COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields

Abstract

This paper presents a real-time and deterministic path planning method for autonomous ships or Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. A modified Artificial Potential Field (APF), which contains a new modified repulsion potential field function and the corresponding virtual forces, is developed to address the issue of Collision Avoidance (CA) with dynamic targets and static obstacles, including emergency situations. Appropriate functional and safety requirements are added in the corresponding virtual forces to ensure International Regulations for Preventing Collisions at Sea (COLREGS)-constrained behaviour for the own ship's CA actions. Simulations show that the method is fast, effective and deterministic for path planning in complex situations with multiple moving target ships and stationary obstacles and can account for the unpredictable strategies of other ships. The authors believe that automatic navigation systems operated without human interaction could benefit from the development of path planning algorithms.

Multi-measure estimation of navigation safety for marine area

“…Второй аспект связан с движением по локальной акватории в условиях интенсивных разнонаправленных судопотоков. В этом случае судоводителями дополнительно решаются задачи планирования пути, в том числе с учётом ограничений, накладываемых определённой схемой движения [19].…”

Fast Path Planning for Autonomous Ships in Restricted Waters

“…The CA decision making for autonomous ships is a complex system that is constrained by COLREGS and should cope with the dynamic environment in real time. The authors have done some work in this field [13]. By considering Rules 13~17 of COLREGS, the CA action scope is divided into four regions, safe zone, negotiation CA zone, emergency CA zone and prohibited zone, according to the checking criterion of collision risk and the checking range (CR) of collision risk.…”

“…No emergency scenarios or unforeseen circumstances are considered. Lyu's method solves the above problems, but it is limited to avoiding point obstacles and cannot deal with irregular linear and surface obstacles [13]. Liu et al [14] achieved CA with static obstacles by constructing an adaptive artificial repulsive force field in a self-organizing map (SOM).…”

Multistage Dynamic Optimization with Different Forms of Neural-State Constraints to Avoid Many Object Collisions Based on Radar Remote Sensing

“…In practice, there are many possible safe collision-avoidance maneuvers; ideally, the navigator chooses the optimal maneuver, which is the one that, in addition to minimizing the collision risk, results in the smallest deviation from the original path. The possibilities for supplementing the system with appropriate methods to support maneuvering decisions taken in an uncertain navigational situation that occurs in a short time in relation to the greater number of objects encountered are described in [7][8][9][10][11][12]. The process of reducing the uncertainty when assessing the real navigational situation of an object by using an artificial neural network is shown in [13][14][15].…”