# US 11,906,977 B2 · Path planning method

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- Number: US 11,906,977 B2
- Document ID: US11906977B2
- Status: Granted
- Kind: B2
- Application number: 17/910,842
- Filed: 2022-05-07
- Published: 2023-06-22
- Granted: 2024-02-20
- Earliest claimed: 2021-05-07
- Assignees: Soochow University
- Applicants: Soochow University
- Inventors: Rongchuan Sun; Junyi Wu; Shumei Yu; Guodong Chen; Lining Sun
- Web page: https://patentgrep.com/patent/US11906977B2

## CPC

- G05D 1/0214: {in accordance with safety or protection criteria, e.g. avoiding hazardous areas (monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13)}
- G05D 1/246: using environment maps, e.g. simultaneous localisation and mapping [SLAM]
- G05D 1/0274: {using mapping information stored in a memory device (navigation using map-matching G01C21/30)}
- G05D 1/22: Command input arrangements
- G05D 1/24: Arrangements for determining position or orientation
- G05D 1/2437: {Extracting relative motion information}
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES

## Abstract

The present invention discloses a path planning method, including the following steps: establishing an empirical map and a corresponding episodic cognitive map using a RatSLAM algorithm based on an episodic memory model; extracting a road edge in a historical memory image with a Canny operator; performing conversion to a world coordinate system from a pixel coordinate system based on the road edge, and preliminarily judging connectivity according to slope of the road edge; continuously injecting energy into the potential path detection network according to continuous observation of a potential path, so as to further judge the road connectivity; fusing the detected potential path and the original episodic cognitive map, and correspondingly updating the empirical map; and planning a path based on the updated episodic cognitive map. The potential safe path in an environment may be detected, and a better path may be planned based on the updated episodic memory model.

## Claims

### Claim 1

A path planning method for improving a navigation efficiency of a mobile robot, comprising the following steps:
providing the mobile robot;
establishing an empirical map and a corresponding episodic cognitive map using a RatSLAM algorithm based on an episodic memory model;
extracting a road edge in a historical memory image with a Canny operator;
performing conversion to a world coordinate system from a pixel coordinate system based on the road edge, and preliminarily judging connectivity according to slope of the road edge;
continuously injecting electric energy into a potential path detection network according to continuous observation of a potential path, so as to further judge the road connectivity;
fusing the detected potential path and the original episodic cognitive map, and correspondingly updating the empirical map;
planning a path based on the updated episodic cognitive map; and
fusing the path based on the updated episodic cognitive map to the mobile robot to improve the navigation efficiency of the mobile robot,
wherein the episodic memory model is a path planning algorithm;
the episodic cognitive map is a two-dimensional incremental matrix and composed of a discrete limited event space and an event transition set;
the preliminarily judging connectivity according to slope of the road edge comprises:
when an absolute value of a difference between the slope of the road edge is less than a set threshold, determining that there exists a possibility of connection between two points;
the potential path detection network has a two-dimensional network structure proposed under inspiration of a continuous attractor network in a RatSLAM model, and is configured to simulate a process of judging whether roads are connected by living things;
the updated episodic cognitive map is a new map obtained by correcting an event transition weight in the original episodic cognitive map according to the detected potential path.

### Claim 2 (depends on 1)

The path planning method according to claim 1, wherein the RatSLAM algorithm is a bionic navigation algorithm, and the establishing an empirical map using a RatSLAM algorithm comprises: establishing a two-dimensional empirical map using RGB (Red, Green, and Blue) image information collected by a monocular camera.

### Claim 3 (depends on 1)

The path planning method according to claim 1, wherein the Canny operator is an edge extraction algorithm.