Camilo Ordonez

The virtual wall approach to limit cycle avoidance for unmanned ground vehicles

Robot Navigation in unknown and very cluttered environments constitutes one of the key challenges in unmanned ground vehicle (UGV) applications. Navigational limit cycles can occur when navigating (UGVs) using behavior-based or other reactive algorithms. Limit cycles occur when the robot is navigating towards the goal but enters an enclosure that has its opening in a direction opposite to the goal. The robot then becomes effectively trapped in the enclosure. This paper presents a solution named the Virtual Wall Approach (VWA) to the limit cycle problem for robot navigation in very cluttered environments. This algorithm is composed of three stages: detection, retraction, and avoidance. The detection stage uses spatial memory to identify the limit cycle. Once the limit cycle has been identified, a labeling operator is applied to a local map of the obstacle field to identify the obstacle or group of obstacles that are causing the deadlock enclosure. The retraction stage defines a waypoint for the robot outside the deadlock area. When the robot crosses the boundary of the deadlock enclosure, a virtual wall is placed near the endpoints of the enclosure to designate this area as off-limits. Finally, the robot activates a virtual sensor so that it can proceed to its original goal, avoiding the virtual wall and obstacles found on its way. Simulations, experiments, and analysis of the VWA implemented on top of a preference-based fuzzy behavior system demonstrate the effectiveness of the proposed method.

Power modeling of a skid steered wheeled robotic ground vehicle

Analysis of the power consumption of a robotic ground vehicle (RGV) is important for planning since it enables motion plans that do not violate the power limitations of the motors, energy efficient path planning, prediction of the ability to complete a task based upon the vehicles current energy supply, and estimation of when the vehicle will need to refuel or recharge. Power modeling is particularly difficult for skid steered vehicles because of the complexities of properly taking into account the skidding that is used for vehicle turning. This paper begins with a 2-dimensional, second order differential equation of a skid steered, wheeled RGV and shows that the power model is terrain dependent and is a function of both the turning radius and linear velocity of the vehicle. This model was verified experimentally, and a comprehensive set of experiments was performed to describe the power consumption of a skid steered RGV on asphalt.

Terrain surface classification for autonomous ground vehicles using a 2D laser stripe-based structured light sensor

To increase autonomous ground vehicle (AGV) safety and efficiency on outdoor terrains the vehicles control system should have settings for individual terrain surfaces. A first step in such a terrain-dependent control system is classification of the surface upon which the AGV is traversing. This paper considers vision-based terrain surface classification for the path directly in front of the vehicle (≪ 1 m). Most visionbased terrain classification has focused on terrain traversability and not on terrain surface classification. The few approaches to classifying traversable terrain surfaces, with the exception of the use of infrared cameras to classify mud, have relied on stand-alone cameras that are designed for daytime use and are not expected to perform well in the dark. In contrast, this research uses a laser stripe-based structured light sensor, which uses a laser in conjunction with a camera, and hence can work at night. Also, unlike most previous results, the classification here does not rely on color since color changes with illumination and weather, and certain terrains have multiple colors (e.g., sand may be red or white). Instead, it relies only on spatial relationships, specifically spatial frequency response and texture, which captures spatial relationships between different gray levels. Terrain surface classification using each of these features separately is conducted by using a probabilistic neural network. Experimental results based on classifying four outdoor terrains demonstrate the effectiveness of the proposed methods.

POWER MODELING OF THE XRL HEXAPEDAL ROBOT AND ITS APPLICATION TO ENERGY EFFICIENT MOTION PLANNING

Analysis of the power consumption for walking and running robots is particularly important for trajectory planning tasks as it enables motion plans that minimize energy consumption and do not violate power limitations of the robot actuators. This paper builds upon previous work on wheeled skid-steered robots, and for curvilinear motion of the XRL hexapedal robot, presents models of the inner and outer side torques and power requirements. In addition, the applicability of the power model to energy efficient motion planning is illustrated for a walking gait on a vinyl surface.

Terrain identification for RHex-type robots

Terrain identification is a key enabling ability for generating terrain adaptive behaviors that assist both robot planning and motor control. This paper considers running legged robots from the RHex family) which the military plans to use in the field to assist troops in reconnaissance tasks. Important terrain adaptive behaviors include the selection of gaits) modulation of leg stiffness) and alteration of steering control laws that minimize slippage) maximize speed and/or reduce energy consumption. These terrain adaptive behaviors can be enabled by a terrain identification methodology that combines proprioceptive sensors already available in RHex-type robots. The proposed classification approach is based on the characteristic frequency signatures of data from leg observers) which combine current sensing with a dynamic model of the leg motion. The paper analyzes the classification accuracy obtained using both a single leg and groups of legs (through a voting scheme) on different terrains such as vinyl) asphalt) grass) and pebbles. Additionally) it presents a terrain classifier that works across various gait speeds and in fact almost as good as an overly specialized classifier.

Terrain surface classification with a control mode update rule using a 2D laser stripe-based structured light sensor

It is necessary for autonomous ground vehicles operating on outdoor terrains to identify and adapt to different terrains in order to improve their mobility and safety. This work presents a classification scheme to identify outdoor terrains and an update rule to reduce the possibility of implementing control modes based on classification inaccuracies. A laser stripe-based structured light sensor, which has a laser and infrared camera component, is used to sense terrains directly in front of the vehicle (<1m). Use of this infrared vision system allows sensing at night, without external lighting, unlike many previous vision-based approaches that rely on stand-alone cameras. Also, unlike many previous results, the classification algorithm presented here does not rely on measures of color, which are subject to illumination and weather conditions. Instead, the method presented here relies on spatial relationships which are captured in two quantities: spatial frequency from range data and texture from camera data. The presented terrain classification scheme uses a probabilistic neural network classifier to exploit the spatial differences in four terrains: asphalt, grass, gravel and sand. This approach yields empirical results that report a greater than 97% classification accuracy when both spatial frequency and texture features are used. Color robustness and lighting robustness is then shown through additional experiments. Furthermore, instead of implementing control modes directly from the identified terrains, it is shown that the use of current and past terrain detections allows for the rejection of misclassifications with minimal effect on the rate at which a new control mode can be implemented.

Modeling of Skid-Steered Wheeled Robotic Vehicles on Sloped Terrains

Skid-steered robots are commonly used in outdoor applications due to their mechanical simplicity, high maneuverability, and robustness. The maneuverability of these robots allows them, under ideal conditions (e.g., flat terrain and powerful actuators), to perform turning maneuvers ranging from point turns to straight line motion. However, sloped terrain, terrain with high friction, or actuator torque and power limitations can limit the achievable turning radii. This work presents the analysis and experimental verification of a dynamic model for skid-steered autonomous ground vehicles equipped with non ideal (i.e., torque and power limited) actuators and moving on sloped terrains. The experimental results show that the model is able to predict motor torques for the full range of turning radii on flat ground, i.e., from point turns to straight line motion. In addition, it is shown that the proposed model is able to predict motor torques (including motor saturation) and minimum turn radius as a function of terrain slope, vehicle heading, terrain parameters and actuator characteristics. This makes the model usable for curvilinear motion planning tasks on sloped surfaces.© 2012 ASME

Thermal Simulation of an Off-Grid Zero Emissions Building

This paper presents a simulation of the thermal response of the off-grid zero emissions building at the Florida State University Energy and Sustainability Center in efforts to assess the development of effective thermal management strategies for the building. The paper describes the OGZEB and its energy systems and presents a thermal model to compute the temperature and humidity distribution using a volume element methodology. The energy characterization conducted in this paper will be used in future studies to develop control strategies and minimize energy consumption.Copyright

Momentum based traversal of mobility challenges for autonomous ground vehicles

Autonomous ground vehicles operating in the field are likely to be faced with several mobility challenges such as piles of rubble, water crossings, steep hills, mud, and stiff vegetation patches. These scenarios are particularly critical for smaller robots with torque and power limited actuators, which as experimentally shown in this work can easily fail to accomplish their tasks in these environments. This paper motivates and provides a methodology that integrates the robot, actuator and terrain models with an efficient motion planner to exploit the vehicle momentum as a way to successfully traverse these difficult terrains. In particular, experimental results showing the efficacy of the proposed methodology are presented for a vegetation patch and a steep hill. Finally, a discussion of the necessary perception work to fully automate the process is included.

Dynamically feasible, energy efficient motion planning for skid-steered vehicles

Recent research has developed experimentally verified dynamic models for skid-steered wheeled vehicles and used these results to derive a power model for this important class of all-terrain vehicles. As presented in this paper, based on the torque limitations of the vehicle motors, the dynamic model can be used to develop payload and terrain-dependent minimum turn radius constraints and the power model can be used to predict the energy consumption of a given trajectory. This paper uses these results along with sampling based model predictive optimization to develop an effective methodology for generating dynamically feasible, energy efficient trajectories for skid-steered autonomous ground vehicles (AGVs) and compares the resultant trajectories with those based on the standard distance optimal trajectories. The simulated and experimental results consider an AGV moving at a constant forward velocity on both wood and asphalt surfaces under various payloads. The results show that a small increase in the distance of a trajectory over the distance optimal trajectory can result in a dramatic savings in the AGV’s energy consumption. They also show that distance optimal planning can often produce trajectories that violate the motor torque constraints for skid-steered AGVs, which can result in poor navigation performance.