NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain

• URL: http://arxiv.org/abs/2308.04792v3
• Date: Thu, 20 Jun 2024 13:35:10 GMT
• Title: NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain
• Authors: Yiming Ji, Yang Liu, Guanghu Xie, Boyu Ma, Zongwu Xie, Baoshi Cao,
• Abstract summary: We propose the NNPP model for computing the region, enabling foundation algorithms like Astar to find the optimal path solely within this reduced search space. The NNPP model learns information about start and goal locations, as well as map representations, from numerous pre-annotated optimal path demonstrations. It is able to textcolorrevisionaccelerate path planning on novel maps.
• Score: 5.337162499594818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent autonomous path planning is essential for enhancing the exploration efficiency of mobile robots operating in uneven terrains like planetary surfaces and off-road environments.In this paper, we propose the NNPP model for computing the heuristic region, enabling foundation algorithms like Astar to find the optimal path solely within this reduced search space, effectively decreasing the search time. The NNPP model learns semantic information about start and goal locations, as well as map representations, from numerous pre-annotated optimal path demonstrations, and produces a probabilistic distribution over each pixel representing the likelihood of it belonging to an optimal path on the map. More specifically, the paper computes the traversal cost for each grid cell from the slope, roughness and elevation difference obtained from the digital elevation model. Subsequently, the start and goal locations are encoded using a Gaussian distribution and different location encoding parameters are analyzed for their effect on model performance. After training, the NNPP model is able to \textcolor{revision}{accelerate} path planning on novel maps.

Related papers

• Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation [64.84996994779443]
  We propose a novel Affordances-Oriented Planner for continuous vision-language navigation (VLN) task. Our AO-Planner integrates various foundation models to achieve affordances-oriented low-level motion planning and high-level decision-making. Experiments on the challenging R2R-CE and RxR-CE datasets show that AO-Planner achieves state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2024-07-08T12:52:46Z)
• CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning [0.0]
  We propose a novel image-based learning algorithm (CBAGAN-RRT) using a Convolutional Block Attention Generative Adversarial Network. The probability distribution of the paths generated from our GAN model is used to guide the sampling process for the RRT algorithm. We train and test our network on the dataset generated by citezhang 2021 and demonstrate that our algorithm outperforms the previous state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-05-13T20:06:53Z)
• DDPEN: Trajectory Optimisation With Sub Goal Generation Model [70.36888514074022]
  In this paper, we produce a novel Differential Dynamic Programming with Escape Network (DDPEN) We propose to utilize a deep model that takes as an input map of the environment in the form of a costmap together with the desired position. The model produces possible future directions that will lead to the goal, avoiding local minima which is possible to run in real time conditions.
  arXiv  Detail & Related papers  (2023-01-18T11:02:06Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Generative Modeling with Optimal Transport Maps [83.59805931374197]
  Optimal Transport (OT) has become a powerful tool for large-scale generative modeling tasks. We show that the OT map itself can be used as a generative model, providing comparable performance.
  arXiv  Detail & Related papers  (2021-10-06T18:17:02Z)
• Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach [6.510061176722248]
  In many trajectory-based applications, it is necessary to map raw GPS trajectories onto road networks in digital maps. In this paper, we consider the map-matching task from the data perspective, proposing a deep learning-based map-matching model. We generate synthetic trajectory data to pre-train the Transformer model and then fine-tune the model with a limited number of ground-truth data.
  arXiv  Detail & Related papers  (2021-08-01T11:51:11Z)
• Learning Space Partitions for Path Planning [54.475949279050596]
  PlaLaM outperforms existing path planning methods in 2D navigation tasks, especially in the presence of difficult-to-escape local optima. These gains transfer to highly multimodal real-world tasks, where we outperform strong baselines in compiler phase ordering by up to 245% and in molecular design by up to 0.4 on properties on a 0-1 scale.
  arXiv  Detail & Related papers  (2021-06-19T18:06:11Z)
• Conditional Generative Adversarial Networks for Optimal Path Planning [30.892250698479064]
  We propose a novel learning-based path planning algorithm based on the conditional generative adversarial networks (CGAN) and a modified RRT* algorithm (denoted by CGANRRT*) The CGAN model is trained by learning from ground truth maps, each of which is generated by putting all the results of executing RRT algorithm 50 times on one raw map. We demonstrate the efficient performance of this CGAN model by testing it on two groups of maps and comparing CGAN-RRT* algorithm with conventional RRT* algorithm.
  arXiv  Detail & Related papers  (2020-12-06T02:53:50Z)
• Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories [0.5076419064097732]
  We propose a novel trajectory learning method that exploits motion trajectories on topological map using recurrent neural network. Inspired by human's ability to both be aware of distance and direction of self-motion in navigation, our trajectory learning method learns a pattern representation of trajectories encoded as a sequence of distances and turning angles to assist self-localization.
  arXiv  Detail & Related papers  (2020-10-13T02:08:45Z)
• Learning to plan with uncertain topological maps [14.774543742438176]
  We train an agent to navigate in 3D environments using a hierarchical strategy including a graph based planner and a local policy. By performing an empirical analysis of our method in simulated photo-realistic 3D environments, we demonstrate that the inclusion of visual features in the learned neural planner outperforms classical symbolic solutions for graph based planning.
  arXiv  Detail & Related papers  (2020-07-10T09:28:30Z)
• Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation [62.79160608266713]
  morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping. Existing grasp generation approaches are cursed to construct discontinuous grasp maps by aggregating annotations for drastically different orientations per grasping point. We propose a novel augmented grasp map representation, suitable for pixel-wise synthesis, that locally disentangles grasping orientations by partitioning the angle space into multiple bins.
  arXiv  Detail & Related papers  (2020-06-09T08:54:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.