Integrated automated driving system for maritime autonomous surface ship (MASS)

Patent metadata

Field	Value
Publication number	US-11770270-B2
Application number	US-202017034809-A
Country	US
Kind code	B2
Filing date	Sep 28, 2020
Priority date	Oct 18, 2019
Publication date	Sep 26, 2023
Grant date	Sep 26, 2023

Abstract

The present invention discloses an integrated automated driving system for a maritime autonomous surface ship (MASS). The integrated automated driving system for a MASS includes a perception module for perceiving navigational environment of a MASS and obtaining real-time dynamic information of a navigation channel, hydrology, a state of the MASS and traffic environment; a communication system for transmitting data and instructions between the MASS and a shore base as well as between system modules; a data processing module for processing information obtained by the perception module; a decision-making module for identifying a current operating status of the MASS and environment according to data outputted by the data processing module, selecting actions to be taken, and generating operating instructions corresponding to the action; and an execution module for receiving operating instructions sent from the decision-making module and controlling a propeller and a rudder of the MASS through a proportional-integral-derivative (PID) controller.

First claim

What is claimed is:

1. An integrated automated driving system for a maritime autonomous surface ship (MASS), comprising:
a perception device configured to perceive a navigational environment of the MASS and obtain real-time dynamic information of a navigation channel, hydrology, a state or a traffic environment of the MASS;
a communication device coupled to the perception device;
a processor configured to:
process information obtained by the perception device;
identify a current operating status of the MASS and environment according to the processed information
select actions to be taken,
generate operating instructions corresponding to the action based on a deep deterministic policy gradient (DDPG) algorithm, wherein a reward function in the DDPG algorithm comprises a path tracing reward function and a collision avoidance reward function, and
control a propeller and a rudder of the MASS through a proportional-integral-derivative (PID) controller based on the operating instructions to change a state of motion of the MASS;

wherein the path tracing reward function is:

r
t

=

cxp
⁡
(

-

k
E

⁢

(

x
-

x
⁡
(

θ
t

)

)

2

+

(

y
-

y
⁡
(

θ
t

)

)

2

-

k
D

⁢

φ
-

φ
⁡
(

θ
t

)

)

2

)

+

k

V
⁢
1

(

μ
-

μ
⁡
(

θ
t

)

)

+

k

V
⁢
2

(

v
-

v
⁡
(

θ
t

)

)

+

k
M

⁢

θ
Θ

;

wherein, k E is a path deviation coefficient, k D is a course angle deviation coefficient, k V1 and k V2 are velocity coefficients, k M is a progress coefficient, which can be adjusted according to a training effect; x and y are respectively a horizontal coordinate and a longitudinal coordinate in vessel coordinates; θ t is path tracing completion progress at a time t, Θ is a total path length, and

θ
Θ

is a ratio of a distance between a position of the MASS and a starting point to a distance between the starting point and a finishing point; φ is the course angle of the MASS; x(θ t ) y(θ t ) and φ(θ t ) are respectively an expected horizontal coordinate, an expected longitudinal coordinate and the course angle, at the time t; u and v are respectively velocity of the MASS in a direction of surging and velocity of the MASS in a direction of swaying; and u(θ t ) and v(θ t ) are respectively an expected value of the velocity of the MASS in the direction of surging and an expected value of the velocity of the MASS in the direction of swaying;
a computation formula of a latest collision avoidance action opportunity D LMA determined by the close quarter situation of the MASS is:

D
LMA

=

A
d

×

1
+

1

k
2

-

Assignees

• Univ Wuhan Tech

Inventors

• Ma Yong
• Zhao Yujiao

Classifications

• G05D1/0206 Primary

  specially adapted to water vehicles · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/092

  Reinforcement learning · CPC title

• H04L12/40045 Primary

  Details regarding the feeding of energy to the node from the bus · CPC title

• B63B49/00

  Arrangements of nautical instruments or navigational aids (nautical measuring instruments G01C; radio navigation, analogous arrangements using other waves G01S) · CPC title