Learning of skid-steered kinematic and dynamic models for motion planning

Ordóñez, Camilo; Gupta, Nikhil; Reese, Brandon M.; Seegmiller, Neal; Kelly, Alonzo; Collins, Emmanuel G.

doi:10.1016/j.robot.2017.05.014

Cited by 27 publications

(18 citation statements)

References 15 publications

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“…In particular, this paper solves this problem involving a random variable by using machine learning regression. It bears mentioning that the predicted slip and the uncertainty in such prediction can be certainly useful for both slip compensation Gonzalez et al (2014) and motion planners Ordonez et al (2017). The methodology proposed here could complement those approaches by considering routes to a target point where uncertainty in slip is minimized.…”

Section: Related Workmentioning

confidence: 98%

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

González

Fiacchini

Iagnemma

2018

Robotics and Autonomous Systems

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This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning regression algorithms are trained considering proprioceptive sensing. This contribution is validated by using the MIT single-wheel testbed equipped with an MSL spare wheel. The combination of IMU-related and torque-related features outperforms the torque-related features only. Gaussian process regression results in a proper trade-off between accuracy and computation time. Another advantage of this algorithm is that it returns the variance associated with each prediction, which might be used for future route planning and control tasks. The paper also provides a comparison between machine learning regression and classification algorithms.

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Section: Related Workmentioning

confidence: 98%

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

González

Fiacchini

Iagnemma

2018

Robotics and Autonomous Systems

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“…The lateral dynamics of skidsteering high-speed tracked vehicles were presented, with a nonlinear track terrain model derived based on classic terra-mechanics [77]. Recently, both kinematic and dynamic models of a skid-steered robot were identified via a learning process based on the Extended Kalman filtering and an efficient neural network formulation [78]. In terms of machine control, an automated digging control system (ADCS) for a wheel loader was developed using a behaviour-based control structure combined with fuzzy logic, and implemented on a Caterpillar 980G wheel loader [79].…”

Section: Loadermentioning

confidence: 99%

Earthmoving Construction Automation with Military Applications: Past, Present and Future

Ha¹,

Yen²,

Balaguer³

2018

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Amongst increasing innovations in frontier engineering sciences, the advancements in Robotic and Autonomous Systems (RAS) has brought about a new horizon in construction applications. There is evidence of increasing interest in RAS technologies in the civil construction sector being reflected in construction efforts of many military forces. In particular, Army or ground-based military forces are frequently called upon to conduct construction tasks as part of military operations, tasks which could be partially or fully aided by the employment of RAS technologies. Along with recent advances in the Internet of Things (IoT) and cyberphysical system infrastructure, it is essential to examine the current technical feasibility, maturity, affordability, as well as the challenges and future directions of the adoption and application of RAS to military construction. This paper presents a comprehensive survey and provides a contemporary and industry-independent overview on the state-ofthe-art of construction automation used in defence, spanning current world's best practice through to that which is predicted over the coming years.

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“…On the other hand, the slip information directly decides the tractive and braking force that affect the robot's mobility and stability. Accurate position and velocity estimates are the basis of motion planning and tracking control [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

Liu

Yuan

et al. 2020

IEEE Access

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Benefiting from high mobility and robust mechanical structure, ground mobile robots are widely adopted in the outdoor environment. The mobility of skid-steered mobile robots highly depends on the nonlinear and uncertain interaction between the tire and terrain. This paper introduces an approach to estimate the position, orientation, velocity, and wheel slip for the skid-steered mobile robots navigating on off-road terrains. More specifically, a Multi-Innovation Unscented Kalman Filter (MI-UKF) is developed to fusing different sensors' data. Historical innovations generated along the time sequence are merged into the update process of standard UKF to improve the accuracy of motion estimation. In the proposed estimator, an asymmetric ICR kinematic indicating wheel slip is taken into localization process. A four-wheeled prototype is introduced and three challenging test scenarios are designed. The improvements in orientation and velocity estimation are achieved according to results comparison. In the turning maneuver, the ICRs-based model operates more steady than the traditional wheel slip/skid model.

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Learning of skid-steered kinematic and dynamic models for motion planning

Cited by 27 publications

References 15 publications

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

Earthmoving Construction Automation with Military Applications: Past, Present and Future

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

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