A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios

Rastgoftar, Hossein; Zhang, Bingxin; Atkins, Ella M.

doi:10.23919/acc.2018.8431069

Cited by 7 publications

(3 citation statements)

References 27 publications

(33 reference statements)

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“…However, it is often impossible to get an explicit analytical solution from Eqn. (5) for the hyperparameters of the time series model in Eqn. ( 1) and (2).…”

Section: Multi-robot Computational Trustworthiness Modelmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Optimization Based Trustworthiness Model for Multi-robot Bounding Overwatch

Zheng¹,

Smereka²,

Mikluski³

et al. 2022

Preprint

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In multi-robot system (MRS) bounding overwatch, it is crucial to determine which point to choose for overwatch at each step and whether the robots' positions are trustworthy so that the overwatch can be performed effectively. In this paper, we develop a Bayesian optimization based computational trustworthiness model (CTM) for the MRS to select overwatch points. The CTM can provide real-time trustworthiness evaluation for the MRS on the overwatch points by referring to the robots' situational awareness information, such as traversability and line of sight. The evaluation can quantify each robot's trustworthiness in protecting its robot team members during the bounding overwatch. The trustworthiness evaluation can generate a dynamic cost map for each robot in the workspace and help obtain the most trustworthy bounding overwatch path. Our proposed Bayesian based CTM and motion planning can reduce the number of explorations for the workspace in data collection and improve the CTM learning efficiency. It also enables the MRS to deal with the dynamic and uncertain environments for the multi-robot bounding overwatch task. A robot simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the proposed framework.

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“…However, it is often impossible to get an explicit analytical solution from Eqn. (5) for the hyperparameters of the time series model in Eqn. ( 1) and (2).…”

Section: Multi-robot Computational Trustworthiness Modelmentioning

confidence: 99%

“…Therefore, the bounding overwatch path planning problem can be generalized to an outdoor path planning problem. Some outdoor path planning works deal with the robot traversability problem by using geological information [5,[7][8][9]. These model a cost map or a cost function with the geological terrain height information.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization Based Trustworthiness Model for Multi-robot Bounding Overwatch

Zheng¹,

Smereka²,

Mikluski³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…They applied GP model to interpolate the local paths computed from each locally fitted plane to make a denser path. In [8], [20], safe navigation areas have been classified based on the labeled 3D point cloud map given by the Lidar sensor. However, such a binary division cannot preclude dynamically infeasible paths or collisions due to the complex vehicle interaction with 3D terrain topology.…”

Section: A Vehicle Navigation Over 3d Terrainsmentioning

confidence: 99%

Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains

Lee¹,

Kwon²,

Kwon³

2022

Preprint

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This paper presents a safe, efficient, and agile ground vehicle navigation algorithm for 3D off-road terrain environments. Off-road navigation is subject to uncertain vehicleterrain interactions caused by different terrain conditions on top of 3D terrain topology. The existing works are limited to adopt overly simplified vehicle-terrain models. The proposed algorithm learns the terrain-induced uncertainties from driving data and encodes the learned uncertainty distribution into the traversability cost for path evaluation. The navigation path is then designed to optimize the uncertainty-aware traversability cost, resulting in a safe and agile vehicle maneuver. Assuring real-time execution, the algorithm is further implemented within parallel computation architecture running on Graphics Processing Units (GPU).

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Bayesian Optimization Based Trust Model for Human Multi-robot Collaborative Motion Tasks in Offroad Environments

Zheng

Smereka

Mikulski

et al. 2023

Int J of Soc Robotics

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A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios

Cited by 7 publications

References 27 publications

Bayesian Optimization Based Trustworthiness Model for Multi-robot Bounding Overwatch

Bayesian Optimization Based Trustworthiness Model for Multi-robot Bounding Overwatch

Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains

Bayesian Optimization Based Trust Model for Human Multi-robot Collaborative Motion Tasks in Offroad Environments

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