A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments

• URL: http://arxiv.org/abs/2209.01482v1
• Date: Sat, 3 Sep 2022 19:13:16 GMT
• Title: A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments
• Authors: Yanrong Hu, Simon X. Yang
• Abstract summary: The proposed genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators. The proposed algorithm is capable of finding a near-optimal robot path in both static and dynamic complex environments.
• Score: 3.318708963153893
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In this paper, a novel knowledge-based genetic algorithm for path planning of a mobile robot in unstructured complex environments is proposed, where five problem-specific operators are developed for efficient robot path planning. The proposed genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators, some of which also combine a local search technique. A unique and simple representation of the robot path is proposed and a simple but effective path evaluation method is developed, where the collisions can be accurately detected and the quality of a robot path is well reflected. The proposed algorithm is capable of finding a near-optimal robot path in both static and dynamic complex environments. The effectiveness and efficiency of the proposed algorithm are demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed genetic algorithm for solving the robot path planning problem is demonstrated through a comparison study.

Related papers

• Simulation-Aided Policy Tuning for Black-Box Robot Learning [47.83474891747279]
  We present a novel black-box policy search algorithm focused on data-efficient policy improvements. The algorithm learns directly on the robot and treats simulation as an additional information source to speed up the learning process. We show fast and successful task learning on a robot manipulator with the aid of an imperfect simulator.
  arXiv  Detail & Related papers  (2024-11-21T15:52:23Z)
• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• Evolutionary Swarm Robotics: Dynamic Subgoal-Based Path Formation and Task Allocation for Exploration and Navigation in Unknown Environments [0.0]
  The paper presents a method called the sub-goal-based path formation, which establishes a path between two different locations by exploiting visually connected sub-goals. The paper tackles the problem of inter-collision (traffic) among a large number of robots engaged in path formation, which negatively impacts the performance of the sub-goal-based method. A task allocation strategy is proposed, leveraging local communication protocols and light signal-based communication.
  arXiv  Detail & Related papers  (2023-12-27T15:13:56Z)
• POA: Passable Obstacles Aware Path-planning Algorithm for Navigation of a Two-wheeled Robot in Highly Cluttered Environments [53.41594627336511]
  Passable Obstacles Aware (POA) planner is a novel navigation method for two-wheeled robots in a cluttered environment. Our algorithm allows two-wheeled robots to find a path through passable obstacles.
  arXiv  Detail & Related papers  (2023-07-16T19:44:27Z)
• Contribution \`a l'Optimisation d'un Comportement Collectif pour un Groupe de Robots Autonomes [0.0]
  This thesis studies the domain of collective robotics, and more particularly the optimization problems of multirobot systems. The first contribution is the use of the Butterfly Algorithm Optimization (BOA) to solve the Unknown Area Exploration problem. The second contribution is the development of a new simulation framework for benchmarking dynamic incremental problems in robotics.
  arXiv  Detail & Related papers  (2023-06-10T21:49:08Z)
• Systematic Comparison of Path Planning Algorithms using PathBench [55.335463666037086]
  Path planning is an essential component of mobile robotics. Development of learning-based path planning algorithms has been experiencing rapid growth. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future path planning algorithms.
  arXiv  Detail & Related papers  (2022-03-07T01:52:57Z)
• A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions [2.2452191187045383]
  In multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem. Standard gradient-descent-like algorithms are not applicable to these problems. We introduce a new algorithm that carefully designs each robot's subcost function, the optimization of which can accomplish the overall team objective.
  arXiv  Detail & Related papers  (2021-11-14T20:40:00Z)
• PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms [59.3879573040863]
  Path planning is a key component in mobile robotics. Few attempts have been made to benchmark the algorithms holistically or unify their interface. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future path planning algorithms.
  arXiv  Detail & Related papers  (2021-05-04T21:48:18Z)
• Sparsification for Fast Optimal Multi-Robot Path Planning in Lazy Compilation Schemes [7.766921168069532]
  Path planning for multiple robots (MRPP) represents a task of finding non-colliding paths for robots through which they can navigate from their initial positions to specified goal positions. In this paper, we enhance existing SAT-based algorithm for MRPP via spartification of the set of candidate paths for each robot from which target Boolean encoding is derived.
  arXiv  Detail & Related papers  (2021-03-08T00:57:42Z)
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms [60.59764170868101]
  Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. We formulate it as a few-shot meta-learning problem where the goal is to find a model that captures the common structure shared across different robotic platforms. We experimentally evaluate our framework on a simulated reaching and a real-robot picking task using 400 simulated robots.
  arXiv  Detail & Related papers  (2021-03-05T14:16:20Z)
• New Fusion Algorithm provides an alternative approach to Robotic Path planning [0.0]
  This paper presents a new and efficient fusion algorithm for solving the path planning problem in a custom 2D environment. The new fusion algorithm is feasible and superior in smoothness performance and can satisfy as a time-efficient and cheaper alternative to conventional A* strategies of path planning.
  arXiv  Detail & Related papers  (2020-06-06T17:52:00Z)

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.