Energy-Aware Path Planning for Skid-Steer Robots Operating on Hilly Terrain

Gruning, Veronica; Pentzer, Jesse; Brennan, Sean; Reichard, Karl

doi:10.23919/acc45564.2020.9147470

Cited by 15 publications

(12 citation statements)

References 10 publications

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“…However, obtaining such a heuristic is non-trivial for most of the applications. Practically, the heuristic function is often devised, in energy-aware path planning problems, to represent a marginal underestimation of the true cost [15] [16]. However, as in our application the vehicle moves over unknown terrains, the energy model for the true cost can constantly change.…”

Section: Methodology -Energy-aware Path Plannermentioning

confidence: 99%

“…Within this framework, terrain classifiers can be used to identify the type of terrain, while sensors such as LIDAR or stereo vision provide the geometric information. Hence, different energy models can be adopted, such as semi-empirical functions [15] [16] [17], look-up tables [18], or neural networks [19] [20] to link the terrain geometry to the energy consumption for each terrain type. The main drawback of these methods is that they cannot account for terrains with unknown properties.…”

Section: Related Workmentioning

confidence: 99%

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Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

Visca¹

2022

Preprint

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This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties. A novel feature of the method is the use of a deep meta-learning framework to learn a prior energy model, which can efficiently adapt to the local terrain conditions based on small quantities of exteroceptive and proprioceptive data. A meta-adaptive heuristic function is also proposed for the integration of the energy model into an A* path planner. The performance of the proposed approach is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains, and compared with alternative machine learning solutions. We provide evidence of the advantages of the proposed method to adapt to unforeseen terrain conditions, thereby yielding more informed estimations and energy-efficient paths, when navigating on unknown terrains.<div>Submitted for revision to IEEE Transaction on Cybernetics.</div>

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Section: Methodology -Energy-aware Path Plannermentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

Visca¹

2022

Preprint

View full text Add to dashboard Cite

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“…However, obtaining such a heuristic is non-trivial for most of the applications. Practically, the heuristic function is often devised, in energy-aware path planning problems, to represent a marginal underestimation of the true cost [19] [20]. However, as in our application the vehicle moves over unknown terrains, the energy model for the true cost can constantly change.…”

Section: ) Heuristic Functionmentioning

confidence: 99%

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

et al. 2022

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“…Moreover, it can also influence the energetic performance of the robot according to its direction. This dependency on direction is due to the effect of gravity, making the robot consume different amounts of energy according to whether it is climbing, descending, going laterally or going diagonally through the slope [ 29 , 54 , 55 , 56 , 57 ]. Another relevant terrain feature is the roughness.…”

Section: Path Planning Algorithmsmentioning

confidence: 99%

“…Ichter et al [ 199 ] propose the use of Group Marching Tree (GMT*), a similar algorithm to FMT* but which focuses on speeding up computation via parallelization using GPUs. Finally, it is worth mentioning there are path planning algorithms that combine the Dynamic Sampling approach with Model Predictive Control (MPC) techniques to account for kinodynamic constraints [ 57 , 200 , 201 ].…”

Section: C-space-search-based Path Planning Algorithmsmentioning

confidence: 99%

Path Planning for Autonomous Mobile Robots: A Review

Sánchez‐Ibáñez

Pérez-del-Pulgar

García-Cerezo

2021

Sensors

199

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Providing mobile robots with autonomous capabilities is advantageous. It allows one to dispense with the intervention of human operators, which may prove beneficial in economic and safety terms. Autonomy requires, in most cases, the use of path planners that enable the robot to deliberate about how to move from its location at one moment to another. Looking for the most appropriate path planning algorithm according to the requirements imposed by users can be challenging, given the overwhelming number of approaches that exist in the literature. Moreover, the past review works analyzed here cover only some of these approaches, missing important ones. For this reason, our paper aims to serve as a starting point for a clear and comprehensive overview of the research to date. It introduces a global classification of path planning algorithms, with a focus on those approaches used along with autonomous ground vehicles, but is also extendable to other robots moving on surfaces, such as autonomous boats. Moreover, the models used to represent the environment, together with the robot mobility and dynamics, are also addressed from the perspective of path planning. Each of the path planning categories presented in the classification is disclosed and analyzed, and a discussion about their applicability is added at the end.

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Energy-Aware Path Planning for Skid-Steer Robots Operating on Hilly Terrain

Cited by 15 publications

References 10 publications

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

Path Planning for Autonomous Mobile Robots: A Review

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