Pose estimation of Ackerman steering vehicles for outdoors autonomous navigation

Weinstein, Alejandro; Moore, Kevin L.

doi:10.1109/icit.2010.5472738

Cited by 38 publications

(11 citation statements)

References 7 publications

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“…We generated the robot wheel odometry using the Ackerman model (Weinstein and Moore, 2010). The wheelbase of our robot is 1 . 6 m , the steering angle δ ∈ [ − 19 , 19 ] degrees, and the wheel diameter 0 . 57 m.…”

Section: The Datasetmentioning

confidence: 99%

The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

Pire

Mujica

Civera

et al. 2019

The International Journal of Robotics Research

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In this paper we present The Rosario Dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of 6 sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural SLAM/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, IMU, stereo camera and a GPS-RTK system. The dataset is publicly available in http://www.cifasis-conicet.gov.ar/robot/.

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Section: The Datasetmentioning

confidence: 99%

The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

Pire

Mujica

Civera

et al. 2019

The International Journal of Robotics Research

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“…Figure 8 shows the task window of team DRC-Hubo@UNLV's vehicle-driving task Path estimation is very important for collisions avoidance in the driving task. Therefore, a mathematical model of Polaris Ranger XP is built in the task module (Weinstein & Moore, 2010) considering its kinematic and dynamic features. In Figure 8, bounding boxes of Polaris are shown as white colored rectangles.…”

Section: Vehicle Drivingmentioning

confidence: 99%

Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

Sohn

Jang

et al. 2017

Journal of Field Robotics

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This paper presents a technical overview of Team DRC-Hubo@UNLV's approach to the 2015 DARPA Robotics Challenge Finals (DRC-Finals). The Finals required a robotic platform that was robust and reliable in both hardware and software to complete tasks in 60 min under degraded communication. With this point of view, Team DRC-Hubo@UNLV integrated methods and algorithms previously verified, validated, and widely used in the robotics community. For the communication aspect, a common shared memory approach that the team adopted to enable efficient data communication under the DARPA controlled network is described. A new perception head design (optimized for the tasks of the Finals) and its data processing are then presented. In the motion planning and control aspect, various techniques, such as wheel-driven navigation, zero-momentpoint (ZMP) -based locomotion, and position-based manipulation and controls, are described in this paper. By introducing strategically critical elements and key lessons learned from DRC-Trials 2013 and the testbed of Charleston, we also illustrate how DRC-Hubo has evolved successfully toward the DRC-Finals. C 2017 Wiley Periodicals, Inc.

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“…Based on Wallace's achievement, then Amidi proposed the "Pure-Pursuit" strategy. R.Craig Coulter [9] applies the PPA based on the kinematics model with the Ackerman steering model [10,11] to path tracking and achieves a pretty performance, however, the implementation is in no consideration of speed of the vehicle and curvature of the planned path while computing the parameters of the algorithm. Anibal Ollero [12] analyzed the relationship between the look-ahead distance and the stability.…”

Section: Introductionmentioning

confidence: 99%

Adaptive path tracking for unmanned ground vehicle

Zhang

Tian

et al. 2017

2017 IEEE International Conference on Unmanned Systems (ICUS)

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Abstract-UGV (Unmanned Ground Vehicle) have many potential applications in a variety of areas. Path tracking is a crucial issue for UGV and one of the most popular methods for path tracking is the PPA (Pure Pursuit Algorithm). Most of researches using the PPA only consider the look-ahead distance as a constant parameter or a function. Whereas in fact, the look-ahead distance is affected by some elements and it would directly influence the effect of path tracking. In this paper, an adaptive path tracking method based on the PPA for UGV is proposed, in which the look-ahead distance is selected in a range of value by comparing distance error between the planned path and the actual path. This proposed method will reduce tracking error in some road condition. The results of experiments show that, compared with the classical PPA, the performance in the effectiveness and accuracy of using the adaptive path tracking method is improved.Keywords-Unmanned Ground Vehicle (UGV); Pure Pursuit Algorithm (PPA); the look-ahead distance; distance error.

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Pose estimation of Ackerman steering vehicles for outdoors autonomous navigation

Cited by 38 publications

References 7 publications

The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

Adaptive path tracking for unmanned ground vehicle

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