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Roy R, Tu YP, Sheu LJ, Chieng WH, Tang LC, Ismail H. Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM). SENSORS (BASEL, SWITZERLAND) 2023; 23:3606. [PMID: 37050664 PMCID: PMC10099333 DOI: 10.3390/s23073606] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
Indoor mobile robot (IMR) motion control for e-SLAM techniques with limited sensors, i.e., only LiDAR, is proposed in this research. The path was initially generated from simple floor plans constructed by the IMR exploration. The path planning starts from the vertices which can be traveled through, proceeds to the velocity planning on both cornering and linear motion, and reaches the interpolated discrete points joining the vertices. The IMR recognizes its location and environment gradually from the LiDAR data. The study imposes the upper rings of the LiDAR image to perform localization while the lower rings are for obstacle detection. The IMR must travel through a series of featured vertices and perform the path planning further generating an integrated LiDAR image. A considerable challenge is that the LiDAR data are the only source to be compared with the path planned according to the floor map. Certain changes still need to be adapted into, for example, the distance precision with relevance to the floor map and the IMR deviation in order to avoid obstacles on the path. The LiDAR setting and IMR speed regulation account for a critical issue. The study contributed to integrating a step-by-step procedure of implementing path planning and motion control using solely the LiDAR data along with the integration of various pieces of software. The control strategy is thus improved while experimenting with various proportional control gains for position, orientation, and velocity of the LiDAR in the IMR.
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Affiliation(s)
- Rohit Roy
- Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; (R.R.); (Y.-P.T.); (L.-C.T.)
| | - You-Peng Tu
- Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; (R.R.); (Y.-P.T.); (L.-C.T.)
| | - Long-Jye Sheu
- Department of Mechanical Engineering, Chung Hua University, Hsinchu 30012, Taiwan;
| | - Wei-Hua Chieng
- Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; (R.R.); (Y.-P.T.); (L.-C.T.)
| | - Li-Chuan Tang
- Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; (R.R.); (Y.-P.T.); (L.-C.T.)
| | - Hasan Ismail
- Jurusen Teknik Mesin, Universitas Negeri Malang, Malang 55165, Indonesia;
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Yu B, Zhu H, Xue D, Xu L, Zhang S, Li B. A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1128. [PMID: 36010789 PMCID: PMC9407301 DOI: 10.3390/e24081128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/10/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Parameter calibration is critical for self-localization based on dead reckoning in the control of intelligent vehicles such as autonomous driving. Most traditional calibration methods for robotics control based on dead reckoning rely on data collection with specially designed paths. For the calibration of parameters in the control of intelligent vehicles, the design of such paths is considered impossible due to the complexity of road conditions. To solve this problem, an optimization-based dead reckoning calibration scheme is introduced in this research using the differential global positioning system to obtain the actual positions of the intelligent vehicle. In this scheme, the difference between the positions obtained through dead reckoning and the positions obtained through the differential global positioning system is selected as the optimization objective function to be minimized. An adaptive quantum-inspired evolutionary algorithm is developed to improve the quality and efficiency of optimization. Experiments with an intelligent vehicle were also conducted to demonstrate the effectiveness of the developed calibration scheme. In addition, the newly introduced adaptive quantum-inspired evolutionary algorithm is compared with the classic genetic algorithm and the classic quantum-inspired evolutionary algorithm using eight benchmark test functions considering computation quality and efficiency.
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Affiliation(s)
- Biao Yu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Hui Zhu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Deyi Xue
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Liwei Xu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Shijin Zhang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Bichun Li
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
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3
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Sousa RB, Petry MR, Costa PG, Moreira AP. OptiOdom: a Generic Approach for Odometry Calibration of Wheeled Mobile Robots. J INTELL ROBOT SYST 2022. [DOI: 10.1007/s10846-022-01630-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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4
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Zhao H, Chen W, Zhou S, Liu YH. Parameter Estimation of an Industrial Car-Like Tractor. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3068943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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5
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Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11125490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The exploration of planetary surfaces with unmanned wheeled vehicles will require sophisticated software for guidance, navigation and control. Future missions will be designed to study harsh environments that are characterized by rough terrains and extreme conditions. An accurate knowledge of the trajectory of planetary rovers is fundamental to accomplish the scientific goals of these missions. This paper presents a method to improve rover localization through the processing of wheel odometry (WO) and inertial measurement unit (IMU) data only. By accurately defining the dynamic model of both a rover’s wheels and the terrain, we provide a model-based estimate of the wheel slippage to correct the WO measurements. Numerical simulations are carried out to better understand the evolution of the rover’s trajectory across different terrain types and to determine the benefits of the proposed WO correction method.
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Fazekas M, Gáspár P, Németh B. Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration. SENSORS 2021; 21:s21020337. [PMID: 33419038 PMCID: PMC7825335 DOI: 10.3390/s21020337] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/30/2020] [Accepted: 01/01/2021] [Indexed: 11/16/2022]
Abstract
Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss-Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions.
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Affiliation(s)
- Máté Fazekas
- Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
- Correspondence:
| | - Péter Gáspár
- Systems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), H-1111 Budapest, Hungary; (P.G.); (B.N.)
| | - Balázs Németh
- Systems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), H-1111 Budapest, Hungary; (P.G.); (B.N.)
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7
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Modes V, Burgner-Kahrs J. Calibration of Concentric Tube Continuum Robots: Automatic Alignment of Precurved Elastic Tubes. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2019.2946060] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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8
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Chindakham N, Kim YY, Pirayawaraporn A, Jeong MH. Simultaneous Calibration of Odometry and Head-Eye Parameters for Mobile Robots with a Pan-Tilt Camera. SENSORS 2019; 19:s19163623. [PMID: 31434311 PMCID: PMC6721374 DOI: 10.3390/s19163623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 08/06/2019] [Accepted: 08/10/2019] [Indexed: 11/18/2022]
Abstract
In the field of robot navigation, the odometric parameters, such as wheel radii and wheelbase length, and the relative pose of the optical sensing camera with respect to the robot are very important criteria for accurate operation. Hence, these parameters are necessary to be estimated for more precise operation. However, the odometric and head-eye parameters are typically estimated separately, which is an inconvenience and requires longer calibration time. Even though several researchers have proposed simultaneous calibration methods that obtain both odometric and head-eye parameters simultaneously to reduce the calibration time, they are only applicable to a mobile robot with a fixed camera mounted, not for mobile robots equipped with a pan-tilt motorized camera systems, which is a very common configuration and widely used for wide view. Previous approaches could not provide the z-axis translation parameter between head-eye coordinate systems on mobile robots equipped with a pan-tilt camera. In this paper, we present a full simultaneous mobile robot calibration of head–eye and odometric parameters, which is appropriate for a mobile robot equipped with a camera mounted on the pan-tilt motorized device. After a set of visual features obtained from a chessboard or natural scene and the odometry measurements are synchronized and received, both odometric and head-eye parameters are iteratively adjusted until convergence prior to using a nonlinear optimization method for more accuracy.
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Affiliation(s)
- Nachaya Chindakham
- Department of Control and Instrumentation Engineering; Kwangwoon University, Kwangwoon-ro 1-gil 60, Nowon-gu, Seoul 01890, Korea
| | - Young-Yong Kim
- Research and Development Department; Thinkware Visual Technology, 240 Pangyoyeok-ro, Bundang-gu, Gyeonggi-do, Seongnam-si 463-400, Korea
| | - Alongkorn Pirayawaraporn
- Department of Control and Instrumentation Engineering; Kwangwoon University, Kwangwoon-ro 1-gil 60, Nowon-gu, Seoul 01890, Korea
| | - Mun-Ho Jeong
- Department of Control and Instrumentation Engineering; Kwangwoon University, Kwangwoon-ro 1-gil 60, Nowon-gu, Seoul 01890, Korea.
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10
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Sarim M, Kumar M, Jha R, Minai AA. Memristive device based learning for navigation in robots. BIOINSPIRATION & BIOMIMETICS 2017; 12:066011. [PMID: 28696337 DOI: 10.1088/1748-3190/aa7eab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra-low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with reinforcement learning based algorithms using local and global knowledge of the environment. The simulation as well as experimental results corroborate the validity and potential of the proposed learning scheme for robots. The results also show that our learning scheme approaches an optimal solution for some environment layouts in robot navigation.
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Affiliation(s)
- Mohammad Sarim
- University of Cincinnati, 2600 Clifton Ave, Cincinnati, OH 45221, United States of America
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11
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Chen Y, Wu F, Shuai W, Chen X. Robots serve humans in public places— KeJia robot as a shopping assistant. INT J ADV ROBOT SYST 2017. [DOI: 10.1177/1729881417703569] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Affiliation(s)
- Yingfeng Chen
- Department of Computer Sciences, University of Science and Technology of China, China
| | - Feng Wu
- Department of Computer Sciences, University of Science and Technology of China, China
| | - Wei Shuai
- Department of Computer Sciences, University of Science and Technology of China, China
| | - Xiaoping Chen
- Department of Computer Sciences, University of Science and Technology of China, China
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12
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13
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Research on localization and alignment technology for transfer cask. FUSION ENGINEERING AND DESIGN 2015. [DOI: 10.1016/j.fusengdes.2015.06.160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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14
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Abstract
SUMMARYIn global localization under the framework of a particle filter, the acquiring of effective observations of the whole particle system will be greatly effected by the uncertainty of a prior-map, such as unspecific structures and noises. In this study, taking the uncertainty of the prior-map into account, a localizability-based action selection mechanism for mobile robots is proposed to accelerate the convergence of global localization. Localizability is defined to evaluate the observations according to the prior-map (probabilistic grid map) and observation (laser range-finder) models based on the Cramér-Rao Bound. The evaluation considers the uncertainty of the prior-map and does not need to extract any specific observation features. Essentially, localizability is the determinant of the inverse covariance matrix for localization. Specifically, at the beginning of every filtering step, the action, which makes the whole particle system to achieve the maximum localizability distinctness, is selected as the actual action. Then there will be the increased opportunities for accelerating the convergence of the particles, especially in the face of the prior-map with uncertainty. Additionally, the computational complexity of the proposed algorithm does not increase significantly, as the localizability is pre-cached off-line. In simulations, the proposed active algorithm is compared with the passive algorithm (i.e. global localization with the random robot actions) in environments with different degrees of uncertainty. In experiments, the effectiveness of the localizability is verified and then the comparative experiments are conducted based on an intelligent wheelchair platform in a real environment. Finally, the experimental results are compared and analyzed among the existing active algorithms. The results demonstrate that the proposed algorithm could accelerate the convergence of global localization and enhance the robustness against the system ambiguities, thereby reducing the failure probability of the convergence.
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15
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Laser interferometry measurements based calibration and error propagation identification for pose estimation in mobile robots. ROBOTICA 2013. [DOI: 10.1017/s0263574713000660] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
SUMMARYA widely used method for pose estimation in mobile robots is odometry. Odometry allows the robot in real time to reconstruct its position and orientation from the wheels' encoder measurements. Given to its unbounded nature, odometry calculation accumulates errors with quadratic increase of error variance with traversed distance. This paper develops a novel method for odometry calibration and error propagation identification for mobile robots. The proposed method uses a laser-based interferometer to measure distance precisely. Two variants of the proposed calibration method are examined: the two-parameter model and the three-parameter model. Experimental results obtained using a Khepera 3 mobile robot showed that both methods significantly increase accuracy of the pose estimation, validating the effectiveness of the proposed calibration method.
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16
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Seegmiller N, Rogers-Marcovitz F, Miller G, Kelly A. Vehicle model identification by integrated prediction error minimization. Int J Rob Res 2013. [DOI: 10.1177/0278364913488635] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We present a highly effective approach for the calibration of vehicle models. The approach combines the output error technique of system identification theory and the convolution integral solutions of linear systems and stochastic calculus. Rather than calibrate the system differential equation directly for unknown parameters, we calibrate its first integral. This integrated prediction error minimization (IPEM) approach is advantageous because it requires only low-frequency observations of state, and produces unbiased parameter estimates that optimize simulation accuracy for the chosen time horizon. We address the calibration of models that describe both systematic and stochastic dynamics, such that uncertainties can be computed for model predictions. We resolve numerous implementation issues in the application of IPEM, such as the efficient linearization of the dynamics integral with respect to parameters, the treatment of uncertainty in initial conditions, and the formulation of stochastic measurements and measurement covariances. While the technique can be used for any dynamical system, we demonstrate its usefulness for the calibration of wheeled vehicle models used in control and estimation. Specifically we calibrate models of odometry, powertrain dynamics, and wheel slip as it affects body frame velocity. Experimental results are provided for a variety of indoor and outdoor platforms.
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Affiliation(s)
- Neal Seegmiller
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Greg Miller
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alonzo Kelly
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
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17
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Censi A, Franchi A, Marchionni L, Oriolo G. Simultaneous Calibration of Odometry and Sensor Parameters for Mobile Robots. IEEE T ROBOT 2013. [DOI: 10.1109/tro.2012.2226380] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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18
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Implementing Enzymatic Numerical P Systems for AI Applications by Means of Graphic Processing Units. TOPICS IN INTELLIGENT ENGINEERING AND INFORMATICS 2013. [DOI: 10.1007/978-3-642-34422-0_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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Mastrogiovanni F, Sgorbissa A, Zaccaria R. How the Location of the Range Sensor Affects EKF-based Localization. J INTELL ROBOT SYST 2012. [DOI: 10.1007/s10846-012-9673-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Affiliation(s)
- Rainer Kümmerle
- a Department of Computer Science , University of Freiburg , Georges-Koehler-Allee 079, D-79110, Freiburg , Germany
| | - Giorgio Grisetti
- a Department of Computer Science , University of Freiburg , Georges-Koehler-Allee 079, D-79110, Freiburg , Germany
- b Department of Systems and Computer Science , La Sapienza University of Rome , via Ariosto 25, I-00185, Rome , Italy
| | - Wolfram Burgard
- a Department of Computer Science , University of Freiburg , Georges-Koehler-Allee 079, D-79110, Freiburg , Germany
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Abstract
SUMMARYExact knowledge of the position and proper calibration of robots that move by wheels form an important foundation in mobile robot applications. In this context, a variety of sensory systems and techniques have been developed for accurate positioning of differential drive mobile robots. This paper, first, provides a brief overview of mobile robots positioning techniques and then, presents a new benchmark method capable of calibrating mobile robots with differential drive mechanisms to correct systematic errors. The proposed method is compared with the commonly used University of Michigan Benchmark (UMBmark) odometry method. Two sets of comparisons are conducted on six prototyped robots with differential drives. The first set of tests establishes the workability and accuracy that can be achieved with the new method and compares them with the ones obtained from the UMBmark technique. The second experiment compares the performance of a mobile robot, calibrated with either the UMBmark or the new method, for an unseen path. It is demonstrated that the proposed method of calibration is simple to implement, and leads to accuracy comparable to the UMBmark method. Specifically, while the error corrections in both methods are within ±5% of each other, the proposed method requires single straight line motion for calibration, which is believed to be simpler and less timely to implement than the square path motion required by the UMBmark technique. The method should therefore be considered seriously as a new tool when calibrating differential drive mobile robots.
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Antonelli G, Caccavale F, Grossi F, Marino A. A non-iterative and effective procedure for simultaneous odometry and camera calibration for a differential drive mobile robot based on the singular value decomposition. INTEL SERV ROBOT 2010. [DOI: 10.1007/s11370-010-0067-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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23
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Antonelli G, Chiaverini S. Linear estimation of the physical odometric parameters for differential-drive mobile robots. Auton Robots 2007. [DOI: 10.1007/s10514-007-9030-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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