On-Line Mobile Robot Model Identification Using Integrated Perturbative Dynamics

Rogers-Marcovitz, Forrest; Kelly, Alonzo

doi:10.1007/978-3-642-28572-1_29

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The predicted pose change can be calculated by (14). The actual pose change is unknown, but the observed pose change is available from position and heading sensors.…”

Section: Kinematic Parameters Estimation and Motion Predictionmentioning

confidence: 99%

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Motion Predicting of Autonomous Tracked Vehicles with Online Slip Model Identification

Lü

Xiong

Guo

2016

Mathematical Problems in Engineering

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Precise understanding of the mobility is essential for high performance autonomous tracked vehicles in challenging circumstances, though the complex track/terrain interaction is difficult to model. A slip model based on the instantaneous centers of rotation (ICRs) of treads is presented and identified to predict the motion of the vehicle in a short term. Unlike many research studies estimating current ICRs locations using velocity measurements for feedback controllers, we focus on predicting the forward trajectories by estimating ICRs locations using position measurements. ICRs locations are parameterized over both tracks rolling speeds and the kinematic parameters are estimated in real time using an extended Kalman filter (EKF) without requiring prior knowledge of terrain parameters. Simulation results verify that the proposed algorithm performs better than the traditional method when the pose measuring frequencies are low. Experiments are conducted on a tracked vehicle with a weight of 13.6 tons. Results demonstrate that the predicted position and heading errors are reduced by about 75% and the reduction of pose errors is over 24% in the absence of the real-time kinematic global positioning system (RTK GPS).

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“…The predicted pose change can be calculated by (14). The actual pose change is unknown, but the observed pose change is available from position and heading sensors.…”

Section: Kinematic Parameters Estimation and Motion Predictionmentioning

confidence: 99%

“…In [12], sliding parameters of agricultural tracked robot are estimated online based on the unscented Kalman filter. Further work for slip estimation online is done in [5,13,14], where the extended Kalman filter is utilized.…”

Section: Introductionmentioning

confidence: 99%

Motion Predicting of Autonomous Tracked Vehicles with Online Slip Model Identification

Lü

Xiong

Guo

2016

Mathematical Problems in Engineering

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“…The utilization of the above calculations for way finding for portable robot requires additional time and the finding of this way won't totally doable for constant development. There are numerous fluffy rationale techniques utilizing different executions or in blend with different systems [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Expert system for Robotic Path Planning

Madlol¹,

Abdulsadda²,

Bakry³

2020

IJEAT

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Abstract: Robotic planning to find the target our goal point/s is most important subject with the minimum distance and the fastest speed with obstacle avoidance expert system has been proposed. In this paper we try to compare and consider different scenario by taking two or more moving robot figure out the short path from the initial and the final point automatically through the map of many regular and irregular obstacles. Firstly, the adaptive fuzzy expert system is present where the fuzzy rule has been adaptive recursively through the robot moving, and then the potential field algorithm has been compared with the adaptive fuzzy system, the results demonstrated that the adaptive fuzzy is faster than the potential field but the accuracy moving of the potential field robotic path planning is much better. All the algorithms were failed when two robots moving from two different initial points to one final target point the why we have proposed particle swarm optimization (PSO) algorithm to solve such problem.

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“…In effect, we integrate the model rather than differentiate the measurements. In our recent initial work, we have developed online calibration techniques for learning vehicle slip rates [10]. In this paper, we extend those techniques to a more elegant formulation of the perturbative dynamics that incorporates all of initial condition errors, 3D terrain, and stochastic disturbances, all using the same underlying model.…”

Section: Introductionmentioning

confidence: 99%

A Unified Perturbative Dynamics Approach to Online Vehicle Model Identification

Seegmiller

Rogers-Marcovitz

Miller

et al. 2016

Springer Tracts in Advanced Robotics

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The motions of wheeled mobile robots are largely governed by contact forces between the wheels and the terrain. Inasmuch as future wheel-terrain interactions are unpredictable and unobservable, high performance autonomous vehicles must ultimately learn the terrain by feel and extrapolate, just as humans do. We present an approach to the automatic calibration of dynamic models of arbitrary wheeled mobile robots on arbitrary terrain. Inputs beyond our control (disturbances) are assumed to be responsible for observed differences between what the vehicle was initially predicted to do and what it was subsequently observed to do. In departure from much previous work, and in order to directly support adaptive and predictive controllers, we concentrate on the problem of predicting candidate trajectories rather than measuring the current slip. The approach linearizes the nominal vehicle model and then calibrates the perturbative dynamics to explain the observed prediction residuals. Both systematic and stochastic disturbances are used, and we model these disturbances as functions over the terrain, the velocities, and the applied inertial and gravitational forces. In this way, we produce a model which can be used to predict behavior across all of state space for arbitrary terrain geometry. Results demonstrate that the approach converges quickly and produces marked improvements in the prediction of trajectories for multiple vehicle classes throughout the performance envelope of the platform, including during aggressive maneuvering.

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On-Line Mobile Robot Model Identification Using Integrated Perturbative Dynamics

Cited by 3 publications

References 8 publications

Motion Predicting of Autonomous Tracked Vehicles with Online Slip Model Identification

Motion Predicting of Autonomous Tracked Vehicles with Online Slip Model Identification

Expert system for Robotic Path Planning

A Unified Perturbative Dynamics Approach to Online Vehicle Model Identification

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