Robot Navigation Technology and Its Application

Yurui Hu

College of Light Industry, Harbin University of Commerce University,Harbin City, Heilongjiang Province, China

Keywords: Robot Navigation Technology, Application, Technology.

Abstract: In this paper, robot navigation technology is comprehensively reviewed. This paper introduces the basic

concepts and key technologies of robot navigation, including location, map construction, path planning,

classification and application scenarios of navigation algorithms. This study analyzes the advantages and

disadvantages of different positioning technologies. It evaluates the strengths and limitations of various map

construction technologies. The research compares the performance of different path planning technologies. It

examines the effectiveness of different robot navigation algorithms. The paper forecasts future trends in

intelligent robot navigation technology. Miniaturization is predicted to become a key development direction.

Low-cost solutions are expected to gain prominence in research and applications. Cooperative navigation

technologies are anticipated to advance significantly. The findings aim to provide references for robot

navigation research. The results offer practical guidance for real-world applications of intelligent robots. This

systematic analysis of core robot navigation technologies provides critical insights for technology selection,

algorithm optimization, and system development. The predictions of future trends will help accelerate the

industrialization of intelligent robotics and advance collaborative navigation systems.

1 INTRODUCTION

With the continuous development of science and

technology, robots are increasingly widely used in

various fields, such as industrial production, logistics

distribution, medical services, family services. Robot

navigation technology is the key to realize the

autonomous movement of robots and complete tasks.

It enables robots to determine their own position in a

complex environment, perceive the surrounding

environment and plan a reasonable path to reach the

target location (Thrun, Burgard, Fox,2005).

The research of robot navigation technology

began in the 1960s, and it was mainly applied to

industrial robots at first. With advances in

technologies such as computer vision, sensor fusion

and artificial intelligence, robot navigation has

gradually expanded from structured environments to

industrial unstructured environments. In the 1990s,

the proposal of simultaneous localization and Map

Construction (SLAM) technology brought a

revolutionary breakthrough for robot navigation. In

the 21st century, the rise of artificial intelligence

technologies such as deep learning has further

https://orcid.org/0009-0000-2427-8793

promoted the development of robot navigation

technology, making its performance better in

complex and dynamic environments.

Previous studies have achieved important results

in every key link of robot navigation. In terms of

localization and map construction, researchers have

proposed filter-based SLAM (such as EKF-SLAM)

and Graph-SLAM (such as Graph-SLAM). In path

planning, global planning methods such as A*

algorithm and Dijkstra algorithm, as well as local

planning methods such as dynamic window method

(DWA) and artificial potential field method are

widely used. Obstacle avoidance technology has

developed from a simple ultrasonic sensor to a

complex multi-sensor fusion system. In recent years,

the application of deep reinforcement learning in

navigation decision making has also made

remarkable progress.

This paper aims to comprehensively review the

research status of robot navigation technology,

analyze the advantages and disadvantages of key

technologies, discuss the existing problems and

challenges of robot navigation technology, and put

212

Hu, Y.

Robot Navigation Technology and Its Application.

DOI: 10.5220/0013681200004670

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Data Science and Engineering (ICDSE 2025), pages 212-217

ISBN: 978-989-758-765-8

forward the future development trend of the

technology.

In the first place， the key technologies in robot

navigation must be systematically sorted out, and

their advantages and disadvantages must be analyzed.

The second step summarizes the role of robot

navigation technology in different application

scenarios. The third step is to discuss the existing

problems and challenges of robot navigation

technology. Finally, summarize the future

development trend of robot navigation technology.

2 MAP CONSTRUCTION

TECHNOLOGY

First, raster maps. Raster maps divide the

environment into equal-sized grids, each indicating

whether it is occupied by obstacles, simple and

intuitive, easy-to-implement path planning

algorithms, but there needs to be a balance between

map resolution and storage (Elfes,1989). Next is the

topological map. Topological maps use nodes and

edges to represent key positions and connection

relationships in the environment. With a small

amount of data, topological maps are suitable for

navigation in large-scale environments, but their

adaptability to environmental changes is relatively

weak (Choset, Nagatani,2017). Finally, there are

semantic maps. A semantic map refers to integrating

the semantic information of the environment into the

map, such as the category and function of the object,

so that the robot can better understand the

environment and provide support for advanced task

planning (Galindo, Fernandez-Madrigal,

Gonzalez,2017).

3 PATH PLANNING

TECHNIQUES

3.1 Global Path Planning

The first is the A* algorithm, the principle of the A*

algorithm can be divided into the following steps.

The first is initialization. Place the starting node

into the OPEN list, which is used to store nodes

awaiting evaluation. At the same time, create a

CLOSED list to store the evaluated nodes.

The second step is the node evaluation. Take the

node with the lowest value of the valuation function

f(n) from the open list as the current node and move

it to the closed list. Then, calculate f(n) values for all

neighbors of the current node. If the neighbor node is

not already in the open list, add it to the open list and

set the current node as its parent. If the neighbor node

is already in the open list and the path to that neighbor

node through the current node is shorter, update the

parent and f(n) values for that neighbor node.

The third step is the path lookup. Repeat the

process until the open list is empty or the target node

is added to the open list. If the target node is added to

the open list, then start at the target node and trace

back along the parent node of each node until you

reach the starting node to get the shortest path. If the

open list is empty, there is no path from the start node

to the destination node.

Finally, the heuristic function is selected. The key

to the A* algorithm is the choice of the heuristic

function h(n). A good heuristic function should be

able to accurately predict the cost from the current

node to the destination node so that the algorithm can

search more efficiently. Common heuristic functions

include Manhattan distance, Euclidean distance, and

diagonal distance, among others

A* algorithm uses heuristic search to find the

optimal path from the starting point to the target

point, which has high search efficiency, but may fall

into local optimality in complex environments (Hart,

Nilsson, Raphael,1968).

A* algorithm is mainly used to solve path

planning problem and multi-objective navigation

problem in robot navigation technology.

A* algorithm is widely used in the following

aspects.

3.1.1 Industrial Robots

In automated factories, the A* algorithm is used for

the path planning of material handling robots. The

robots can efficiently handle materials between

shelves, production lines and warehouses. They can

avoid collisions with equipment and personnel. All of

these can improve the efficiency of production

logistics.

3.1.2 Service Robots

The restaurant service robot uses the A* algorithm. It

plans the optimal delivery path from the kitchen to the

table. It does this according to the restaurant layout

and table position. The robot can also flexibly avoid

obstacles. It does so when it encounters customers or

other obstacles. This ensures efficient and accurate

service.

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213

3.1.3 Driverless Vehicles

Driverless vehicles are driving on urban roads. The

A* algorithm is used. It combines map data and real -

time road condition information. It plans the optimal

route from the current location to the destination. It

also takes into account traffic lights, pedestrians,

other vehicles and other factors. This ensures safe and

smooth driving.

3.1.4 Drone

When the drone is conducting surveying and

mapping, inspection and other tasks, the A* algorithm

can use information such as terrain, buildings and no-

fly areas. It can plan a safe and efficient flight path so

that it can complete the task as required.

3.2 Dijkstra's Algorithm

Dijkstra's algorithm is an algorithm used to find the

shortest path between nodes in a graph. The basic

principle is to use the greedy strategy, each time

selecting the node with the shortest distance from the

current node to the starting point as the next

intermediate node, and updating the other nodes with

the shortest distance from the starting point.

The specific steps of Dijkstra's algorithm are as

follows

First is initialization. Set the distance from the starting

point to itself to 0. Set the distance from the other

nodes to the starting point to infinity.

The second step is to select the current node.

Select the node with the shortest distance from the

starting point from the unvisited node as the current

node.

The last step is to update the distance. For nodes

adjacent to the current node, update its shortest

distance to the starting point. If the path to the

adjacent node through the current node is shorter than

the previously calculated path, update the path value.

Dijkstra's algorithm can guarantee to find the global

optimal path: however, the calculation complexity is

higher, and it is suitable for small-scale maps.

In robot navigation technology, Dijkstra's

algorithm is mainly used to solve the path search

problem and the dynamic environment adaptation

problem.

3.2.1 Warehouse Logistics Robots

In large warehouses, logistics robots need to move

goods between shelves quickly and accurately.

The Dijkstra algorithm can plan the optimal

driving path for the robot based on information such

as the layout of the warehouse, the location of the

shelves and the storage points of the goods,

improving the efficiency of cargo handling and

reducing transportation time and cost.

3.2.2 Rescue Robot

Detected obstacles, dangerous areas and possible

channels, and other information, planned a safe

search and rescue path, quickly reached the location

of trapped people, improve the success rate of rescue.

In earthquake, fire, and other disaster sites, the

environment is complex and dangerous, the rescue

robot uses the Dijkstra algorithm, according to real-

time.

3.2.3 Agricultural Robot

When the agricultural robot carries out sowing,

fertilizing, weeding, and other operations in the field.

The Dijkstra algorithm can plan the optimal operation

path for the robot according to the terrain of the field,

crop distribution and obstacles, so as to ensure that

the robot can complete the task efficiently and avoid

damage to crops.

The basic principle of the artificial potential field

method is to regard the robot as a particle moving in

the potential field, the target point generates gravity,

and the obstacle generates repulsion, thus guiding the

robot to avoid the obstacle and reach the target, but

there is a local minimum problem.

By considering the robot's velocity, acceleration,

and other dynamic constraints, the dynamic window

method searches feasible paths in the velocity space

with good real-time performance and can adapt to a

dynamic environment (Fox, Burgard, Thrun, 1997)

Classification and characteristics of robot

navigation algorithm:

3.3.1 Reaction-based Navigation Algorithm

The robot makes decisions directly. It makes

decisions according to the perception information of

the current environment. It responds quickly. It can

respond to emergencies quickly. However, it lacks

consideration of the global environment. This lack of

consideration may lead to a sub-optimal path (Brooks,

1986).

3.3.2 Planning-based Navigation Algorithm

environment modeling and path planning are carried

out first, and then execution is carried out according

to the planned path. The optimal path can be obtained,

but the computational complexity is high, and the

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adaptability to environmental changes is relatively

slow (LaValle, 2006).

3.3.3 Learning-based Navigation Algorithms

Such as reinforcement learning, deep learning, and

other methods, which automatically optimize

navigation strategies by letting robots learn and train

continuously in the environment, have strong

adaptability and flexibility, but need a lot of training

data and computing resources (Sutton, Barto, 2018).

4 APPLICATION SCENARIOS

4.1 Industrial Sector

Industrial robots are on the production line. They

carry out material handling tasks. They also carry out

assembly tasks. Precise navigation technology is

important. It ensures that the robots can complete the

work efficiently. It also ensures that the robots can

complete the work accurately. As a result, production

efficiency is improved. Production quality is also

improved (Groover, 2013).

Robot navigation technology in the industrial

field is mainly used in manufacturing and assembly,

quality testing and monitoring, industrial cleaning

and maintenance.

In the industrial field, robot navigation technology

is playing an irreplaceable role, becoming a key force

in promoting industrial intelligent upgrading. Taking

warehousing and logistics as an example, the

automated guided vehicle (AGV) realizes intelligent

material handling by relying on advanced navigation

technology. Laser navigation AGV in the process of

operation, by emitting a laser beam and receiving

reflected signals, can accurately determine its own

position, and then in the warehouse full of shelves and

goods flexible shuttle, efficient completion of goods

transportation tasks. In the industrial production line,

the collaborative robot can sense the surrounding

environment and the location information of parts in

real-time with the help of visual navigation

technology to achieve high-precision assembly

operations. For example, in the assembly line of auto

parts, after the use of visual navigation cooperative

robots, the assembly efficiency is increased by 40%,

and the product defect rate is reduced by 15%, which

significantly improves the production quality and

efficiency.

4.2 Logistics and Warehousing

Automated guided vehicles (AGV) and mobile robots

realize automatic storage, retrieval and transportation

of goods in warehouses, optimize logistics processes

and reduce labor costs (Vis, 2006).

Robot navigation technology in logistics

warehouses is mainly used in inventory and

management, sorting and distribution, intelligent

storage layout optimization and so on.

In the field of logistics and warehousing, the

application of robot navigation technology is

profoundly changing the traditional operation mode

and becoming a key factor in improving logistics

efficiency and management level. For example, with

the help of laser navigation technology, an automatic

guided vehicle (AGV) can send laser beams through

lidar and receive reflected signals to build

environmental maps and determine its own position,

so as to accurately travel along the preset path in the

complex warehouse environment and realize the

efficient handling of goods. Using laser navigation

AGV large logistics warehouse, cargo handling

efficiency is improved by 35%, and due to accurate

positioning, the cargo placement error rate is reduced

to less than 1%. For example, the composite robot

uses visual navigation and SLAM navigation

technology, which can not only autonomically

navigate and accurately position the goods from the

entrance to the storage area and from the storage area

to the exit of the storage area, but also automatically

sort the goods by scanning the two-dimensional code

or RFID tag on the goods, and seamlessly update the

inventory information with the warehouse

management system.

4.3 Service Robot

Home service robots can autonomously navigate

indoors and complete tasks such as cleaning and

companionship; Medical service robots can assist

medical staff in medicine distribution and patient care

in hospitals to improve service quality and efficiency

(Siciliano, Khatib ,2016).

Robot navigation technology in the service robot

is mainly used in catering service, hotel service,

medical services, family services and so on.

In the field of service robots, robot navigation

technology plays a crucial role, which greatly

improves the quality and efficiency of services. In the

hotel scene, with the help of laser navigation and

visual navigation integration technology, the delivery

service robot can accurately identify the hotel's

corridor, room number, and other information and

quickly deliver items to the designated room. The

Robot Navigation Technology and Its Application

215

hotel distribution robot using this kind of navigation

technology has improved the distribution efficiency

by about 40% compared with manual labor, and can

also effectively reduce the error rate. In the hospital,

the guide robot uses SLAM navigation technology to

build a hospital map, combined with voice interaction

and visual recognition. To provide patients with

accurate department guidance services. The guidance

robot has been introduced. It has reduced the average

time of patients searching for departments. The

reduction is by more than 50%. This has greatly

improved the medical experience of patients. In the

shopping mall, there is a shopping guide robot. It uses

multi-sensor fusion navigation technology. It can

sense the surrounding environment in real time. It can

also sense the flow of people in real time. It plans the

optimal path. Then it provides shopping guide

services for customers. This effectively improves the

shopping efficiency of customers. It also improves

the satisfaction of customers.

5 EXISTING PROBLEMS AND

CHALLENGES

The first is the problem of adaptability to complex

environments. In complex, dynamic and unknown

environments, such as crowded public places and

outdoor unstructured terrain, the accuracy and

reliability of robot navigation still need to be

improved (Himmelsbach, Wuensche, 2018).

Secondly, there is the problem of multi-sensor fusion.

Fusing the data of multiple sensors, giving full play

to the advantages of each sensor, and reducing data

redundancy and conflict are key issues (Khaleghi,

Khamis, Karray, 2013). Real-time performance is

important. Computing resources are important, too.

They ensure the accuracy of navigation. They meet

the needs of real-time decision-making of robots.

They also reduce the dependence on hardware

computing resources. This is of great significance for

some resource-limited robot platforms (Chen, Liu,

Zhang, 2017).

6 FUTURE DEVELOPMENT

TREND

Intelligence: With the development of artificial

intelligence technology, robot navigation will be

more intelligent, able to independently learn and

adapt to complex and changing environments, and

achieve more advanced task planning and decision-

making.

Miniaturization and low cost: Develop smaller

and lower-cost navigation sensors and devices to

promote the popularization of robots in more fields.

Collaborative navigation: Multiple robots can

work and navigate together to complete complex

tasks and improve work efficiency and overall system

performance.

7 CONCLUSION

Robot navigation technology has made remarkable

progress after years of development, but it still faces

many challenges. Key technologies such as

positioning, map construction and path planning are

continuously improved and innovated. This is

combined with the development of emerging

technologies like artificial intelligence. The robot

navigation system will become more intelligent. It

will become more efficient. It will also become more

reliable. It provides strong support for the wide

application of robots in various fields. It promotes the

development of the era of intelligent robots.

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