CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning

• URL: http://arxiv.org/abs/2305.10442v3
• Date: Sun, 20 Jul 2025 23:36:32 GMT
• Title: CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning
• Authors: Abhinav Sagar, Sai Teja Gilukara,
• Abstract summary: We propose a novel image-based learning algorithm using a Convolutional Block Attention Generative Adrial Network (CBAGAN-RRT)<n>Our algorithm outperforms the previous state-of-the-art algorithms using both the image quality generation metrics and path planning metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sampling-based path planning algorithms play an important role in autonomous robotics. However, a common problem among these algorithms is that the initial path generated is not optimal, and the convergence is too slow for real-world applications. In this paper, we propose a novel image-based learning algorithm using a Convolutional Block Attention Generative Adversarial Network (CBAGAN-RRT) with a combination of spatial and channel attention and a novel loss function to design the heuristics, find a better optimal path, and improve the convergence of the algorithm, both concerning time and speed. The probability distribution of the paths generated from our GAN model is used to guide the sampling process for the RRT algorithm. We demonstrate that our algorithm outperforms the previous state-of-the-art algorithms using both the image quality generation metrics, like IOU Score, Dice Score, FID score, and path planning metrics like time cost and the number of nodes. Ablation studies show the effectiveness of various components in our network architecture. The advantage of our approach is that we can avoid the complicated preprocessing in the state space, our model can be generalized to complex environments like those containing turns and narrow passages without loss of accuracy, and our model can be easily integrated with other sampling-based path planning algorithms.

Related papers

• Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces [0.4143603294943439]
  We present a novel method for accelerating path-planning tasks in unknown scenes with obstacles. We approximate the distribution of waypoints for a collision-free path using the Rapidly-exploring Random Tree algorithm. Our experiments demonstrate promising results in accelerating path-planning tasks under critical time constraints.
  arXiv  Detail & Related papers  (2025-01-11T21:14:52Z)
• Neural Algorithmic Reasoning Without Intermediate Supervision [21.852775399735005]
  We focus on learning neural algorithmic reasoning only from the input-output pairs without appealing to the intermediate supervision. We build a self-supervised objective that can regularise intermediate computations of the model without access to the algorithm trajectory. We demonstrate that our approach is competitive to its trajectory-supervised counterpart on tasks from the CLRSic Algorithmic Reasoning Benchmark.
  arXiv  Detail & Related papers  (2023-06-23T09:57:44Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• Stochastic Ratios Tracking Algorithm for Large Scale Machine Learning Problems [0.7614628596146599]
  We propose a novel algorithm for adaptive step length selection in the classical SGD framework. Under reasonable conditions, the algorithm produces step lengths in line with well-established theoretical requirements. We show that the algorithm can generate step lengths comparable to the best step length obtained from manual tuning.
  arXiv  Detail & Related papers  (2023-05-17T06:22:11Z)
• Pathfinding Neural Cellular Automata [23.831530224401575]
  Pathfinding is an important sub-component of a broad range of complex AI tasks, such as robot path planning, transport routing, and game playing. We hand-code and learn models for Breadth-First Search (BFS), i.e. shortest path finding. We present a neural implementation of Depth-First Search (DFS), and outline how it can be combined with neural BFS to produce an NCA for computing diameter of a graph. We experiment with architectural modifications inspired by these hand-coded NCAs, training networks from scratch to solve the diameter problem on grid mazes while exhibiting strong ability generalization
  arXiv  Detail & Related papers  (2023-01-17T11:45:51Z)
• Entropic Neural Optimal Transport via Diffusion Processes [105.34822201378763]
  We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between continuous probability distributions. Our algorithm is based on the saddle point reformulation of the dynamic version of EOT which is known as the Schr"odinger Bridge problem. In contrast to the prior methods for large-scale EOT, our algorithm is end-to-end and consists of a single learning step.
  arXiv  Detail & Related papers  (2022-11-02T14:35:13Z)
• Fidelity-Guarantee Entanglement Routing in Quantum Networks [64.49733801962198]
  Entanglement routing establishes remote entanglement connection between two arbitrary nodes. We propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (SD) pairs in quantum networks.
  arXiv  Detail & Related papers  (2021-11-15T14:07:22Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Generative Adversarial Network based Heuristics for Sampling-based Path Planning [34.368519009432426]
  We present a novel image-based path planning algorithm to overcome limitations of sampling-based path planning. Specifically, a generative adversarial network (GAN) is designed to take the environment map as the input without other preprocessing works. We conduct a number of simulation experiments to validate the effectiveness of the proposed method, and the results demonstrate that our method performs much better in terms of the quality of initial solution and the convergence speed to the optimal solution.
  arXiv  Detail & Related papers  (2020-12-07T07:29:57Z)
• 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)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data [59.50904660420082]
  Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model.
  arXiv  Detail & Related papers  (2020-05-22T23:07:42Z)
• Channel Assignment in Uplink Wireless Communication using Machine Learning Approach [54.012791474906514]
  This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. Due to high computational complexity, machine learning approaches are employed to obtain computational efficient solutions.
  arXiv  Detail & Related papers  (2020-01-12T15:54:20Z)

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.