Optimal navigation of ground vehicles in an off-road setting is a challenging task. One must accurately model the properties of the terrain and reconcile it with vehicle capabilities, while simultaneously addressing mission requirements. An important part of navigation is path planning, the selection of the route a vehicle takes between the start and end points. It is often seen that, given the starting and end points for a vehicle, the optimal path that the vehicle should take varies considerably with the mission requirements. While most commonly used algorithms use a local cost function, mission requirements are typically defined over the entire run of the vehicle. Utility theoretic methods provide a normative tool to model tradeoffs over attributes (mission requirements) that the operator cares about. It is critical therefore, that preferences embedded in the utility function influence the local cost functions used. In this paper, we provide a framework for a feedback-based method to update the parameters of the local cost-function. We do so by using a geodesic-based method for path planning given the terrain inputs, followed by a physics-based simulation of a vehicle to evaluate the attributes. These attributes are then combined into a multiattribute utility function. An optimization-based approach is used to find the parameters of the cost function that maximizes this multiattribute utility. We present our approach on a vehicle navigation example over a terrain acquired from United States Geological Survey data.
<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicle navigation, both global and local, makes use of large amounts of multifactorial data from onboard sensors, prior information, and simulations to safely navigate a chosen terrain. Additionally, as each mission has a unique set of requirements, operational environment and vehicle capabilities, any fixed formulation for the cost associated with these attributes is sub-optimal across different missions. Much work has been done in the literature on finding the optimal cost definition and subsequent mission pathing given sufficient measurements of the preference over the mission factors. However, obtaining these measurements can be an arduous and computationally expensive task. Furthermore, the algorithms that utilize this large amount of multifactorial data themselves are time consuming and expensive. Often, it is valuable to make assessments about a terrain with limited information and using similarity with existing terrains without necessarily performing the entire simulation. This paper will investigate how topological data analysis (TDA) can be used to describe ontological features of the collected terrain data and how those features can be used to help navigation of the mission without making assumptions of the mission requirements or operator preferences.</div></div>
Published resources such as technical literature and patent documents are extremely useful in engineering design and form an important input to methods such as TRIZ. Often, design engineers will investigate these resources when working on new design problems. Aside from getting technical information and even direct design solutions, they may find the design principles used in each patent document a useful design stimulus. Unfortunately, patents are not classified based on such “design useful” characterizations. Using unsupervised clustering and Latent Dirichlet Allocation, this paper investigates four hypotheses using engineering patents in informing TRIZ based design. It first investigates the optimal number of TRIZ topics present in a corpus. Using this information, it attempts to map the TRIZ methods to the individual patents using unsupervised machine learning. Both rejected and accepted patents are then tested to determine if an autoencoder can successfully differentiate between the two, just from the text of the document. The autoencoder reconstruction errors of “Vehicle Brake Control” patents are also examined for possible correlation between reconstruction error and patent citation count. Finally, by combining the TRIZ clustering and the trained autoencoder, we show that high reconstruction error patents may be harder to assign to TRIZ methods than low reconstruction error patents.
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