Rolls Royce has revealed its plans for making autonomous naval vessels which will be capable of surveilling the waters for 100 days at a go with a range of 3,500 miles at maximum speeds of more than 25 knots. "Rolls-Royce is seeing interest from major navies in autonomous, rather than remote controlled, ships. Such ships offer a way to deliver increased operational capability, reduce the risk to crew and cut both operating and build costs…Over the next 10 years or so, Rolls-Royce expects to see the introduction of medium sized unmanned platforms, particularly in leading navies, as the concept of mixed manned and unmanned fleets develops. With our experience and capabilities we expect to lead the field," Benjamin Thorp, Rolls-Royce, General Manager Naval Electrics, Automation and Control, stated in the press release. The self-navigating ship will be capable of navigating entirely without a human crew and will not be affected by human issues such as tedium.

Engineering giant Rolls-Royce plans to make an autonomous navy ship, powered by artificial intelligence, sophisticated sensors and advanced propulsion, for sale to militaries throughout the world. The British company, known for its aircraft engines and luxury automotive heritage, revealed a concept version of the naval vessel in multiple photos released Tuesday. Amid increasing concern among some technologists about the prospect of self-aware artificial intelligence systems becoming a threat to humanity, Rolls-Royce said it was already conducting "significant analysis of potential cyber risks" to "ensure end-to-end security." With range of 3,500 nautical miles, the 60-meter-long Rolls-Royce vessel would be able to operate on its own without human intervention for more than 100 days. Its missions could include patrol and surveillance, fleet watch or sea mine detection.

KARLA MURTHY: Hyundai means "modernity," and it's is a big name in the South Korean economic landscape – and not only for cars. Headquartered in the industrialized port city of Ulsan, Hyundai Heavy Industries, or HHI, is the world's largest shipbuilder. It produces engines and construction equipment. This is where they test robots used to assemble cars. KARLA MURTHY: That shake when the robot stops slowed down productivity and accuracy.

I don't know why Siemens thinks "SiSpis" is a less-creepy name than "robot spider." Shipbuilding is a community effort. For Siemens, that community in the future won't just be the engineers, designers, and workmen on a project: it will also include an army of small robot spiders, 3D printing and weaving together plastic to build that hull. Think of it like a normal shipbuilding facility, only with hundreds of tiny scurrying parts, all working together. To accomplish this, the robots use onboard cameras as well as a laser scanner to interpret their immediate environment.

One of the most important aspects of current expert systems technology is the ability to make causal inferences about the impact of new evidence. When the domain knowledge and problem knowledge are uncertain and incomplete Bayesian reasoning has proven to be an effective way of forming such inferences [3,4,8]. While several reasoning schemes have been developed based on Bayes Rule, there has been very little work examining the comparative effectiveness of these schemes in a real application. This paper describes a knowledge based system for ship classification [1], originally developed using the PROSPECTOR updating method [2], that has been reimplemented to use the inference procedure developed by Pearl and Kim [4,5]. We discuss our reasons for making this change, the implementation of the new inference engine, and the comparative performance of the two versions of the system.

The problems associated with scaling involve active and challenging research topics in the area of artificial intelligence. The purpose is to solve real world problems by means of AI technologies, in cases where the complexity of representation of the real world problem is potentially combinatorial. In this paper, we present a novel approach to cope with the scaling issues in Bayesian belief networks for ship classification. The proposed approach divides the conceptual model of a complex ship classification problem into a set of small modules that work together to solve the classification problem while preserving the functionality of the original model. The possible ways of explaining sensor returns (e.g., the evidence) for some features, such as portholes along the length of a ship, are sometimes combinatorial. Thus, using an exhaustive approach, which entails the enumeration of all possible explanations, is impractical for larger problems. We present a network structure (referred to as Sequential Decomposition, SD) in which each observation is associated with a set of legitimate outcomes which are consistent with the explanation of each observed piece of evidence. The results show that the SD approach allows one to represent feature-observation relations in a manageable way and achieve the same explanatory power as an exhaustive approach.

We present several new contributions in sampling-based coverage path planning, the task of finding feasible paths that give 100% sensor coverage of complex structures in obstaclefilled and visually occluded environments. First, we establish a framework for analyzing the probabilistic completeness of a sampling-based coverage algorithm, and derive results on the completeness and convergence of existing algorithms. Second, we introduce a new algorithm for the iterative improvement of a feasible coverage path; this relies on a samplingbased subroutine that makes asymptotically optimal local improvements to a feasible coverage path based on a strong generalization of the RRT* algorithm. We then apply the algorithm to the real-world task of autonomous in-water ship hull inspection. We use our improvement algorithm in conjunction with redundant roadmap coverage planning algorithm to produce paths that cover complex 3D environments with unprecedented efficiency.

We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80% when compared to passive methods.

Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems.

Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator. In addition, we developed the paneled-block assembly shop scheduler and the long-range production planner. For this large-scale project, we devised a phased development strategy consisting of three phases: (1) vision revelation, (2) data-dependent realization, and (3) prospective enhancement. The DAS systems were successfully launched in January 1994 and are actively being used as indispensable systems in the shipyard, resulting in significant improvement in productivity and visible and positive effects in many areas.