Burhman Q. Gates

A Real-Time, Interactive Simulation Environment for Unmanned Ground Vehicles: The Autonomous Navigation Virtual Environment Laboratory (ANVEL)

Modeling and simulation tools have become an integral part of modern engineering processes. In particular, accurate and efficient simulation tools are critical for the design, development, and testing of autonomous unmanned ground vehicles (UGVs). However, because of the complexity of the problem, many UGV simulators are computationally intensive, require expensive hardware to run, and are often not interactive or real-time. Those simulation environments that do provide users with some degree of interactivity and real-time or faster performance gain these features at the sacrifice of simulation fidelity, and these products often provide inadequate results. A new simulation environment for UGV design and development, called the Autonomous Navigation Virtual Environment Laboratory (ANVEL), has been created to address the need for a real-time, interactive, physics-based simulation environment for UGVs. ANVEL is able to meet this need by fusing readily available, off-the-shelf video game technology with high-fidelity, physics-based models. This paper presents the methodology used in developing ANVEL, an example use of ANVEL for development and testing of an autonomous UGV, and plans for the future development.

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.

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.

Application of an off-road mobility model to autonomous cross-country routing of unmanned ground vehicles

This paper describes the application of an Army-standard legacy off-road mobility model to cross-country route planning and negotiation by unmanned ground vehicles. A planned route is created from a movement map generated from existing terrain data. An unmanned ground vehicle negotiates the planned route and makes local routing adjustments based on a trafficability assessment of terrain features which are observed from the platform. This research leverages results from other work investigating the scalability of the existing legacy off-road mobility model to small vehicles (<500 kg). The legacy mobility model is the NATO Reference Mobility Model II (NRMM II), a standard for combat mobility modeling and procurement since the mid-90s.

Unmanned ground vehicle simulation with the Virtual Autonomous Navigation Environment

Unmanned and autonomous ground vehicles have the potential to revolutionize military and civilian navigation. Military vehicles, however, present unique challenges related to autonomous navigation that are not encountered in civilian applications. These include a high percentage of off-road navigation, navigation in hostile environments, navigation in GPS-denied environments, and navigation in urban environments where little data regarding road networks are available. These unique challenges require a deliberate approach for developing robust, reliable autonomous and unmanned systems that features extensive testing for performance and safety features in a wide variety of environments and conditions. In order to enable this approach, we developed a computational tool for simulating and predicting the performance of unmanned and autonomous ground vehicles in realistic environmental conditions. This tool, the Virtual Autonomous Navigation Environment, is discussed in this paper.

Establishing UGV power requirements based on mission profiles

The propulsion systems employed on unmanned ground vehicle platforms in Future Force Units of Action will likely involve electric or hybrid-electric drive. Power storage systems for these platforms will therefore be driven largely by expected power depletion rates. Resistances that propulsion systems must overcome during maneuvers will be a major factor affecting power depletion rates, and the resistance forces will vary drastically depending on the mission. Therefore, realistic mission-related considerations need to be applied when defining power storage requirements. The US Army has developed numerous models and simulations that use terra-mechanics algorithms to predict maneuver capability for ground vehicles as limited by terrain and environmental factors, and the algorithms employed for predicting maneuver capability in most of these models and simulations are founded on the terra-mechanics algorithms contained in the NATO Reference Mobility Model. The NATO Reference Mobility Model uses physics-based force balancing algorithms with terra-mechanics relationships that were empirically derived from decades of vehicle-terrain interaction research, and it also incorporates proven methodologies for assessing mission effectiveness in terms of maneuver capabilities. The terra-mechanics algorithms and methodologies for assessing mission effectiveness that are implemented in this model and in other related software tools, such as those used for route analysis, can be used to generate realistic mission-related resistance profiles for defining power storage requirements.