities driven by machine learning, big data and AI to automate procedures and functions.
Major advances in autonomous processes could support mission planning, training , decision-making, administrative roles, and business functions. Autonomous assets include the physical equipment and resources the Services can use to carry out missions. These assets are divided primarily into three categories: unmanned aerial vehicles UAV, unmanned underwater vehicles UUV and unmanned surface vehicles USV..
USVs receive less attention than their aerial counterparts, but hold enormous potential. USVs support cross-domain integration and increase the capabilities of other unmanned systems with their large payloads, power reserves, and endurance and overcome anti-access/area-denial environments by projecting information operations, military deception campaigns and electronic warfare capabilities, Current projects aim to have swarms of autonomous vessels conducting both surveillance and security operations.
Autonomous assets can act as a major force multiplier. UAVs, USVs, and UUVs can increase the strength of the force and material readiness while the Navy’s requirements for deployments, readiness, and forward presence remain high.
Additionally, autonomous assets strategically support principles such as distribution and maneuver by leveraging “additional weapons and sensors over large areas” and optimizing the “strategic depth of the force.“
Both airborne and surface-borne drones can support intelligence collection and targeting requirements for multi-domain battlespaces and over-the-horizon amphib ops with adequate fire support for landing forces, and autonomous drones could overcome this challenge by acting as mobile mini-mortars with increased on-station times.
The greatest benefit of implementing autonomy into the Navy is the speed of decision making for command and control. Autonomy and man-machine teaming can allow leaders to make better decisions faster. Military leaders must “be prepared to make decisions at the speed of relevance. When the speed of relevance is the speed of electrons, the Navy will depend on autonomy to remain a relevant fighting force.
The military already uses autonomous systems for offensive and defensive missions. Various levels of autonomy support mobility, targeting, intelligence, and interoperability, Autonomy empowers homing missiles, navigation, and autopilot capabilities. Basic targeting systems use automated target recognition to identify objects and support non-kinetic targeting for ISR collections.
Counter-artillery batteries and Phalanx close-in-weapons-systems can engage automatically upon detecting a threat. Recurring and rules-based tasks such as scheduling replenishments at sea, naval weapon-target assignment plans, dynamic frequency allocations, and planning daily aircraft routing are candidates for integration with AI in the near future.
Navy recently created its first underwater drone squadron.. Future uses of USVs are under-explored but hold substantial promise. USVs have significant advantages over UAVs and UUVs with regard to endurance and payload capacity for prolonged operations.
Previous exercises highlighted the ability of USVs to relay instructions from shore to underwater assets, in this case by ordering the launch of a UAV which a UUV was carrying
Most USVs are directed toward missions such as observation and collection, physical environment mapping, countermeasures, countering small boats, and testing to involve automated payloads and autonomous coordination with multiple ships.
The Navy and Marine Corps are uniquely suited to benefit from autonomous systems. Attributes that welcome autonomy: empowering lower-skilled workers to perform higher-skilled work, replication for large-scale operations, faster-than-human reaction speed, superhuman precision, extended patience, and operations away from reliable communications. Some strides are being made to foster autonomy, but more can be done.
Most AI systems require some level of guidance from humans. Sailors and Marines will require instruction and training on these technical systems, just as officers will require education on how to integrate them into operations and planning. Educating front-line leaders on the capabilities of autonomous systems should be a priority.
Clearly define the goals and tasks for which an autonomous system is being considered. What will it take for autonomous vehicles to become an integral part of our mobility landscape? They must be applied in the right business context to solve the right problems. In other words, a critical success factor will be a clear value proposition for the company and its customers, and without a clear and viable value proposition, autonomy projects will lack support and are at risk of failure.
Identify/define what constitutes the successful completion of that task. Any application of autonomy must also provide some benefit to the provider of products and services. Consider that customer-facing autonomous solutions must integrate, both operationally and in terms of information technology, with supply chain systems further upstream. To accommodate autonomous deliveries, companies will have to make process and infrastructure changes. The question is whether that can be done in a way that reduces costs and/or improves profitability.
Identify all possible off-nominal conditions, contingencies, and challenging edge case problems that occur at extreme operating parameters that the autonomous system must address. Bad weather, for instance, could push a drone off-course, causing it to land outside of its targeted landing zone. Malfunctions—whether mechanical or caused by glitches—or unexpected conditions such as temporary obstructions could cause a collision or crash, causing damage to the payload .
Test the autonomous system in a simulation environment with many nominal and off-nominal conditions. To ensure that autonomous vehicles and their supporting technologies perform safely and correctly, it will be critical to test them in both normal, expected conditions and abnormal, unexpected situations. The safest way to test is to use a simulation program similar to a flight simulator to re-create real-world scenarios and use that information to make improvements. In other words, virtual testing of autonomous vehicles should augment and "stress-test" the physical testing.
Prioritize projects. Create a methodology for prioritizing which sort of autonomous vehicle projects to focus on first. One way to structure it might be a "crawl-walk-run" approach, starting by adopting autonomy for simple tasks where the risk is lowest and there are fewer variables to control for example, a single vehicle traveling a repetitive route in a sparsely populated area, and then moving to more complex tasks with more variables multiple vehicles traveling variable routes in congested areas
Another way to prioritize autonomy projects is to think about what problem a project would solve and rank it in order of its importance to the company and/or its customers. For example, a company's most important reasons for pursuing an autonomous vehicle project might be to improve safety and access to critical information. Improvements in speed, productivity, and cost, while important, could be lower priorities.
Ensure that you have a plan for managing data collection, storage, and analysis. Every autonomous vehicle constantly produces enormous amounts of data. This creates three issues.
The first is how to collect all that information and use it, without latency, to make real-time decisions and take real-time or near real-time actions. The second is how to determine which data should be stored for later use. Out of that huge stream of data, which signals are important to keep, and which can be discarded? The third is how to distribute the storage of so much data. There will be too much data to keep in one location—and from a risk management perspective, it would be unwise to do so anyway. But when forensic research becomes necessary—perhaps to recreate an accident and identify its causes—it will be critical to know where all the data is sitting and how to access it.
We are just beginning to scratch the surface of how autonomous vehicles will impact logistics and supply chain operations. For example, in the future, autonomous vehicles will play an important role in data gathering, processing, and sharing.
Processing advances in sensors, machine learning, and artificial intelligence are already pushing computing farther out in a supply chain. Autonomous vehicles could not only function as a mobility system, but they also could serve as both a local storage device for the relevant sensor data and as a "mesh network" for processing it—an example of "edge computing."
Newly available data-rich information infrastructure will help enterprises to enable fluid alignment/realignment of resources and workflows through a major shift in their network and supply chain architectures.
1. Integrating with large suppliers
The ability to integrate systems with that of large suppliers is one of the major advantages of automating a supply chain, as it enables a strong foundation of which permits more visibility between partners. Improving order-to-cycle is critical since the speed of delivery is important for customers today.
2. Data Availability
Having accurate, timely data available won’t only help you to pinpoint improvement opportunities for processes and allow you to collaborate better with your partners, but also to ensure that your customers receive their orders in the shortest time possible.
3. Real-time product tracking
It’s important to provide your customers with real-time visibility over their orders, so they can always know the status of their deliveries and when they can expect them.
4. Focus on delivery plan monitor
Access to data on delivery in real-time can help you to monitor and manage your routes better, communicate with your agents, and if necessary, adjust your delivery plan.
5. Improve operational efficiency with data sharing and partnerships
Data can help you to optimize customer service – knowing the exact status of your customers’ shipments by having all data in a single place will make serving them far easier. As you continue enhancing the quality of your partnerships and providing excellent customer experience, you will also increase customer retention.
6. Predictive Data
Having data on everything that can affect your operations will help you to better predict future events and act before those events would happen. By putting such data points into predictive models, you are able to improve the accuracy of your forecasts and remove some of the uncertainty involved in the process.
7. Boost productivity.
By making use of AI in supply chain management, it is possible to analyze its performance and come up with new factors which impacts the same area. In order to find the factors and issues which affects the performance of the supply chain, AI combines the capabilities of different technologies like reinforcement learning, unsupervised learning and supervised learning.
8. Demand forecasting by analyzing large volumes of data.
Measure and track all the factors which can work towards offering accuracy in demand forecasting. Based on the weather, real-time sales and other factors, it provides continuous forecasts in a loop. Such kind of information can help with automated sorting, improving warehouse management, self-management of inventory systems and forklifts that are self-driving.
9. Improving the selection of the supplier and its effectiveness.
AI can analyze the data related to the supplier like audits, in-full delivery performance, evaluations and based on that deliver information which can be used to make future decisions. This kind of step helps the company make better decisions as a supplier and work towards improving customer service
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10. Improves factory scheduling and production planning.
Now companies can work on enhancing factory scheduling and production planning. They can work on to analyze the different issues and then optimize them. By leveraging power to balance constraints this can work well for build-to-order situations automatically.
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