Jody D. Priddy

High fidelity sensor simulations for the virtual autonomous navigation environment

The Virtual Autonomous Navigation Environment (VANE) is a high-fidelity simulation environment for ground robotics. Physicsbased realism is the primary goal of the VANE. The VANE simulation incorporates realistic lighting, vehicle-terrain interaction, environmental attributions, and sensors. The sensor models, including camera, laser ranging, and GPS, are the focus of this work. These sensor models were designed to incorporate both internal (electronic) and external (environment) noise in order to produce a sensor output that closely matches that produced in real-world environments. This sensor output will allow roboticists to use simulation further into the development and debugging process before exposing robots to field conditions.

Development of a Multi-Year Database to Assess Off-Road Mobility Algorithms in Fine-Grained Soils

A gap in off-road mobility is the availability of the test data necessary for the development and validation of tyre, track and soil models. This paper presents the results from a multi-year program to create the database records for off-road vehicle environments (DROVE), a consolidated database of laboratory and field test results. A fine-grained soil component was added to DROVE by compiling 2657 test results. Performance parameters including drawbar pull, motion resistance, torque, sinkage, and slip were included in the database. The database was used to evaluate the accuracy of existing algorithms used in the vehicle-terrain interface (VTI) model. The results indicated that the VTI algorithms can provide reasonable predictions for a few parameters but for others, such as motion resistance, further improvements are warranted. The DROVE dataset can be used for several applications including evaluation of existing mobility models, development of improved algorithms, and validation of numerical simulations.

High-fidelity physics-based simulation of a UGV reconnaissance mission in a complex urban environment

Physics-based simulations of autonomous unmanned ground vehicles (UGV) present unique challenges and advantages compared to real-time simulations with lower-fidelity models. We have created a high-fidelity simulation environment, called the Virtual Autonomous Navigation Environment (VANE), to perform physics-based simulations of UGV. To highlight the capabilities of the VANE, we recently completed a simulation of a robot performing a reconnaissance mission in a typical Middle Eastern town. The result of the experiment demonstrated the need for physics-based simulation for certain circumstances such as LADAR returns from razor wire and GPS dropout and dilution of precision in urban canyons.

The Need for High-Fidelity Robotics Sensor Models

Simulations provide a safe, controlled setting for testing and are therefore ideal for rapidly developing and testing autonomous mobile robot behaviors. However, algorithms for mobile robots are notorious for transitioning poorly from simulations to fielded platforms. The difficulty can in part be attributed to the use of simplistic sensor models that do not recreate important phenomena that affect autonomous navigation. The differences between the output of simple sensor models and true sensors are highlighted using results from a field test exercise with the National Robotics Engineering Centers Crusher vehicle. The Crusher was manually driven through an area consisting of a mix of small vegetation, rocks, and hay bales. LIDAR sensor data was collected along the path traveled and used to construct a model of the area. LIDAR data were simulated using a simple point-intersection model for a second, independent path. Cost maps were generated by the Crusher autonomy system using both the real-world and simulated sensor data. The comparison of these cost maps shows consistencies on most solid, large geometry surfaces such as the ground, but discrepancies around vegetation indicate that higher fidelity models are required to truly capture the complex interactions of the sensors with complex objects.

New algorithms for predicting longitudinal motion resistance of wheels on dry sand

Predicting the resisting forces against a vehicle’s wheel during movement in loose sand is critical for optimizing tractive capabilities in desert regions, sand dunes, and beaches. We review existing braked, powered, and towed motion resistance equations and present improved algorithms based on field and laboratory measurements when the cone index is used to define soil strength. The algorithm predictions are compared against measured values available through Database Records for Off-road Vehicle Environments (DROVE), a database of tests conducted with wheeled vehicles. The examination of braked, towed, and powered motion resistance algorithms is considered for loads varying from 0.187 to 4.49 kN, tire diameters from 0.377 to 1.05 m, and soil strengths from 50 to 800 kPa. A simplified motion resistance algorithm was developed for each operation type utilizing a bootstrap technique. Simple relationships using wheel slip and the ratio of the contact pressure to the cone index are shown to provide predictions of motion resistance with accuracy comparable to more complex empirical models.

HPCMP CREATE-GV: Supporting Ground Vehicle Acquisition

The development of high-fidelity, physics-based software for analyzing ground vehicle concept designs and the mobility performance of wheeled and tracked ground vehicles is increasing important. The CREATE-GV toolset’s three modules are integrated to provide a complete performance evaluation of vehicle concept designs.