Effects of Turning Radius on Skid-Steered Wheeled Robot Power Consumption on Loose Soil

This research highlights the need for a new power model for skid-steered wheeled robots driving on loose soil and lays the groundwork to develop such a model. State-of-the-art power modeling assumes hard ground; under typical assumptions this predicts constant power consumption over a range of small turning radii where the inner wheels are rotating backwards. However, experimental results performed both in the field and in a controlled laboratory sandbox show that, on sand, power is not in fact constant with respect to turning radius. Power peaks by 20% in a newly identified range of turns where the inner wheels rotate backwards but are being dragged forward. This range of turning radii spans from half the rover width to R', the radius at which the inner wheel is not commanded to turn. Data shows higher motor torque and wheel sinkage in this range. To progress toward predicting the required power for a skid-steered wheeled robot to maneuver on loose soil, a preliminary version of a two-dimensional slip-sinkage model is proposed, along with a model of the force required to bulldoze the pile of sand that accumulates next to the wheels as it they are skidding. However, this is shown to be a less important factor contributing to the increased power in small-radius turns than the added inner wheel torque induced by dragging these wheels through the piles of sand they excavate by counter-rotation (in the identified range of turns). Finally, since a direct application of a power model is to design energy-efficient paths, time dependency of power consumption is also examined. Experiments show reduced rover angular velocity in sand around turning radii where the inner wheels are not rotated and this leads to the introduction to a new parameter to consider in path planning: angular slip

DESIGN AND ANALYSIS OF ROVER WHEEL TESTBED

Wheel performance has been one of the limiting factors in interplanetary rover mis-sions. Because the rigors of space restrict use of conventional tire materials, roverwheels suffer from lack of traction, high risk of snagging, and little or no compliance,which limits the rover's ability to explore and traverse discontinuous terrain. Whatis worse is that these limitations go unresolved by the current lack of testing. Theconcept that wheel utilization and design are enhanced by testing is not new. TheApollo program enjoyed substantial testing of the Lunar Rover Vehicle's wheel butat a tremendous cost in time and money, which is probably the reason for its currentlow priority. Single wheel testing is a solution to this problem because it can cheaplyprovide data for a full rover assembly's performance. This paper details these prob-lems and provides solutions to several road blocks of using single wheel testing asa substitute for full rover testing. The Suspension and Wheel Experimentation andEvaluation Testbed (S.W.E.E.T), which is specifically designed to test single wheelsin situations previously neglected, will enable engineers to iteratively improve wheeldesign and to develop more accurate and encompassing mission contingency strategieswithout the cost and time of full rover testing

Interactive multiple model filtering for robotic navigation and tracking applications

The work contained in this thesis focuses on two main objectives. The first objective is to evaluate the Interactive Multiple Model (IMM) filter method for robotic applications including inertial navigation systems (INS) and computer vision tracking. The second objective is to design an experimental testbed for multi-model mobile robot state estimation research in the Intelligent Systems Laboratory (ISLAB) at Memorial University. An IMM estimator uses multiple filters that run simultaneously to produce a combined weighted estimation of an observed system’s states. The weights are functions of the likelihood of how well each individual filter matches the current behaviour exhibited by the system. The performance of IMM filtering is evaluated using two different strategies for augmenting the system’s filter banks. The first method uses multiple kinematic models (constant velocity and constant acceleration models) in a mean-shift-based computer vision tracking application. The results of this experiment indicate that the IMM improves tracking performance due to its ability to adapt to the continuously changing motion characteristics of 2D blobs in videos. The second approach uses the same kinematics for each filter; however, the process and sensor noise parameters are tuned differently for each model. This method is tested in INS applications for both an automobile and a skid-steer mobile robot (Seekur Jr). Results show that the method improves INS tracking over single model Extended Kalman Filter (EKF) designs. Furthermore, an augmented state-space model containing skid-steer instantaneous center of rotation (ICR) kinematics is presented for future testing on the Seekur Jr INS. The experimental testbed designed in this thesis work is an operational data acquisition system developed for use with the Seekur Jr robot. The Seekur Jr platform has been Robot Operating System (ROS) enabled with access to data streams from 2D Lidar, 3D nodding Lidar, inertial measurement unit, digital compass, wheel encoder, onboard Global Positioning System (GPS), real-time kinematic (RTK) differential global positioning system (DGPS) ground truth, and vision sensors. The physical setup and data networking aspects of the testbed have been used for validation of an IMM filter presented in this thesis and is fully configured for future multi-model localization experiments of the ISLAB

Planetary Rover Inertial Navigation Applications: Pseudo Measurements and Wheel Terrain Interactions

Accurate localization is a critical component of any robotic system. During planetary missions, these systems are often limited by energy sources and slow spacecraft computers. Using proprioceptive localization (e.g., using an inertial measurement unit and wheel encoders) without external aiding is insufficient for accurate localization. This is mainly due to the integrated and unbounded errors of the inertial navigation solutions and the drifted position information from wheel encoders caused by wheel slippage. For this reason, planetary rovers often utilize exteroceptive (e.g., vision-based) sensors. On the one hand, localization with proprioceptive sensors is straightforward, computationally efficient, and continuous. On the other hand, using exteroceptive sensors for localization slows rover driving speed, reduces rover traversal rate, and these sensors are sensitive to the terrain features. Given the advantages and disadvantages of both methods, this thesis focuses on two objectives. First, improving the proprioceptive localization performance without significant changes to the rover operations. Second, enabling adaptive traversability rate based on the wheel-terrain interactions while keeping the localization reliable. To achieve the first objective, we utilized the zero-velocity, zero-angular rate updates, and non-holonomicity of a rover to improve rover localization performance even with the limited available sensor usage in a computationally efficient way. Pseudo-measurements generated from proprioceptive sensors when the rover is stationary conditions and the non-holonomic constraints while traversing can be utilized to improve the localization performance without any significant changes to the rover operations. Through this work, it is observed that a substantial improvement in localization performance, without the aid of additional exteroceptive sensor information. To achieve the second objective, the relationship between the estimation of localization uncertainty and wheel-terrain interactions through slip-ratio was investigated. This relationship was exposed with a Gaussian process with time series implementation by using the slippage estimation while the rover is moving. Then, it is predicted when to change from moving to stationary conditions by mapping the predicted slippage into localization uncertainty prediction. Instead of a periodic stopping framework, the method introduced in this work is a slip-aware localization method that enables the rover to stop more frequently in high-slip terrains whereas stops rover less frequently for low-slip terrains while keeping the proprioceptive localization reliable

Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory

To build commercial robots, skid-steering mechanical design is of increased popularity due to its manufacturing simplicity and unique mechanism. However, these also cause significant challenges on software and algorithm design, especially for pose estimation (i.e., determining the robot's rotation and position), which is the prerequisite of autonomous navigation. While the general localization algorithms have been extensively studied in research communities, there are still fundamental problems that need to be resolved for localizing skid-steering robots that change their orientation with a skid. To tackle this problem, we propose a probabilistic sliding-window estimator dedicated to skid-steering robots, using measurements from a monocular camera, the wheel encoders, and optionally an inertial measurement unit (IMU). Specifically, we explicitly model the kinematics of skid-steering robots by both track instantaneous centers of rotation (ICRs) and correction factors, which are capable of compensating for the complexity of track-to-terrain interaction, the imperfectness of mechanical design, terrain conditions and smoothness, and so on. To prevent performance reduction in robots' lifelong missions, the time- and location- varying kinematic parameters are estimated online along with pose estimation states in a tightly-coupled manner. More importantly, we conduct in-depth observability analysis for different sensors and design configurations in this paper, which provides us with theoretical tools in making the correct choice when building real commercial robots. In our experiments, we validate the proposed method by both simulation tests and real-world experiments, which demonstrate that our method outperforms competing methods by wide margins.Comment: 18 pages in tota

Sistemas de suporte à condução autónoma adequados a plataforma robótica 4-wheel skid-steer: percepção, movimento e simulação

As competições de robótica móvel desempenham papel preponderante na difusão da ciência e da engenharia ao público em geral. E também um espaço dedicado ao ensaio e comparação de diferentes estratégias e abordagens aos diversos desafios da robótica móvel. Uma das vertentes que tem reunido maior interesse nos promotores deste género de iniciativas e entre o público em geral são as competições de condução autónoma. Tipicamente as Competi¸c˜oes de Condução Autónoma (CCA) tentam reproduzir um ambiente semelhante a uma estrutura rodoviária tradicional, no qual sistemas autónomos deverão dar resposta a um conjunto variado de desafios que vão desde a deteção da faixa de rodagem `a interação com distintos elementos que compõem uma estrutura rodoviária típica, do planeamento trajetórias à localização. O objectivo desta dissertação de mestrado visa documentar o processo de desenho e concepção de uma plataforma robótica móvel do tipo 4-wheel skid-steer para realização de tarefas de condução autónoma em ambiente estruturado numa pista que pretende replicar uma via de circulação automóvel dotada de sinalética básica e alguns obstáculos. Paralelamente, a dissertação pretende também fazer uma análise qualitativa entre o processo de simulação e a sua transposição para uma plataforma robótica física. inferir sobre a diferenças de performance e de comportamento.Mobile robotics competitions play an important role in the diffusion of science and engineering to the general public. It is also a space dedicated to test and compare different strategies and approaches to several challenges of mobile robotics. One of the aspects that has attracted more the interest of promoters for this kind of initiatives and general public is the autonomous driving competitions. Typically, Autonomous Driving Competitions (CCAs) attempt to replicate an environment similar to a traditional road structure, in which autonomous systems should respond to a wide variety of challenges ranging from lane detection to interaction with distinct elements that exist in a typical road structure, from planning trajectories to location. The aim of this master’s thesis is to document the process of designing and endow a 4-wheel skid-steer mobile robotic platform to carry out autonomous driving tasks in a structured environment on a track that intends to replicate a motorized roadway including signs and obstacles. In parallel, the dissertation also intends to make a qualitative analysis between the simulation process and the transposition of the developed algorithm to a physical robotic platform, analysing the differences in performance and behavior

Predicting planetary rover mobility in reduced gravity using 1-g experiments

Traversing granular regolith, especially in reduced gravity environments, remains a potential challenge for wheeled rovers. Mitigating hazards for planetary rovers requires testing in representative environments, but direct Earth-based testing fails to account for the effect of reduced gravity on the soil itself. Here, experimental apparatus and techniques for reduced-gravity flight testing are used to systematically evaluate three existing Earth-based testing methods and develop guidelines for their use and interpretation: (i) reduced-weight testing, (ii) matching soil testing instrument response through soil simulant design, and (iii) granular scaling laws (GSL). Experimentation campaigns flying reduced-gravity parabolas, with soil and wheel both in lunar-g, have shown reductions in net traction of 20% or more and increases in sinkage of up to 40% compared to Earth-based testing methods (i) and (ii). Scaled-wheel testing, according to GSL (method iii) has shown better agreement with reduced-g tests (less than 10% error) and also tends to err on the side of conservative predictions. Limitations of GSL are investigated including a recently proposed cohesion constraint (that the wheel radius ratio must be the inverse of the gravity ratio) and the effects of wheel size and aspect ratio on GSL’s accuracy. It was found that the cohesion constraint can most likely be ignored for mildly cohesive soils such as lunar regolith. Limits on wheel sizes and aspect ratio variation are also proposed. The application of GSL to planetary rover testing is demonstrated through two studies undertaken in collaboration with NASA’s Jet Propulsion Laboratory. One study compares wheel designs for a skid-steer lunar rover in single-wheel tests scaled by GSL, demonstrating that diagonal grousers improve turning performance without requiring larger wheels. The second study involves application of GSL to the design of two reconfigurable test platforms for evaluating steep-terrain mobility performance. Another aspect of rover mobility testing—normal force control in single-wheel testbeds—is also investigated. An improved method for single-wheel testing, using a 4-bar mechanism, essentially eliminates normal force oscillations from frictional vertical sliders. Finally, guidelines for conducting and interpreting 1-g mobility tests for lunar rovers are presented, and potential avenues for future research are outlined

Proceedings of the 4th field robot event 2006, Stuttgart/Hohenheim, Germany, 23-24th June 2006

Zeer uitgebreid verslag van het 4e Fieldrobotevent, dat gehouden werd op 23 en 24 juni 2006 in Stuttgart/Hohenhei

Methods for the improvement of power resource prediction and residual range estimation for offroad unmanned ground vehicles

Unmanned Ground Vehicles (UGVs) are becoming more widespread in their deployment. Advances in technology have improved not only their reliability but also their ability to perform complex tasks. UGVs are particularly attractive for operations that are considered unsuitable for human operatives. These include dangerous operations such as explosive ordnance disarmament, as well as situations where human access is limited including planetary exploration or search and rescue missions involving physically small spaces. As technology advances, UGVs are gaining increased capabilities and consummate increased complexity, allowing them to participate in increasingly wide range of scenarios. UGVs have limited power reserves that can restrict a UGV’s mission duration and also the range of capabilities that it can deploy. As UGVs tend towards increased capabilities and complexity, extra burden is placed on the already stretched power resources. Electric drives and an increasing array of processors, sensors and effectors, all need sufficient power to operate. Accurate prediction of mission power requirements is therefore of utmost importance, especially in safety critical scenarios where the UGV must complete an atomic task or risk the creation of an unsafe environment due to failure caused by depleted power. Live energy prediction for vehicles that traverse typical road surfaces is a wellresearched topic. However, this is not sufficient for modern UGVs as they are required to traverse a wide variety of terrains that may change considerably with prevailing environmental conditions. This thesis addresses the gap by presenting a novel approach to both off and on-line energy prediction that considers the effects of weather conditions on a wide variety of terrains. The prediction is based upon nonlinear polynomial regression using live sensor data to improve upon the accuracy provided by current methods. The new approach is evaluated and compared to existing algorithms using a custom ‘UGV mission power’ simulation tool. The tool allows the user to test the accuracy of various mission energy prediction algorithms over a specified mission routes that include a variety of terrains and prevailing weather conditions. A series of experiments that test and record the ‘real world’ power use of a typical small electric drive UGV are also performed. The tests are conducted for a variety of terrains and weather conditions and the empirical results are used to validate the results of the simulation tool. The new algorithm showed a significant improvement compared with current methods, which will allow for UGVs deployed in real world scenarios where they must contend with a variety of terrains and changeable weather conditions to make accurate energy use predictions. This enables more capabilities to be deployed with a known impact on remaining mission power requirement, more efficient mission durations through avoiding the need to maintain excessive estimated power reserves and increased safety through reduced risk of aborting atomic operations in safety critical scenarios. As supplementary contribution, this work created a power resource usage and prediction test bed UGV and resulting data-sets as well as a novel simulation tool for UGV mission energy prediction. The tool implements a UGV model with accurate power use characteristics, confirmed by an empirical test series. The tool can be used to test a wide variety of scenarios and power prediction algorithms and could be used for the development of further mission energy prediction technology or be used as a mission energy planning tool

A Novel Method for Prediction of Mobile Robot Maneuvering Spaces

As the operational uses of mobile robots continue to expand, it becomes useful to be able to predict the admissible maneuvering space to prevent the robot from executing unsafe maneuvers. A novel method is proposed to address this need by using force-moment diagrams to characterize the robot’s maneuvering space in terms of path curvature and curvature rate. Using the proposed superposition techniques, these diagrams can then be transformed in real-time to provide a representation of the permissible maneuvering space while allowing for changes in the robot’s loading and terrain conditions. Simulation results indicate that the technique can be applied to determine the appropriate maneuvering space for a given set of loading conditions, longitudinal acceleration, and tire-ground coefficient of friction. This may lead to potential expansion in the ability to integrate predictive vehicle dynamics into autonomous controllers for mobile robots and a corresponding potential to safely increase operating speeds