Particle filtering for online motion planning with task specifications

Berntorp, Karl; Cairano, Stefano Di

doi:10.1109/acc.2016.7525232

Cited by 17 publications

(4 citation statements)

References 20 publications

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“…To evaluate the performance of proposed method, it is compared with conventional Kalman filter and Particle filter (Berntorp K., et al, 2016). Fig.…”

Section: Performance Of the Stacked Denoising Autoencoder Based Exten...mentioning

confidence: 99%

Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning

Hassan

Saleem

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Navigation in dynamic environments for mobile robots is a difficult problem as it involves estimating the path of moving obstacles. The measured data usually contains a bias and noise in addition to its true value. Based on a stacked denoising autoencoder (SDAE), the enhanced Kalman filter developed in this paper can estimate the obstacle position from any type of noisy input. The extended Kalman filter's ability to predict an error-free path is impacted by the measurement noise covariance matrix employed. The SDAE is a neural network topology based on deep learning that can be used to determine the optimum covariance matrix. Both Adam and stochastic gradient learning algorithms are used to train the neural network. The robot's path is re-planned based on the predicted obstacle path to ensure safe navigation. MATLAB-based numerical simulations are used to demonstrate the utility and superiority of the proposed method over the traditional Kalman filter and Particle filter methodologies. The simulation results show that in the presence of any sort of noise, the proposed technique is exceptionally durable and reliable. The simulation findings also reveal that when it comes to denoising the measured data, the stacked denoising autoencoder with Adam optimizer is more efficient than the stochastic approach. The performance of the developed algorithm is validated in MATLAB simulated environments, and it can be extended for navigation tasks. In terms of computation time and robustness in closely spaced obstacles, simulation experiments demonstrated that the path planning using the proposed algorithm outperforms the hybrid A star, artificial potential field, and decision algorithms.

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“…To evaluate the performance of proposed method, it is compared with conventional Kalman filter and Particle filter (Berntorp K., et al, 2016). Fig.…”

Section: Performance Of the Stacked Denoising Autoencoder Based Exten...mentioning

confidence: 99%

Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning

Hassan

Saleem

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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“…The deterministic requirements represent goals that a dynamical system aims to satisfy, whereas the probability distribution represents tolerated deviations from the requirements accounting for uncertainties and noise, or that the requirements may not be perfectly achieved. The considered control objective for decision-making has been proposed in [6], [7], where a particle filter extracts the motion plan for autonomous driving from given requirements and their joint probability distribution. This paper considers the inverse problem, where motion plans are generated by a different actor, e.g., a human, who demonstrates how to operate the dynamical system.…”

Section: Key Contributionsmentioning

confidence: 99%

Inverse Learning for Data-Driven Calibration of Model-Based Statistical Path Planning

Menner

Berntorp

Zeilinger

et al. 2021

IEEE Trans. Intell. Veh.

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This paper presents a method for inverse learning of a control objective defined in terms of requirements and their joint probability distribution from data. The probability distribution characterizes tolerated deviations from the deterministic requirements and is learned using maximum likelihood estimation from data. Further, this paper introduces both parametrized requirements for motion planning in autonomous driving applications and methods for the estimation of their parameters from driving data. Both the parametrized requirements and their joint probability distributions are estimated using a posterior distribution such that the control objective is personalized from a prior as driver data are accumulated. Finally, three variants of the learning method are presented that vary in computational complexity and data storage requirements. Key advantages of the proposed inverse learning method are a relatively low computational complexity, a need for a limited amount of data, and that the data do not have to be segmented into specific maneuvers, which makes the method easily implementable. Learning results using data of five human drivers in a simulation environment suggest that the proposed model for human-conscious driving along with the proposed learning method enable a more natural and personalized driving style of autonomous vehicles for their human passengers.

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“…Therefore, (3) can not be solved a priori. Unlike [19][20][21][22] where y(t) is treated as a random variable, consequently making µ(t|t) itself a random variable, we approximate the optimization problem (3) with an approximate stochastic optimal control problem by using µ(t|0) instead of µ(t|t). Recall that µ(t|0) is computed using only the prediction step of the Kalman filter so that µ(t|0) is equivalent to the unconditional mean µ(t).…”

Section: The Approximate Stochastic Optimal Control Problemmentioning

confidence: 99%

“…The authors in [17,18] propose model predictive control frameworks that contain distributionally robust risk constraints that are based on ambiguity sets defined around an empirical state distribution from input-output data. On the other hand, sensor and sampling-based approaches that integrate Kalman or particle filters have been proposed in [19][20][21][22]. These works have in common to treat future output measurements as random variables via its output measurement map, i.e., the map from states to observations.…”

Section: Introductionmentioning

confidence: 99%

Robust Motion Planning in the Presence of Estimation Uncertainty

Lindemann¹,

Cleaveland²,

Kantaros³

et al. 2021

Preprint

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Motion planning is a fundamental problem and focuses on finding control inputs that enable a robot to reach a goal region while safely avoiding obstacles. However, in many situations, the state of the system may not be known but only estimated using, for instance, a Kalman filter. This results in a novel motion planning problem where safety must be ensured in the presence of state estimation uncertainty. Previous approaches to this problem are either conservative or integrate state estimates optimistically which leads to non-robust solutions. Optimistic solutions require frequent replanning to not endanger the safety of the system. We propose a new formulation to this problem with the aim to be robust to state estimation errors while not being overly conservative. In particular, we formulate a stochastic optimal control problem that contains robustified risk-aware safety constraints by incorporating robustness margins to account for state estimation errors. We propose a novel sampling-based approach that builds trees exploring the reachable space of Gaussian distributions that capture uncertainty both in state estimation and in future measurements. We provide robustness guarantees and show, both in theory and simulations, that the induced robustness margins constitute a trade-off between conservatism and robustness for planning under estimation uncertainty that allows to control the frequency of replanning.

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Particle filtering for online motion planning with task specifications

Cited by 17 publications

References 20 publications

Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning

Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning

Inverse Learning for Data-Driven Calibration of Model-Based Statistical Path Planning

Robust Motion Planning in the Presence of Estimation Uncertainty

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