Integrated Mapping and Path Planning for Very Large-Scale Robotic (VLSR) Systems

Morelli, Julian; Zhu, Pingping; Doerr, Bryce; Linares, Richard; Ferrari, Silvia

doi:10.1109/icra.2019.8793795

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Since the Hilbert occupancy map learning is not the key contribution, its implementation details are omitted in this paper. The interested readers are referred to [28], [29] for more details. Based on the updated Hilbert occupancy map h(x, t), the obstacle map function is defined as a binary function, such that…”

Section: Background On Occupancy Mappingmentioning

confidence: 99%

Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems

Zhu

Liu

Ferrari

2020

Preprint

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This paper presents an adaptive online distributed optimal control approach that is applicable to optimal planning for very-large-scale robotics systems in highly uncertain environments. This approach is developed based on the optimal mass transport theory. It is also viewed as an online reinforcement learning and approximate dynamic programming approach in the Wasserstein-GMM space, where a novel value functional is defined based on the probability density functions of robots and the time-varying obstacle map functions describing the changing environmental information. The proposed approach is demonstrated on the path planning problem of very-largescale robotic systems where the approximated layout of obstacles in the workspace is incrementally updated by the observations of robots, and compared with some existing state-of-the-art approaches. The numerical simulation results show that the proposed approach outperforms these approaches in aspects of the average traveling distance and the energy cost.

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Section: Background On Occupancy Mappingmentioning

confidence: 99%

Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems

Zhu

Liu

Ferrari

2020

Preprint

Self Cite

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Section: Introductionmentioning

confidence: 99%

“…The problem of field estimation and deployment has also been studied in recent works [11], [7], [8], [12]. In [8], [12], the authors also consider a field estimation problem followed by an optimal control formulation for swarm deployment. However, the numerical solutions are usually open-loop and have robustness issues due to environmental uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Field Estimation using Robotic Swarms through Bayesian Regression and Mean-Field Feedback

Zheng¹,

Lin²

2021

Preprint

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Recent years have seen an increased interest in using mean-field density based modelling and control strategy for deploying robotic swarms. In this paper, we study how to dynamically deploy the robots subject to their physical constraints to efficiently measure and reconstruct certain unknown spatial field (e.g. the air pollution index over a city). Specifically, the evolution of the robots' density is modelled by mean-field partial differential equations (PDEs) which are uniquely determined by the robots' individual dynamics. Bayesian regression models are used to obtain predictions and return a variance function that represents the confidence of the prediction. We formulate a PDE constrained optimization problem based on this variance function to dynamically generate a reference density signal which guides the robots to uncertain areas to collect new data, and design mean-field feedback-based control laws such that the robots' density converges to this reference signal. We also show that the proposed feedback law is robust to density estimation errors in the sense of input-to-state stability. Simulations are included to verify the effectiveness of the algorithms.

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