69 research outputs found

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

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    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

    Energy-Efficient Trajectory Planning for Skid-Steer Rovers

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    A skid-steer rover’s power consumption is highly dependent on the turning radius of its path. For example, a point turn consumes a lot of power compared to a straight-line motion. Thus, in path planning for this kind of rover, turning radius is a factor that should be considered explicitly. Based on the literature, there is a lack of analytical approach for finding energy-optimal paths for skid-steer rovers. This thesis addresses this problem for such rovers, specifically on obstacle-free hard ground. The equivalency theorem in this thesis indicates that, when using a popular power model for skid-steer rovers on hard ground, all minimum-energy solutions follow the same path irrespective of velocity constraints that may or may not be imposed. This non-intuitive result stems from the fact that with this model of the system the total energy is fully parametrized by the geometry of the path alone. It is shown that one can choose velocity constraints to enforce constant power consumption, thus transforming the energy-optimal problem to an equivalent time-optimal problem. Existing theory, built upon the basis of Pontryagin’s minimum principle to find the extremals for time-optimal trajectories for a rigid body, can then be used to solve the problem. Accordingly, the extremal paths are obtained for the energy-efficient path planning problem. As there is a finite number of extremals, they are enumerated to find the minimum-energy path for a particular example. Moreover, the analysis identifies that the turns in optimal paths (aside from a small number of special cases called whirls) are to be circular arcs of a particular turning radius, R′, equal to half of a skid-steer rover’s slip track. R′ is the turning radius at which the inner wheels of a skid-steer rover are not commanded to turn, and its description and the identification of its paramount importance in energy-optimal path planning are investigated. Experiments with a Husky UGV rover validate the energy-optimality of using R′ turns. Furthermore, a practical velocity constraint for skid-steer rovers is proposed that maintains constant forward velocity above R’ and constant angular velocity below it. Also, in separate but related work, it is shown that almost always equal “friction requirement” can be used to obtain optimal traction forces for a common and practical type of 4-wheel rover

    Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir

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    Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can jeopardize the performance and efficiency of a robot when performing agricultural activity. For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. A test bed equipped with a tracked-driven robot and three types o f terrain i.e. sand, gravel and vegetation has been developed. A small and low power MEMS accelerometer is integrated into the robot for measuring the vertical acceleration. In this experiment, the vibration signals resulted from the interaction between the robot and the different type of terrain were collected. An extensive experimental study was conducted to evaluate the effectiveness of SVM. The results in terms of accuracy of two machine learning techniques based on terrain classification are analyzed and compared. The results show that the robot that is equipped with an SVM can recognize different terrain conditions effectively. Such capability enables the robot to traverse across changing terrain conditions without being trapped in the field. Hence, this research work contributes to develop a self-adaptive agricultural robot in coping with different terrain conditions with minimum human supervision

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

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    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

    Collapsible Cubes: Removing Overhangs from 3D Point Clouds to Build Local Navigable Elevation Maps

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    Elevation maps offer a compact 2 1/2 dimensional model of terrain surface for navigation in field mobile robotics. However, building these maps from 3D raw point clouds con- taining overhangs, such as tree canopy or tunnels, can produce useless results. This paper proposes a simple processing of a ground-based point cloud that identifies and removes overhang points that do not constitute an obstacle for navigation while keeping vertical structures such as walls or tree trunks. The procedure uses efficient data structures to collapse unsupported 3D cubes down to the ground. This method has been successfully applied to 3D laser scans taken from a mobile robot in outdoor environments in order to build local elevation maps for navigation. Computation times show an improvement with respect to a previous point-based solution to this problem.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Rough-terrain mobile robot planning and control with application to planetary exploration

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    Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001.Includes bibliographical references (leaves 119-130).Future planetary exploration missions will require mobile robots to perform difficult tasks in highly challenging terrain, with limited human supervision. Current motion planning and control algorithms are not well suited to rough-terrain mobility, since they generally do not consider the physical characteristics of the rover and its environment. Failure to understand these characteristics could lead to rover entrapment and mission failure. In this thesis, methods are presented for improved rough-terrain mobile robot mobility, which exploit fundamental physical models of the rover and terrain. Wheel-terrain interaction has been shown to be critical to rough terrain mobility. A wheel-terrain interaction model is presented, and a method for on-line estimation of important model parameters is proposed. The local terrain profile also strongly influences robot mobility. A method for on-line estimation of wheel-terrain contact angles is presented. Simulation and experimental results show that wheel-terrain model parameters and contact angles can be estimated on-line with good accuracy. Two rough-terrain planning algorithms are introduced. First, a motion planning algorithm is presented that is computationally efficient and considers uncertainty in rover sensing and localization. Next, an algorithm for geometrically reconfiguring the rover kinematic structure to optimize tipover stability margin is presented. Both methods utilize models developed earlier in the thesis.(cont.) Simulation and experimental results on the Jet Propulsion Laboratory Sample Return Rover show that the algorithms allow highly stable, semi-autonomous mobility in rough terrain. Finally, a rough-terrain control algorithm is presented that exploits the actuator redundancy found in multi-wheeled mobile robots to improve ground traction and reduce power consumption. The algorithm uses models developed earlier in the thesis. Simulation and experimental results show that the algorithm leads to improved wheel thrust and thus increased mobility in rough terrain.by Karl David Iagnemma.Ph.D

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

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    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

    Adaptive Localization and Mapping for Planetary Rovers

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    Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach

    A robotic platform for precision agriculture and applications

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    Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.Le tecniche agricole sono state migliorate nel corso dei secoli per soddisfare la crescente domanda di aumento della popolazione mondiale. I recenti progressi tecnologici in termini di piattaforme robotiche possono essere sfruttati in questo contesto. Poiché la gestione del frutteto è una delle applicazioni più impegnative, a causa della sua struttura arborea e della necessaria interazione con l'ambiente, è stata oggetto di ricerca per fornire una soluzione personalizzata che sviluppi un nuovo concetto di veicolo agricolo. Il risultato si è concretizzato in un veicolo cingolato leggero, capace di effettuare una navigazione autonoma sia nello scenario di pieno campo che all'interno dei frutteti (navigazione interfilare). La progettazione meccanica, insieme all'implementazione del software, sono stati dettagliati per evidenziarne i punti di forza, accanto ad alcuni ulteriori miglioramenti previsti per incrementarne le prestazioni complessive. I test di stabilità statica hanno dimostrato che il veicolo può resistere a ripidi pendii. Sono stati inoltre studiati miglioramenti per affinare la stima dello slittamento che si verifica durante le manovre di svolta, tipico dei veicoli cingolati. L'architettura software è stata implementata utilizzando il framework Robot Operating System (ROS), in modo da sfruttare i pacchetti disponibili relativi a componenti base, come le interfacce dei sensori, e consentendo al contempo un'implementazione personalizzata degli algoritmi di navigazione sviluppati. I test in condizioni reali all'interno dei frutteti sperimentali dell'università hanno dimostrato la robustezza e la stabilità della soluzione con oltre 800 ore di lavoro sul campo. Il veicolo ha permesso di attivare e svolgere un'ampia gamma di attività agricole in maniera autonoma, come l'irrorazione, la falciatura e la raccolta di dati sul campo. Questi ultimi possono essere sfruttati per stimare automaticamente le proprietà più rilevanti del frutteto, come il conteggio e la calibratura dei frutti, la stima delle proprietà della chioma e la raccolta autonoma dei frutti con stime post-raccolta

    Design of a Mobile Robotic Platform with Variable Footprint

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    This thesis presents an in-depth investigation to determine the most suitable mobile base design for a powerful and dynamic robotic manipulator. It details the design process of such a mobile platform for use in an indoor human environment that is to carry a two-arm upper-body humanoid manipulator system. Through systematic dynamics analysis, it was determined that a variable footprint holonomic wheeled mobile platform is the design of choice for such an application. Determining functional requirements and evaluating design options is performed for the platform’s general configuration, geometry, locomotion system, suspension, and propulsion, with a particularly in-depth evaluation of the problem of overcoming small steps. Other aspects such as processing, sensing and the power system are dealt with sufficiently to ensure the feasibility of the overall proposed design. The control of the platform is limited to that necessary to determine the appropriate mechanical components. Simulations are performed to investigate design problems and verify performance. A basic CAD model of the system is included for better design visualization. The research carried out in this thesis was performed in cooperation with the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt)’s Robotics and Mechatronics Institute (DLR RM). The DLR RM is currently utilizing the findings of this research to finish the development of the platform with a target completion date of May 2008
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