Learned and Controlled Autonomous Robotic Exploration in an Extreme, Unknown Environment

Exploring and traversing extreme terrain with surface robots is difficult, but highly desirable for many applications, including exploration of planetary surfaces, search and rescue, among others. For these applications, to ensure the robot can predictably locomote, the interaction between the terrain and vehicle, terramechanics, must be incorporated into the model of the robot's locomotion. Modeling terramechanic effects is difficult and may be impossible in situations where the terrain is not known a priori. For these reasons, learning a terramechanics model online is desirable to increase the predictability of the robot's motion. A problem with previous implementations of learning algorithms is that the terramechanics model and corresponding generated control policies are not easily interpretable or extensible. If the models were of interpretable form, designers could use the learned models to inform vehicle and/or control design changes to refine the robot architecture for future applications. This paper explores a new method for learning a terramechanics model and a control policy using a model-based genetic algorithm. The proposed method yields an interpretable model, which can be analyzed using preexisting analysis methods. The paper provides simulation results that show for a practical application, the genetic algorithm performance is approximately equal to the performance of a state-of-the-art neural network approach, which does not provide an easily interpretable model.Comment: Published in: 2019 IEEE Aerospace Conference Date of Conference: 2-9 March 2019 Date Added to IEEE Xplore: 20 June 201

Client-Oriented Blind Quality Metric for High Dynamic Range Stereoscopic Omnidirectional Vision Systems

A high dynamic range (HDR) stereoscopic omnidirectional vision system can provide users with more realistic binocular and immersive perception, where the HDR stereoscopic omnidirectional image (HSOI) suffers distortions during its encoding and visualization, making its quality evaluation more challenging. To solve the problem, this paper proposes a client-oriented blind HSOI quality metric based on visual perception. The proposed metric mainly consists of a monocular perception module (MPM) and binocular perception module (BPM), which combine monocular/binocular, omnidirectional and HDR/tone-mapping perception. The MPM extracts features from three aspects: global color distortion, symmetric/asymmetric distortion and scene distortion. In the BPM, the binocular fusion map and binocular difference map are generated by joint image filtering. Then, brightness segmentation is performed on the binocular fusion image, and distinctive features are extracted on the segmented high/low/middle brightness regions. For the binocular difference map, natural scene statistical features are extracted by multi-coefficient derivative maps. Finally, feature screening is used to remove the redundancy between the extracted features. Experimental results on the HSOID database show that the proposed metric is generally better than the representative quality metric, and is more consistent with the subjective perception

Selecting fracturing interval for the exploitation of tight oil reservoirs from logs: a case study

The optimal selection of fracturing interval for the exploitation of tight oil reservoirs is very important for formulating a development program. In this study, the reservoir quality and the reservoir fracability are evaluated, and the criteria for the optimal selection of the fracturing interval are established, using the tight reservoir in the the Qing I Member of Qingshankou Formation in the Daqingzijing Oilfield of China as the study site. The results indicate that the porosity, the oil saturation and the effective thickness of tight reservoir are keys to optimizing the fracturing interval. The brittleness index and the difference coefficient among the horizontal stresses in the reservoir have a strong influence on fracability. The stress difference coefficient in the reservoir is smaller and the reservoir develops microfractures, the complex mesh fractures are easier to occur during fracturing. The stress difference between the reservoir and the surrounding bed is small and the thickness of the surrounding bed is thin, it is easy to communicate with adjacent oil-bearing layers when fracturing