VikPath: A Vision Kansformer Framework for Effective Obstacle Avoidance in Self-Supervised Pathfinding

• URL: http://arxiv.org/abs/2608.22675v1
• Date: Mon, 24 Aug 2026 00:23:57 GMT
• Title: VikPath: A Vision Kansformer Framework for Effective Obstacle Avoidance in Self-Supervised Pathfinding
• Abstract summary: We propose a self-supervised pathfinding framework that considers obstacle proximity and path smoothness.<n>$Design$ achieves an average of 3.28% greater obstacle clearance and 87.07% lower latency while maintaining smooth path generation.
• Score: 12.76109113310209
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Pathfinding is a fundamental problem in artificial intelligence and autonomous systems. Traditional heuristic-based algorithms, such as A*, rely on predefined heuristic functions to guide the search process. Although effective in structured environments, their search efficiency can degrade substantially in complex, obstacle-rich scenarios, where handcrafted heuristics may provide limited guidance. Recent studies have explored learning-based approaches to improve pathfinding efficiency; however, most existing methods rely on supervised learning and require labels generated by conventional planners or obtained through manual annotation. As a result, their performance is inherently influenced by the quality of the underlying supervision and may degrade when the labeling heuristics fail to capture complex environmental structures. Moreover, existing methods primarily optimize for path length while paying limited attention to obstacle clearance and trajectory smoothness, which can lead to paths that are difficult or unsafe to execute in real-world environments. To address these limitations, we propose $\Design$, a self-supervised pathfinding framework that jointly considers obstacle proximity and path smoothness. At its core, our novel \textit{Vision Kansformer} module learns representations of obstacle distributions without relying on labeled trajectories, enabling the model to better adapt to complex environments. We further introduce a sharp-turn penalty to encourage smoother and more practically executable paths. Extensive experiments demonstrate that, compared with state-of-the-art (SOTA) approaches, $\Design$ achieves an average of 3.28\% greater obstacle clearance and 87.07\% lower inference latency while maintaining smooth path generation.

Related papers

• AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations [103.61864552260316]
  Real-time navigation in dynamic environments requires collision-free and dynamically feasible motion under limited perception.<n>We formulate navigation as learning a transferable goal-aligned path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations.<n>We develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning.
  arXiv  Detail & Related papers  (2026-07-13T16:00:41Z)
• FlexPath: Learned Semantic Path Priors for Image-Based Planning [2.739231894601598]
  We introduce FlexPath, a two-stage framework that decouples feasibility from preference.<n>For shortest-path planning, FlexPath reduces search effort on TMP by 14.3% compared to the state-of-the-art TransPath.<n>For obstacle clearance with minimum clearance distance 2, it achieves 96.8% full obstacle avoidance while maintaining low search cost.
  arXiv  Detail & Related papers  (2026-06-08T20:53:11Z)
• Towards Efficient Constraint Handling in Neural Solvers for Routing Problems [53.35866378109893]
  We present Construct-and-Refine, the first general and efficient constraint-handling framework for neural routing solvers.<n>CaR achieves superior feasibility, solution quality, and efficiency compared to both classical and neural state-of-the-art solvers.
  arXiv  Detail & Related papers  (2026-02-17T21:06:23Z)
• StruProKGR: A Structural and Probabilistic Framework for Sparse Knowledge Graph Reasoning [68.58655814341996]
  Sparse Knowledge Graphs (KGs) are commonly encountered in real-world applications, where knowledge is often incomplete or limited.<n>We propose a Structural and Probabilistic framework named StruProKGR, tailored for efficient and interpretable reasoning on sparse KGs.
  arXiv  Detail & Related papers  (2025-12-14T09:36:58Z)
• VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation [4.022717732460524]
  We propose Vision Language Model RRT (VLM-RRT), a hybrid approach that integrates the pattern recognition capabilities of Vision Language Models (VLMs) with the path-planning strengths of Rapidly-exploring Random Trees (RRT)<n>Our method biases sampling toward regions more likely to contain feasible paths, significantly improving sampling efficiency and path quality.
  arXiv  Detail & Related papers  (2025-05-29T09:15:44Z)
• LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning [91.95362946266577]
  Path planning is a fundamental scientific problem in robotics and autonomous navigation.<n>Traditional algorithms like A* and its variants are capable of ensuring path validity but suffer from significant computational and memory inefficiencies as the state space grows.<n>We propose a new LLM based route planning method that synergistically combines the precise pathfinding capabilities of A* with the global reasoning capability of LLMs.<n>This hybrid approach aims to enhance pathfinding efficiency in terms of time and space complexity while maintaining the integrity of path validity, especially in large-scale scenarios.
  arXiv  Detail & Related papers  (2024-06-20T01:24:30Z)
• 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)
• Path Choice Matters for Clear Attribution in Path Methods [84.29092710376217]
  We introduce textbfConcentration Principle, which allocates high attributions to indispensable features. We then present textbfSAMP, a model-agnostic interpreter, which efficiently searches the near-optimal path. We also propose the infinitesimal constraint (IC) and momentum strategy (MS) to improve the rigorousness and optimality.
  arXiv  Detail & Related papers  (2024-01-19T01:11:44Z)
• Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning [17.69984142788365]
  Coverage path planning ( CPP) is the problem of finding a path that covers the entire free space of a confined area. We investigate how suitable reinforcement learning is for this challenging problem. We propose a computationally feasible egocentric map representation based on frontiers, and a novel reward term based on total variation.
  arXiv  Detail & Related papers  (2023-06-29T14:32:06Z)
• Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning [0.0]
  This study develops a novel applied framework for real-world applications of chaotic coverage path planners.<n>It provides techniques for effective obstacle avoidance, chaotic trajectory dispersal, and accurate real-time coverage calculation.<n>The performance of this application was comparable to that of a conventional optimal path planner.
  arXiv  Detail & Related papers  (2023-05-03T00:09:31Z)
• Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path [15.679210057474922]
  We train a deep convolutional network that can predict collision-free paths based on a map of the environment. This is then used by a reinforcement learning algorithm to learn to closely follow the path. We show that our method consistently improves the sample efficiency and generalization capability to novel environments.
  arXiv  Detail & Related papers  (2020-03-03T17:07:47Z)

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.