Laura E. Ray

Estimation of Terrain Forces and Parameters for Rigid-Wheeled Vehicles

This paper provides a methodology for the estimation of resistance, thrust, and resistive torques on each wheel of a rigid-wheeled vehicle generated at the vehicle-terrain interface, and from these forces and moments, a methodology to estimate terrain parameters is presented. Terrain force estimation, which is independent of a terrain model, can infer the ability to accelerate, climb, or tow a load independent of the underlying terrain properties. When a terrain model is available, parameters of that model, such as soil cohesion, friction angle, maximum normal stress, and stress distribution parameters, are determined from estimated vehicle-terrain forces using a multiple-model estimation approach, providing parameters that relate to accepted mobility metrics. The methodology requires a standard proprioceptive sensor suite-accelerometers, rate gyros, wheel speeds, motor torques, and ground speed. Sinkage sensors are not required. Simulation results demonstrate efficacy of the method on three terrains spanning a range of soil cohesions reported in the literature.

Design and power management of a solar-powered “Cool Robot” for polar instrument networks

The Cool Robot is a four-wheel-drive, solar-powered, autonomous robot designed to support summertime science campaigns in Antarctica and Greenland over distances exceeding 500 km. This paper provides an overview of key features of the robot, including design for good mobility, high efficiency, and long-term deployment under solar power in harsh polar environments. The Cool Robots solar panel box, comprising panels on four sides and a top panel, encounters insolation variations with a bandwidth of up to 1 Hz due to sastrugi. The paper details a unique photovoltaic control algorithm to accommodate these variations. We deployed the robot at Summit Camp, Greenland to validate its mobility and power budget and to assess the photovoltaic control system. The 61 kg robot drove continuously at 0.78 m/s on soft snow, its 160 W average power demand met by solar power alone under clear skies above 16° sun elevation. The power-control system reliably matched input with demand as insolation varied during testing. A simple GPS waypoint-following algorithm provides low-bandwidth path planning and course correction and demonstrated reliable autonomous navigation during testing over periods of 5–8 h. Field data validate the Cool Robot design models and indicate that it will exceed its design goal of carrying a 15 kg payload 500 km across Antarctica in 2 weeks. A brief description of instrument payloads and scientific studies aided by networks of such autonomous solar robots is provided.

Performance of a solar-powered robot for polar instrument networks

The Cool robot is a four-wheel-drive, solar-powered autonomous vehicle designed to support summertime science campaigns in Antarctica and Greenland. We deployed the robot at Summit Camp, Greenland, during 2005 to validate its power budget and to assess its unique control system that matches solar power input with power demand as the robot drives over rough terrain. The 61-kg robot drove continuously at 0.78 m/s on soft snow, its 160-W average power demand met by solar power alone under clear skies when sun elevation exceeded 16deg. The power-control system reliably matched input with demand as insolation changed during the tests. A simple GPS waypoint-following algorithm provided reliable autonomous navigation over periods of 5-8 hours. The data validate our design models and indicate that the Cool Robot exceeds its design goal of carrying a 15-kg payload 500 km in two weeks on the Antarctic plateau

The Design of a Mobile Robot for Instrument Network Deployment in Antarctica

This paper describes the design and fabrication of a low cost, solar powered mobile robot to support a variety of scientific missions on the Antarctic plateau during the austral summer. Key to the overall design is maintaining a lightweight vehicle by using a high strength and stiffness honeycomb-fiberglass composite chassis, custom wheels and drivetrain mounting components, and high efficiency, low cost solar cells. A solar power availability analysis is detailed, demonstrating that in the low elevation of the summer sun and high albedo of pristine snow, a robot with panels on all sides exposed to direct and reflected sunlight provides ample power, even under worst-case insolation conditions. A relatively simple navigation and control algorithm provides low-bandwidth path planning and course correction. A description of potential instruments to be deployed and scientific studies aided by networks of such autonomous solar robots is provided.

Tuning principal component weights to individualize HRTFs

Prior research has investigated development of virtual auditory displays (VADs) using low-dimensional models of head related transfer functions (HRTFs) as a function of a finite number of principal components (PCs) and associated weights (PCWs). This paper investigates the effect of PCWs on horizontal plane HRTFs derived from a database of HRIRs through analytical optimization experiments. The experiments investigate whether average HRTFs can be tuned to match individual HRTFs. Results provide insight on the effect of tuning PCWs on spectral features of the HRTF. A reduced order modeling technique is used to compactly represent each HRTF. Subject testing results are provided, showing that a human can conduct the tuning procedure and reduce localization errors.

Autonomous GPR Surveys using the Polar Rover Yeti

The National Science Foundation operates stations on the ice sheets of Antarctica and Greenland to investigate Earths climate history, life in extreme environments, and the evolution of the cosmos. Understandably, logistics costs predominate budgets due to the remote locations and harsh environments involved. Currently, manual ground-penetrating radar (GPR) surveys must preceed vehicle travel across polar ice sheets to detect subsurface crevasses or other voids. This exposes the crew to the risks of undetected hazards. We have developed an autonomous rover, Yeti, specifically to conduct GPR surveys across polar ice sheets. It is a simple four-wheel-drive, battery-powered vehicle that executes autonomous surveys via GPS waypoint following. We describe here three recent Yeti deployments, two in Antarctica and one in Greenland. Our key objective was to demonstrate the operational value of a rover to locate subsurface hazards. Yeti operated reliably at −30 °C, and it has has good oversnow mobility and adequate GPS accuracy for waypoint-following and hazard georeferencing. It has acquired data on hundreds of crevasse encounters to improve our understanding of heavily crevassed traverse routes and to develop automated crevasse-detection algorithms. Importantly, it helped to locate a previously undetected buried building at the South Pole. Yeti can improve safety by decoupling survey personnel from the consequences of undetected hazards. It also enables higher-quality systematic surveys to improve hazard-detection probabilities, increase assessment confidence, and build datasets to understand the evolution of these regions. Yeti has demonstrated that autonomous vehicles have great potential to improve the safety and efficiency of polar logistics.

Cooperative multi-robot reinforcement learning: A framework in hybrid state space

In the area of autonomous multi-robot cooperation, much emphasis has been placed on how to coordinate individual robot behaviors in order to achieve an optimal solution to task completion as a team. This paper presents an approach to cooperative multi-robot reinforcement learning based on a hybrid state space representation of the environment to achieve both task learning and heterogeneous role emergence in a unified framework. The methodology also involves learning space reduction through a neural perception module and a progressive rescheduling algorithm that interleaves online execution and relearning to adapt to environmental uncertainties and enhance performance. The approach aims to reduce combinatorial complexity inherent in role-task optimization, and achieves a satisficing solution to complex team-based tasks, rather than a globally optimal solution. Empirical evaluation of the proposed framework is conducted through simulation of a foraging task.

Stability of potential function formation control with communication and processing delay

We derive conditions for which a circular formation of nonholonomic robots under potential function control is stable, where robots assume a radially-directed pose at equilibrium. In addition to the delay-free case, we investigate the stability of the equilibrium when communication and local processing introduce delay. It is shown that while sufficiently large processing delay always leads to instability, with the right choice of control parameters, the system can tolerate unbounded communication delay. The analytical results are compared with simulations of a fleet of nonholomonic robots as well as with experimental data.

Potential function control for multiple high-speed nonholonomic robots

Many approaches to the formation control problem for multi-robot systems have been proposed. In distributed consensus algorithm methods, and leader-follower structures the robots are explicitly assigned positions within the desired formation. By contrast, artificial potential function (APF) control generally does not specify a formation explicitly but rather drives the robots down the negative gradient of a potential field such that a formation emerges at a global or local minimum. The ad hoc emergence of the formation has several benefits, especially for a fleet of homogeneous vehicles: It allows for spontaneous adaptation of the formation to addition and removal of vehicles, and it allows for truly homogeneous control for each agent since no hierarchy or unique assignment in a constraint graph is needed. APF methods, however, are generally designed for and tested on robots that approximate fully holonomic double integrator point masses. APF methods designed for nonholonomic robots have been limited to robots with single integrator dynamics or to a single robot traveling at low speed. This video presents the results of an effort to adopt APF methods for high-speed, dynamic, nonholonomic robots. This paper describes the experimental testbed: a fleet of inexpensive 4-wheel drive skid-steered robots called Dynabots capable of speeds up to 10 m/s and accelerations of at least 4 m/s2.

An autonomous robotic platform for ground penetrating radar surveys

Detection of hidden surface crevasses on glaciers is a vital process involved in over-snow traverses for science and resupply missions in Polar regions. There are several areas warranting improvement in the current protocol for crevasse detection, which employs a human-operated ground penetrating radar (GPR) on a mid-weight tracked vehicle. In this fashion, a GPR scout team must plan an appropriate crevasse-free route by investigating paths across the glacier. This paper presents methods supporting a completely autonomous robotic system employing GPR probing of the glacier surface. We tested and evaluated three machine learning algorithms on post-processed Antarctic GPR data, collected by our robot and a Pisten Bully in 2009 and 2010 at McMurdo Station. We achieved 82% classification rate for a linear SVM, compared to 82% using logistic regression and 80% using a Bayes network for contrast. We also discuss independent versus sequential classification of GPR scans, and suggest improvements to or combinations of the most successful training models. Our experiment demonstrates the promise and reliability of real-time object detection with GPR.