Accurate Odometry Research Articles

Accurate calibration of systematic errors for car-like mobile robots using experimental orientation errors

One of the essential technologies for autonomous navigation is localization. Localization is important because accurate pose estimation is required for path planning and motion control. In order to improve localization accuracy, a relative positioning method on the basis of accurate odometry is necessary. Odometry calibration methods for two wheel differential drive robots have been researched for many years. However, it is difficult to find odometry calibration methods for car-like mobile robots. In this paper, an accurate calibration method for car-like mobile robots is proposed. Experimentally measured orientation errors were used to improve the accuracy of the calibration method. There are two contributions in this paper. The first is the significant reduction of calibration errors by the use of accurate calibration equation. In the previous research, calibration equation required approximations. However, there is no approximation error in the proposed equation owing to the use of orientation errors, not positional errors. The second is the experimental convenience. The orientations can be easily measured by onboard inertial sensors. Therefore, calibration experiments can be easily carried out in both indoor and outdoor environments. The presented experimental results show that resultant performance of the proposed scheme is superior to the results of the previous research.

Design, Development, and Mobility Evaluation of an Omnidirectional Mobile Robot for Rough Terrain

Omnidirectional vehicles have been widely applied in several areas, but most of them are designed for the case of motion on flat, smooth terrain, and are not feasible for outdoor usage. This paper presents the design and development of an omnidirectional mobile robot that possesses high mobility in rough terrain. The omnidirectional robot consists of a main body with four sets of mobility modules, called an active split offset caster (ASOC). The ASOC module has independently driven dual wheels that produce arbitrary planar translational velocity, enabling the robot to achieve its omnidirectional motion. Each module is connected to the main body via a parallel link with shock absorbers, allowing the robot to conform to uneven terrain. In this paper, the design and development of the ASOC‐driven omnidirectional mobile robot for rough terrain are described. A control scheme that considers the kinematics of the omnidirectional mobile robot is presented. The mobility of the robot is also evaluated experimentally based on a metric called the ASOC mobility index. The mobility evaluation test clarifies a design tradeoff between terrain adaptability and omnidirectional mobility due to the shock absorbers. In addition, an odometry improvement technique that can reduce position estimation error due to wheel slippage is proposed. Experimental odometry tests confirmed that the proposed technique is able to improve the odometry accuracy for sharp‐turning maneuvers.

Co-robotic intra-row weed control system

The automation of intra-row weed control in row crop production systems is very challenging. This work describes the development and in-field assessment of an automatic intra-row, hoe-based weeding co-robot system with real-time pneumatic hoe actuation based on an accurate odometry sensing technique. The US National Science Foundation has identified a need for robots (called co-robots) that serve as co-workers and work beside, or cooperatively with, people. These co-robots have a symbiotic relationship with a human partner, where, as a team, they combine their relative strengths to jointly perform a task. Such co-robots should be relatively inexpensive and easy to use. In this work, mechanical weed control was achieved by a co-robot actuator that automatically positioned a pair of miniature hoes into the intra-row zone between crop plants. The design was tested in a precision transplanted row crop and may also be suitable for direct seeded row crops. Co-robot cost was minimised by limiting the system to a single, simple odometry sensor. Co-robot hoe actuation was controlled using pre-programmed knowledge of the crop planting pattern and real-time odometry data as the control input for hoe positioning. Low-frequency drift in the odometry control points relative to the actual plant locations was corrected occasionally as needed in real-time by a human partner monitoring system performance. The co-robot was evaluated in an experimental trial conducted on the UC Davis campus farm. Assessment was based upon the follow-up hand hoeing required after automated intra-row weeding in comparison to the labour required to manually hoe a control plot. The mean person hours required for hand hoeing weeds in the control were 0.241 h for the 100 m2 plot, while only 0.102 h 100 m−2 were required in follow-up labour to complete the weed removal in the plots weeded by the co-robot. This represents a 57.5% reduction in hand labour requirements for intra-row weed control and indicates that the co-robot could help reduce traditional hand hoeing labour requirements with mechanised weed control in intra-row areas between the crop plants.

차륜형 이동로봇의 방향각오차를 이용한 오도메트리 정밀보정기법

Accurate estimation of the robot’s position has an important role in autonomous navigation. Odometry is one of the most widely used techniques for mobile robot positioning. However, odometry has a well-known drawback that the position errors are accumulated when the travel distance increases. The UMBmark method is the conventional odometry calibration scheme for two wheel differential mobile robots. In the UMBmark method, the approximations for small angles are used in order to simplify the calculations. In this paper, we propose the new calibration scheme by using experimental orientation errors. Kinematic parameters can be calculated accurately without approximations by using experimental orientation errors. The numerical simulation and experimental results show that the odometry accuracy can be improved by the proposed method.

SPARTAN: Developing a Vision System for Future Autonomous Space Exploration Robots

Mars exploration is expected to remain a focus of the scientific community in the years to come. A Mars rover should be highly autonomous because communication between the rover and the terrestrial operation center is difficult, and because the vehicle should spend as much of its traverse time as possible moving. Autonomous behavior of the rover implies that the vision system provides both a wide view to enable navigation and three‐dimensional (3D) reconstruction, and at the same time a close‐up view ensuring safety and providing reliable odometry data. The European Space Agency funded project “SPAring Robotics Technologies for Autonomous Navigation” (SPARTAN) aimed to develop an efficient vision system to cover all such aspects of autonomous exploratory rovers. This paper presents the development of such a system, starting from the requirements up to the testing of the working prototype. The vision system was designed with the intention of being efficient, low‐cost, and accurate and to be implemented using custom‐designed vectorial processing by means of field programmable gate arrays (FPGAs). A prototype of the complete vision system was developed, mounted on a basic mobile robot platform, and tested. The results on both real‐world Mars‐like and long‐range simulated data are presented in terms of 3D reconstruction and visual odometry accuracy, as well as execution speed. The developed system is found to fulfill the set requirements.

Autonomous task partitioning in robot foraging: an approach based on cost estimation

We propose an approach for autonomous task partitioning in swarms of foraging robots. Task partitioning is the process of decomposing tasks into sub-tasks. Task partitioning impacts tasks execution and associated costs. Our approach is characterized by the use of a cost function, mapping the size of sub-tasks to the overall task cost. The robots model the cost function and use the model to select sub-tasks to perform, aiming to minimize costs. Our approach separates the task partitioning process from task-specific actions and it does not require a priori assumptions to be made about the best partitioning strategy to employ. We study a foraging scenario in which object transportation is performed by different robots, each moving objects for a limited distance. The robots autonomously decide the distance traveled on the basis of our approach. The robots use odometry for navigational purposes; we show that task partitioning reduces the impact of odometry errors and improves performance. We validate our approach using simulation-based experiments. We study how the swarm partitions transportation under a number of experimental conditions characterized by different levels of odometry accuracy, size of the environment and the swarm, and total transportation distance. Our approach leads to partitioning solutions that are appropriate for each condition.

Spatiotemporal 3D motion vector filtering method for robust visual odometry

Most of the previous visual odometry methods cannot deal with a large independently moving object that takes up over 50% of the image area. To overcome this problem, the spatiotemporal filter is incorporated into the RANSAC method to filter out false match that occurrs by a large independently moving object. This spatiotemporal filter uses the current and previous motion vector's length and direction. Experimental results demonstrate that the proposed method effectively rejects the motion vectors generated from large independently moving objects and improves the visual odometry accuracy.

차량형 이동로봇의 기구학적 파라미터 보정을 위한 수렴성 분석

Automated parking assist systems are being commercialized and rapidly spread in the market. In order to improve odometry accuracy, we proposed a practical odometry calibration scheme of Car-Like Mobile Robot (CLMR). However, there were some open problems in our prior work. For example, it was not clear whether the kinematic parameters always converged or not using the proposed calibration scheme. In addition, test driving had to be carried out "twice" without detailed explanation. This research aims to provide answers for the addressed questions though the convergence property analysis of the calibration scheme. In this paper, we evaluate on the effect of the kinematic parameter error on the odometry error at the final pose by numerical computation. The evaluation will show that the wheel diameter and tread of the CLMR can be calibrated by iterative test drives. In addition, the region of convergence in the parametric space will be discussed. Presented experimental results clearly showed that the proposed calibration scheme would be useful in practical applications.

Target tracking without line of sight using range from radio

We propose a framework for utilizing fixed ultra-wideband ranging radio nodes to track a moving target radio node in an environment without guaranteed line of sight or accurate odometry. For the case where the fixed nodes' locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. For the case of unknown fixed node locations, we present a two-step approach that first reconstructs the target node's path using Gaussian Process Latent Variable models (GPLVMs) and then uses that path to determine the locations of the fixed nodes. We present experiments verifying our algorithm in an office environment, and we compare our results to those generated by online and batch SLAM methods, as well as odometry mapping. Our algorithm is successful at tracking a moving target node without odometry and mapping the locations of fixed nodes using radio ranging data that are both noisy and intermittent.

Accurate calibration of kinematic parameters for two wheel differential mobile robots

Odometry using wheel encoders provides fundamental pose estimates for wheeled mobile robots. Systematic errors of odometry can be reduced by the calibration of kinematic parameters. The UMBmark method is one of the widely used calibration schemes for two wheel differential mobile robot. In this paper, an accurate calibration scheme of kinematic parameters is proposed by extending the conventional UMBmark. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that remarkably reduce the systematic error of odometry. The new equations were derived to overcome the limitation of the conventional schemes. The second contribution is to propose the design guideline of the test track for calibration experiments. The calibration performance can be significantly improved by appropriate design of the test track. The numerical simulations and experimental results show that the odometry accuracy can be improved by the proposed calibration schemes.

Motion planning algorithms of an omni-directional mobile robot with active caster wheels

This work deals with motion planning algorithms of an omni-directional mobile robot with active caster wheels. A typical problem occurred in the motion control of such omni-directional mobile robot, which has been identified through experimental experiences, is skidding of the mobile wheel. It sometimes results in uncertain rotation of the steering wheel. To cope with this problem, a motion planning algorithm which resolves the skidding problem and uncertain motions of the steering wheel is mainly investigated. For navigation of the mobile robot, the posture of the omni-directional mobile robot is initially calculated using the odometry information. Then, the accuracy of the mobile robot's odometry is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through simulations and experimentation, the feasibility of proposed algorithms was verified.

Design of Test Tracks for Odometry Calibration of Wheeled Mobile Robots

Pose estimation for mobile robots depends basically on accurate odometry information. Odometry from the wheel's encoder is widely used for simple and inexpensive implementation. As the travel distance increases, odometry suffers from kinematic modeling errors regarding the wheels. Therefore, in order to improve the odometry accuracy, it is necessary that systematic errors be calibrated. The UMBmark test is a practical and useful scheme for calibrating the systematic errors of two-wheeled mobile robots. However, the square path track size used in the test has not been validated. A consideration of the calibration equations, experimental conditions, and modeling errors is essential to improve the calibration accuracy. In this paper, we analyze the effect on calibration performance of the approximation errors of calibration equations and nonsystematic errors under experimental conditions. Then, we propose a test track size for improving the accuracy of odometry calibration. From simulation and experimental results, we show that the proposed test track size significantly improves the calibration accuracy of odometry under a normal range of kinematic modeling errors for robots.

Kinematic parameter calibration of a car-like mobile robot to improve odometry accuracy

Odometry provides fundamental pose estimates for wheeled vehicles. For accurate and reliable pose estimation, systematic and nonsystematic errors of odometry should be reduced. In this paper, we focus on systematic error sources of a car-like mobile robot (CLMR) and we suggest a novel calibration method. Kinematic parameters of the CLMR can be successfully calibrated by only a couple of test driving. After reducing deterministic errors by calibration, odometry accuracy can be further improved by redundant odometry fusion with the extended Kalman filter (EKF). Odometry fusion reduces nonsystematic or stochastic errors. Experimental verifications are carried out using a radio-controlled miniature car.

Calibration and planning techniques for mobile robots in industrial environments

PurposeThis paper aims to provide a simple and cheap calibration method to improve the odometry accuracy and to present an alternative method for the control of the path produced by an already existed path planning method.Design/methodology/approachThrough the integration of systematic odometry errors the robot's odometry parameters are estimated and through the replacement of a caster with an omniwheel the unmodeled sources of odometry errors are removed. The control of the paths shape is achieved through the definition of intermediate points and robots velocity at these points.FindingsThe paper finds that the odometry calibration method improves significantly odometry accuracy. Caster and omniwheel performance depends on the paths characteristics and on their quality. The use of intermediate points offers a very good control on the paths' shape.Research limitations/implicationsThe paper encourages the manufacturers of casters and omniwheels to improve their quality. The described path planning method inevitably halts the robot at certain points. Further research must be done to avoid this drawback.Practical implicationsOdometry calibration has become a simple procedure and can be easily implemented by robot manufacturers or researchers to improve odometry accuracy.Originality/valueThe odometry calibration method significantly reduces systematic odometry errors and makes odometry reliable apart from cheap. The influence of caster and omniwheel to odometry is examined along with the influence of better controlled path shape.

Holonomy in mobile robots

The search for a simple and accurate odometry is a main concern when working with mobile robots. This article presents a general analysis of the problem and proposes a particular solution to improve the odometry. The three crucial kinematical aspects of mobile robots (mobility, control, and positioning) are reviewed in detail for vehicles based both in conventional and in omnidirectional wheels. The latter case is more suitable from a maneuvering point of view as it provides the robot frame with the three Degrees Of Freedom (DOF) of plane motion without singular configurations. Moreover, a suitable design of the omnidirectional wheels leads to a strictly invariant Jacobian matrix and thus to a linear control equation with constant coefficients. It is shown that such vehicles may have a holonomic behavior when moving under suitable kinematical restrictions without constraining their trajectory. In that case, the odometry is algebraic (instead of integrative) and thus more accurate. An application case is presented.

Wheel slippage and sinkage detection for planetary rovers

Mobile robots are increasingly being used in high-risk rough terrain situations, such as planetary exploration and military applications. Current control and localization algorithms are not well suited to rough terrain, since they generally do not consider the physical characteristics of the vehicle and its environment. Little attention has been devoted to the study of the dynamic effects occurring at the wheel-terrain interface, such as slip and sinkage. These effects compromise odometry accuracy, traction performance, and may even result in entrapment and consequent mission failure. This paper describes methods for wheel slippage and sinkage detection aiming at improving vehicle mobility on soft sandy terrain. Novel measures for wheel slip detection are presented based on observing different onboard sensor modalities and defining deterministic conditions that indicate vehicle slippage. An innovative vision-based algorithm for wheel sinkage estimation is discussed based on edge detection strategy. Experimental results, obtained with a Mars rover-type robot operating in high-slippage sandy environments and with a wheel sinkage testbed, are presented to validate our approach. It is shown that these techniques are effective in detecting wheel slip and sinkage.

Mobile Robot Map Building from an Advanced Sonar Array and Accurate Odometry

Mobile Robot Map Building from an Advanced Sonar Array and Accurate Odometry