Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning

要約

タイトル：ダイナミックシステムベースの経路計画と教師なし学習を組み合わせた現実環境の自律検索

要約：
– 現在、経路計画にカオス性を取り入れた自律検索に関する研究が進んでいるが、実験的研究は限られており、実用的な問題に対処できるロバストな手法の開発が必要である。
– 実用的な問題とは以下の３つである：（1）ロボットの運動学的効率を損なわずに障害物を回避する手法、（2）被覆すべき大きく複雑な環境に対してカオス的な経路を拡散する手法、（3）セルサイズに依存しない正確なリアルタイムの被覆計算手法。
– この論文では、これらの問題に対処するアルゴリズムを提案し、ROSフレームワークを用いた新しく開発されたカオス経路計画アプリケーションを示している。
– このアプリケーションは、様々なサイズ、形状、障害物密度の環境で実験を行い、従来の最適経路プランナーと同等のパフォーマンスを示した。また、実環境とGazeboシミュレーションでのテストも行われた。

要約(オリジナル)

In recent years, advancements have been made towards the goal of using chaotic coverage path planners for autonomous search and traversal of spaces with limited environmental cues. However, the state of this field is still in its infancy as there has been little experimental work done. Current experimental work has not developed robust methods to satisfactorily address the immediate set of problems a chaotic coverage path planner needs to overcome in order to scan realistic environments within reasonable coverage times. These immediate problems are as follows: (1) an obstacle avoidance technique which generally maintains the kinematic efficiency of the robot’s motion, (2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and (3) a real-time coverage calculation technique that is accurate and independent of cell size. This paper aims to progress the field by proposing algorithms that address all of these problems by providing techniques for obstacle avoidance, chaotic trajectory dispersal, and accurate coverage calculation. The algorithms produce generally smooth chaotic trajectories and provide high scanning coverage of environments. These algorithms were created within the ROS framework and make up a newly developed chaotic path planning application. The performance of this application was comparable to that of a conventional optimal path planner. The performance tests were carried out in environments of various sizes, shapes, and obstacle densities, both in real-life and Gazebo simulations.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI