Fast Local Planning and Mapping in Unknown Off-Road Terrain

Overbye, Timothy; Saripalli, Srikanth

doi:10.1109/icra40945.2020.9196848

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…Although these methods provide cost estimates for specific scenarios, they struggle to adapt to off-road environments with complex road conditions and the incorporation of human driving preferences. In contrast, Tian et al (2023) constructs a potential field cost map based on obstacles and risks, while Overbye and Saripalli (2020) creates four temporary maps and produces a cost map through weighted averaging. Both approaches combine costs in a weighted manner to generate a single-value cost map; however, they fail to consider the relationships between costs and road conditions, as well as the relationships between different costs.…”

Section: Off-road Environment Processingmentioning

confidence: 99%

“…However, these single feature maps were not able to fully express various terrains such as undulations, roughness, ramps, and others in off-road scenarios. Overbye and Saripalli (2020) proposed to construct four types of feature maps for the environment and combine them with specific weights into one cost map. However, the combined map usually couples different features with an equal weights and fails to adapt to different types of terrain with different driving preference.…”

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confidence: 99%

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Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

Wang,

Gong,

Gong

et al. 2024

Journal of Field Robotics

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Driving in an off‐road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human‐like cognition and cost evaluation for off‐road driving. First, we construct a multilayer map describing different features of off‐road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network‐long short‐term memory network to learn the trajectories planned by human drivers in various off‐road scenarios. Then, based on human‐like generated trajectories in different environments, we design a primitive‐based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off‐road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off‐road environment with complex terrain and varying road conditions. The experimental results show that the proposed human‐like motion planner has excellent adaptability to different off‐road conditions. It shows real‐time operation, greater stability, and more human‐like planning ability in diverse and challenging scenarios.

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Section: Off-road Environment Processingmentioning

confidence: 99%

mentioning

confidence: 99%

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

Wang,

Gong,

Gong

et al. 2024

Journal of Field Robotics

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show abstract

“…The elevation map, created by Lidar point clouds [25], [26], has been used to compute the geometrical features of the 3D surface [27], [28]. Using geometry-based traversability cost, local and global navigation algorithms have been proposed in [19], [29], [30]. However, such methods disregard the vehicleterrain interactions derived from the terrain conditions, such as friction coefficients.…”

Section: B Vehicle Navigation In View Of Traversabilitymentioning

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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“…The cost map process is a continuation on the methods developed in [23]. A lidar scan is taken over a local grid map.…”

Section: A Cost Map Creationmentioning

confidence: 99%

Path Optimization for Ground Vehicles in Off-Road Terrain

Overbye¹,

Saripalli²

2021

Preprint

Self Cite

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We present a method for path optimization for ground vehicles in off-road environments at high speeds. This path optimization considers the kinematic constraints of the vehicle. By thinking in the actuator space we can represent these constraints as limits in the space rather than derived properties of the path. In this paper we present a actuator space approach to path optimization for off-road ground vehicles. This is done by representing and operation on the path as a list of steering angles over the path length. This transforms the set of kinematic constraints into constraints on the steering angle. We then put this path into a gradient descent solver. This produced paths that are kinematically feasible and optimized in accordance with our cost function. Finally, we tested the system both in simulation and on an off-road vehicle at speeds of 5 m/s.

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Fast Local Planning and Mapping in Unknown Off-Road Terrain

Cited by 15 publications

References 28 publications

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

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

Path Optimization for Ground Vehicles in Off-Road Terrain

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