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Hierarchical approaches and methodologies are commonly used for control system design and synthesis. Well-known model-based techniques are often applied to solve problems of complex and large-scale control systems. The general philosophy of decomposing control problems into modular and more manageable subsystem control problems applies equally to the growing domain of intelligent and autonomous systems. However, for this class of systems, new techniques for subsystem coordination and overall system control are often required. This article presents an approach to hierarchical control design and synthesis for the case where the collection of subsystems is comprised of fuzzy logic controllers and fuzzy knowledge-based decision systems. The approach is used to implement hierarchical behavior-based controllers for autonomous navigation of one or more mobile robots. Theoretical details of the approach are presented, followed by discussions of practical design and implementation issues. Example implementations realized on various physical mobile robots are described to demonstrate how the techniques may be applied in practical applications involving homogeneous and heterogeneous robot teams.

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This paper presents the study of the relationship between electrical properties and physical characteristics of the soil. Measures of apparent electrical resistivity of the soil were made for different types of soil, varying moisture content gradually while maintaining a constant compaction, and then varying the compaction and relating it to a constant humidity. Development of a correlation surface is proposed in order to identify granulometry of the soil from moisture and compaction measurements. For the study of spatial variability, two areas were chosen to allow the change of moisture content and compaction in order to verify the measurement capacity of apparent electrical resistivity of the soil as methodology to identify change in soil dynamics. Results obtained show correlations among apparent electrical resistivity of the soil, moisture, soil compaction and clay content.

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We present a micro aerial vehicle (MAV) system, built with inexpensive off-the-shelf hardware, for autonomously following trails in unstructured, outdoor environments such as forests. The system introduces a deep neural network (DNN) called TrailNet for estimating the view orientation and lateral offset of the MAV with respect to the trail center. The DNN-based controller achieves stable flight without oscillations by avoiding overconfident behavior through a loss function that includes both label smoothing and entropy reward. In addition to the TrailNet DNN, the system also utilizes vision modules for environmental awareness, including another DNN for object detection and a visual odometry component for estimating depth for the purpose of low-level obstacle detection. All vision systems run in real time on board the MAV via a Jetson TX1. We provide details on the hardware and software used, as well as implementation details. We present experiments showing the ability of our system to navigate forest trails more robustly than previous techniques, including autonomous flights of 1 km.

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