A study on the Delta-Bullington irregular terrain radiofrequency propagation model : assessing model suitability for use in decision support tools

Modeling the propagation of radiofrequency signals over irregular terrain is both challenging and critically important in numerous Army applications. One application of particular importance is the performance and radio connectivity of sensors deployed in scenarios where the terrain and the environment significantly impact signal propagation. This report investigates both the performance of and the algorithms and assumptions underlying the Delta-Bullington irregular terrain radiofrequency propagation model discussed in International Telecommunications Union Recommendation P.526-15. The aim is to determine its suitability for use within sensor-planning decision support tools. After reviewing free-space, spherical earth diffraction, and terrain obstacle diffraction losses, the report dis-cusses several important tests of the model, including reciprocity and geographic continuity of propagation loss over large areas of rugged terrain. Overall, the Delta-Bullington model performed well, providing reasonably rapid and geographically continuous propagation loss estimates with computational demands appropriate for operational use.

A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils

Measurements made in the Columbia River Basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP 2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modelled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin-Obukhov similarity and (ii) a modified Priestley-Taylor (P-T) algorithm with physically based ecophysiological constraints which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP 2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various timescales based on the modelled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP 2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

Adaptive Walking Control for a Quadruped Robot on Irregular Terrain Using the Complex-Valued CPG Network

In this paper, we propose a CPG (central pattern generator) network control system using motor dynamics for the gait planning of a quadruped robot with a trot walking pattern to climb up and down a slope and turn back and follow the symmetry of route. The CPG unit model, which includes two DC motors model, has the ability to generate the periodic joint angle with complex-value parameters. Through plural feedback parameters, the CPG network can adjust the frequency and amplitude of an internal neuron model such as a robot meeting an irregular surface of a road. Using the stride length and frequency of robot joint angles, the distance of walking with a trot pattern can be calculated. In order to confirm the validity of the proposed control system, a quadruped robot is produced to implement the adaptive walking system.

A Redundant Configuration of Four Low-Cost GNSS-RTK Receivers for Reliable Estimation of Vehicular Position and Posture

This paper proposes a low-cost sensor system composed of four GNSS-RTK receivers to obtain accurate position and posture estimations for a vehicle in real-time. The four antennas of the receivers are placed so that every three-antennas combination is optimal to get the most precise 3D coordinates with respect to a global reference system. The redundancy provided by the fourth receiver allows to improve estimations even more and to maintain accuracy when one of the receivers fails. A mini computer with the Robotic Operating System is responsible for merging all the available measurements reliably. Successful experiments have been carried out with a ground rover on irregular terrain. Angular estimates similar to those of a high-performance IMU have been achieved in dynamic tests.

High-Resolution Vertical Habitat Mapping of a Deep-Sea Cliff Offshore Greenland

Recent advances in deep-sea exploration with underwater vehicles have led to the discovery of vertical environments inhabited by a diverse sessile fauna. However, despite their ecological importance, vertical habitats remain poorly characterized by conventional downward-looking survey techniques. Here we present a high-resolution 3-dimensional habitat map of a vertical cliff hosting a suspension-feeding community at the flank of an underwater glacial trough in the Greenland waters of the Labrador Sea. Using a forward-looking set-up on a Remotely Operated Vehicle (ROV), a high-resolution multibeam echosounder was used to map out the topography of the deep-sea terrain, including, for the first time, the backscatter intensity. Navigational accuracy was improved through a combination of the USBL and the DVL navigation of the ROV. Multi-scale terrain descriptors were derived and assigned to the 3D point cloud of the terrain. Following an unsupervised habitat mapping approach, the application of a K-means clustering revealed four potential habitat types, driven by geomorphology, backscatter and fine-scale features. Using groundtruthing seabed images, the ecological significance of the four habitat clusters was assessed in order to evaluate the benefit of unsupervised habitat mapping for further fine-scale ecological studies of vertical environments. This study demonstrates the importance of a priori knowledge of the terrain around habitats that are rarely explored for ecological investigations. It also emphasizes the importance of remote characterization of habitat distribution for assessing the representativeness of benthic faunal studies often constrained by time-limited sampling activities. This case study further identifies current limitations (e.g., navigation accuracy, irregular terrain acquisition difficulties) that can potentially limit the use of deep-sea terrain models for fine-scale investigations.

Hexapod Robot Gait Switching for Energy Consumption and Cost of Transport Management Using Heuristic Algorithms

Due to the prospect of using walking robots in an impassable environment for tracked or wheeled vehicles, walking locomotion is one of the most remarkable accomplishments in robotic history. Walking robots, however, are still being deeply researched and created. Locomotion over irregular terrain and energy consumption are among the major problems. Walking robots require many actuators to cross different terrains, leading to substantial consumption of energy. A robot must be carefully designed to solve this problem, and movement parameters must be correctly chosen. We present a minimization of the hexapod robot’s energy consumption in this paper. Secondly, we investigate the reliance on power consumption in robot movement speed and gaits along with the Cost of Transport (CoT). To perform optimization of the hexapod robot energy consumption, we propose two algorithms. The heuristic algorithm performs gait switching based on the current speed of the robot to ensure minimum energy consumption. The Red Fox Optimization (RFO) algorithm performs a nature-inspired search of robot gait variable space to minimize CoT as a target function. The algorithms are tested to assess the efficiency of the hexapod robot walking through real-life experiments. We show that it is possible to save approximately 7.7–21% by choosing proper gaits at certain speeds. Finally, we demonstrate that our hexapod robot is one of the most energy-efficient hexapods by comparing the CoT values of various walking robots.