Vertex-based Networks to Accelerate Path Planning Algorithms

• URL: http://arxiv.org/abs/2307.07059v1
• Date: Thu, 13 Jul 2023 20:56:46 GMT
• Title: Vertex-based Networks to Accelerate Path Planning Algorithms
• Authors: Yuanhang Zhang and Jundong Liu
• Abstract summary: We propose the utilization of vertices-based networks to enhance the sampling process of RRT*, leading to more efficient path planning. We employ focal loss to address the associated data imbalance issue, and explore different masking configurations to determine practical tradeoffs in system performance.
• Score: 3.684936338492373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Path planning plays a crucial role in various autonomy applications, and RRT* is one of the leading solutions in this field. In this paper, we propose the utilization of vertex-based networks to enhance the sampling process of RRT*, leading to more efficient path planning. Our approach focuses on critical vertices along the optimal paths, which provide essential yet sparser abstractions of the paths. We employ focal loss to address the associated data imbalance issue, and explore different masking configurations to determine practical tradeoffs in system performance. Through experiments conducted on randomly generated floor maps, our solutions demonstrate significant speed improvements, achieving over a 400% enhancement compared to the baseline model.

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)
• SCoTT: Wireless-Aware Path Planning with Vision Language Models and Strategic Chains-of-Thought [78.53885607559958]
  A novel approach using vision language models (VLMs) is proposed for enabling path planning in complex wireless-aware environments. To this end, insights from a digital twin with real-world wireless ray tracing data are explored. Results show that SCoTT achieves very close average path gains compared to DP-WA* while at the same time yielding consistently shorter path lengths.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem [67.92544792239086]
  We introduce an NN-based solver to significantly narrow the gap with advanced metaheuristics. First, we propose direction-aware facilitating attention model (DaAM) to incorporate directionality into the embedding process. Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy.
  arXiv  Detail & Related papers  (2024-03-11T02:17:42Z)
• 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)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning [18.586260468459386]
  We propose a data-driven algorithm that does the path selection in the network considering the overhead of route computation and path updates. Our scheme fares well by a factor of 40% with respect to reducing link utilization compared to traditional TE schemes such as ECMP.
  arXiv  Detail & Related papers  (2022-12-21T16:08:47Z)
• 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)
• Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization [79.96177511319713]
  We invoke deep learning (DL) to assist routing in aeronautical ad-hoc networks (AANETs) A deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. We extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.
  arXiv  Detail & Related papers  (2021-10-28T14:18:22Z)
• 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)
• Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks [151.27147513363502]
  This paper studies the problem of the trajectory design for a group of energyconstrained drones operating in dynamic wireless network environments. A value based reinforcement learning (VDRL) solution and a metatraining mechanism is proposed.
  arXiv  Detail & Related papers  (2020-12-06T01:30:12Z)

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.