Event-Based Path-Planning and Path-Following in Unknown Environments for Underactuated Autonomous Underwater Vehicles

Abstract

The paper addresses path planning and path-following problems in an unknown complex environment for an underactuated autonomous underwater vehicle (AUV). The AUV is required to follow a given reference path represented as a sequence of smoothly joined lines and arcs, bypassing obstacles encountered on the path. A two-level control system is proposed with an upper level for event-driven path planning and a lower level for path-following. A discrete event system is designed to identify situations that require planning a new path. An improved waypoint guidance algorithm and a Dubins curves based algorithm are proposed to build paths that allow the AUV to avoid collision with obstacles and to return to the reference path respectively. Both algorithms generate paths that meet the minimum turning radius constraint. A robust parameter-varying controller is designed using sublinear vector Lyapunov functions to solve the path-following problem. The performance of the developed event-based control system is demonstrated in three different simulation scenarios: with a sharp-edged obstacle, with a U-shaped obstacle, and with densely scattered obstacles. The proposed scheme does not require significant computing resources and allows for easy implementation on board.

On the application of the calculus of positively constructed formulas for the study of controlled discrete-event systems

“…В качестве примера рассмотрим ДСС, описывающую задачу планирования пути автономного необитаемого подводного аппарата (АНПА) в неизвестной среде [50]. Предположим, что АН-ПА должен следовать по заданному эталонному пути, покидая его во избежание столкновения с встреченными препятствиями и возвращаясь к нему после выполнения маневров уклонения (рисунок 11).…”

Analysis and Control of Partially Observed Discrete-Event Systems via Positively Constructed Formulas

“…As an example, we consider the problem of robot's path planning in an unknown environment. We use the model from [41,42] simplified in the part of the path-following controller and extended by additional states and events, including unobservable ones.…”

“…Let I L ≜ {i : α i ≤ 0} and I R ≜ {i : α i > 0} be the sets of left and right beams of the FLS, respectively. For each obstacle point Q i , i = 1, N b , we define the maximum turning radius [41,44] as…”

“…The main component of the proposed approach is DES G msn (Figure 8), which is designed to detect situations that require updating the current path. Since, unlike [41], we do not employ a path-following controller to drive the robot along the generated paths, a situation may happen in which the robot finds itself in the wrong position or faced with some obstacle. State WPD corresponds to such a situation, and an unobservable event eER denotes transitions to this state.…”

“…Changing the DES state entails building a new path for the robot. Entering states DOL or DOR generates a path to avoid the detected obstacle using a modification [41] of the waypoint guidance algorithm [44]. Entering state NRP constructs a path that connects the robot's current position with the reference path for what the path planning algorithm from [41] is used, which is based on Dubins curves [45].…”