Access to this full-text is provided by MDPI.
Content available from Applied Sciences
This content is subject to copyright.
Academic Editor: Yutaka Ishibashi
Received: 10 February 2025
Revised: 19 February 2025
Accepted: 22 February 2025
Published: 24 February 2025
Citation: Baek, Y.; Park, J.K. Fast Path
Generation Algorithm for Mobile
Robot Navigation Using Hybrid Map.
Appl. Sci. 2025,15, 2414. https://
doi.org/10.3390/app15052414
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Article
Fast Path Generation Algorithm for Mobile Robot Navigation
Using Hybrid Map
Youngmi Baek 1,† and Jung Kyu Park 2,*,†
1Department of Smart Convergence Engineering, Computer Science Major, Changshin University,
Changwon-si 51352, Republic of Korea; ymbaek@cs.ac.kr
2Department of Computer Engineering, Daejin University, Pocheon-si 11159, Republic of Korea
*Correspondence: jkpark@daejin.ac.kr; Tel.: +82-31-539-1969
†These authors contributed equally to this work.
Abstract: In this paper, we address the challenge of memory consumption in mobile robot
navigation, particularly when using grid-based maps for accurate environment representa-
tion. While grid-based maps are widely used, their large memory requirements make them
unsuitable for embedded systems. To overcome this limitation, we propose a novel path
planning algorithm that rapidly generates secure paths using a hybrid map. This hybrid
map significantly reduces memory usage compared to grid-based maps while maintaining
the efficiency of a topological map. Experimental results demonstrate that the proposed
method requires only 1.5% of the computation time needed for grid-based path planning,
ensuring faster and more efficient navigation. Furthermore, the approach provides robust
and secure paths, making it ideal for mobile robot applications. This method holds promise
for advancing autonomous navigation in resource-constrained environments.
Keywords: hybrid map; mobile robot; memory consumption; path planning; navigation
1. Introduction
The rapid advancement of modern society has significantly increased the importance
of security and surveillance in various sectors. As the demand for enhanced security
measures grows, the number of areas and environments requiring constant monitoring has
risen sharply. This has created a substantial reliance on human resources to maintain safety
and security. To address these challenges, numerous studies have explored the potential of
intelligent mobile robots to enhance surveillance efficiency and reduce the dependence on
manual labor [1–5].
Intelligent mobile robots can perform continuous monitoring, detect anomalies, and
respond to security threats with minimal human intervention [
6
–
8
]. These robots are
equipped with advanced sensors and decision-making algorithms, enabling them to nav-
igate complex environments and provide real-time feedback. Recent advancements in
artificial intelligence and robotics have further improved their capabilities, including bet-
ter path planning, object recognition, and obstacle avoidance [
9
–
11
]. Furthermore, the
integration of IoT technology allows these robots to collaborate with existing security
infrastructure, offering a more comprehensive approach to modern surveillance. This
development not only reduces operational costs but also enhances the reliability and
consistency of security systems [12,13].
To effectively utilize intelligent mobile robots for security and patrol tasks, navigation
challenges must be addressed [
14
–
16
]. For mobile robots to solve these challenges, several
key issues need to be resolved. First, mobile robots require a map that accurately represents
Appl. Sci. 2025,15, 2414 https://doi.org/10.3390/app15052414
Appl. Sci. 2025,15, 2414 2 of 12
their surrounding environment and a path planning algorithm to perform various service
tasks [
17
,
18
]. The robot must independently navigate to its destination using the environ-
ment map. Second, mobile robots must be equipped with an obstacle avoidance algorithm.
When encountering obstacles while following a path generated from the environment
map, the robot must utilize its sensors to analyze the surroundings and safely avoid the
obstacle [19,20].
Additionally, the robot must be capable of dynamically updating its environment
map in real time to adapt to changes in its surroundings [
21
–
24
]. This capability ensures
reliable navigation in dynamic or unknown environments. Furthermore, the integration
of robust decision-making algorithms enables mobile robots to prioritize tasks efficiently,
ensuring optimal performance in complex security and patrol operations. These features
are essential for the deployment of intelligent robots in real-world applications, where
unpredictability and rapid decision-making are common.
Numerous studies have been conducted to develop algorithms that generate safe
paths for robots [
25
–
28
]. Path generation algorithms can generally be categorized into two
types: those that utilize occupancy grid-based maps and those that rely on topology maps.
One widely used approach in grid-based methods is the distance transform algorithm,
which creates paths by calculating distance values from the destination to the robot’s
starting position. In this algorithm, the robot follows the path by moving toward locations
with progressively lower distance values until it reaches its destination. While grid-based
path search algorithms are capable of generating precise paths, they present significant
limitations. One major drawback is their inability to adapt to new obstacles that may appear
during the robot’s movement. Additionally, the computational time required for path
calculation increases significantly as the size of the map grows. These challenges highlight
the need for more efficient and adaptive approaches to path planning, particularly in
dynamic and large-scale environments. Advanced algorithms that combine the precision of
grid-based methods with the adaptability of topological approaches could offer a promising
solution to these issues.
A commonly used path generation algorithm that utilizes a topological map is Depth
First Search (DFS). To apply the DFS algorithm, the environment must first be converted
into a graph-type data structure. In the DFS algorithm, the path search time is proportional
to the square of the number of nodes in the graph. Despite this, it offers faster path
generation compared to grid-based map algorithms. However, a significant drawback is
that the robot may struggle to move accurately using the path information generated by the
algorithm [
29
–
31
]. This limitation arises because topological maps often lack the detailed
spatial information needed for precise navigation, particularly in complex environments.
As a result, additional optimization or hybrid approaches may be required to enhance
the accuracy of robot movement while maintaining the efficiency of topological path
planning methods.
The previously mentioned occupancy grid-based path generation algorithm and
topology map-based path generation algorithm have the following issues. First, both
algorithms assume that the robot has access to a pre-generated map before it begins to
move. As a result, the robot cannot generate a path if pre-generated map information is
unavailable. Second, when using an occupancy grid-based map, the time required for path
generation increases significantly with the size of the map.
The rest of this paper is organized as follows. Section 2describes the research related
to the paper. Section 3describes the structure of the hybrid map and shows the fast path
planning using the hybrid map. Section 4describes the experimental results. Finally,
Section 5describes the conclusion and future plans.
Appl. Sci. 2025,15, 2414 3 of 12
2. Related Works
This paper examines the environment map and path generation algorithm used by mo-
bile robots for path planning. Existing path generation algorithms primarily focus on creat-
ing paths that minimize travel time and distance from a specific location to
the destination.
2.1. Path Planning
The A* algorithm is one of the most widely used path planning algorithms in mobile
robotics [
32
–
34
]. It efficiently finds the shortest path by combining the cost from the starting
point to a node and an estimated cost from that node to the destination. A major advantage
of A* is its ability to guarantee an optimal path if the heuristic is admissible and consistent.
Additionally, it is flexible and can be adapted to different environments and constraints
by modifying the heuristic function. However, A* can be computationally expensive,
especially in large maps with high resolution, as it explores a significant number of nodes.
This leads to increased memory usage and slower performance in real-time applications.
Moreover, it does not handle dynamic obstacles well, requiring a complete re-computation
of the path when the environment changes. Despite its limitations, A* is ideal for static
environments where precision and optimality are critical.
The Rapidly Exploring Random Tree (RRT) algorithm is a sampling-based path
planning method that is particularly effective in high-dimensional and complex envi-
ronments [
35
]. It generates a path by randomly sampling points in space and incrementally
building a tree that connects the robot’s starting position to the goal. One of its strengths is
its ability to handle non-linear constraints and dynamic environments, making it suitable
for real-time applications. Additionally, RRT requires less memory and computational
resources compared to grid-based methods like A*. However, the paths generated by
RRT are often suboptimal and may require additional post-processing to smoothen them.
Furthermore, the randomness of the algorithm can lead to inconsistent results, with the
quality of the path depending on the sampling process. Despite these drawbacks, RRT is
highly versatile and well suited for navigating in complex or unknown environments.
The Distance Transform algorithm is a grid-based path planning method that calculates
the shortest path by propagating distance values from the destination to the robot’s starting
position [
36
]. One of its primary advantages is its simplicity and ease of implementation,
making it a popular choice for straightforward environments. Additionally, it generates
precise paths by taking the exact geometry of obstacles into account, which is useful for
environments with static and well-defined boundaries. The algorithm also guarantees
optimal paths in terms of distance when no dynamic changes occur in the environment.
However, the Distance Transform algorithm has notable drawbacks. It requires a high-
resolution occupancy grid map, which can significantly increase memory consumption
and computational load. This makes it less efficient for large or complex environments,
particularly when used in real-time applications. Another limitation is its inability to
handle dynamic obstacles, as it relies on a pre-generated map. If the environment changes
during navigation, the entire path needs to be recalculated, leading to delays. Despite these
challenges, the algorithm is well suited for static environments where precision is more
important than adaptability.
2.2. Mapping
Simultaneous Localization and Mapping (SLAM) is a critical algorithm in robotics
that enables a robot to build a map of an unknown environment while simultaneously
determining its position within it [
37
]. SLAM integrates sensor data, such as lidar, cameras,
or ultrasonic sensors, to create a representation of the environment in real time. A key
component of SLAM is solving the data association problem, which involves matching
Appl. Sci. 2025,15, 2414 4 of 12
current sensor observations with previously recorded data to ensure map accuracy. Modern
SLAM algorithms leverage techniques like probabilistic methods, such as particle filters or
extended Kalman filters, to account for uncertainty in sensor data and robot motion. SLAM
is widely used in applications like autonomous vehicles, drones, and mobile robots, where
accurate navigation in dynamic environments is essential. Recent advancements in SLAM,
including visual SLAM and deep learning-based approaches, have further enhanced its
robustness and efficiency in complex and large-scale environments.
For an intelligent mobile robot to generate a map on its own, it must utilize the SLAM
algorithm. The environment map generated by the SLAM algorithm primarily employs
an occupancy grid-type map. Occupancy grid maps are widely used in mobile robots for
mission planning and navigation due to their ability to represent an environment with
fine-grained spatial details. One key advantage is their flexibility, as they can accurately
represent both free spaces and obstacles by dividing the environment into small grid cells.
This makes them particularly effective for algorithms that require precise path planning,
such as A* or Distance Transform. Additionally, occupancy grids are straightforward to
update with new sensor data, allowing for incremental map-building in static or semi-
dynamic environments. The occupancy grid method involves dividing the entire target
space into a grid and classifying each cell as Free (empty space), Occupied (obstacle), or
Unknown (unexplored area).
However, there are notable limitations to using occupancy grid maps [
38
]. High-
resolution grids require significant memory, which can be a challenge for embedded
systems or robots with limited resources. The computational complexity of processing these
maps increases with their size, making real-time performance in large-scale environments
difficult to achieve. Moreover, occupancy grids struggle with dynamic obstacles, as constant
updates to the grid can be computationally expensive. Another drawback is that these
maps often lack higher-level abstraction, such as connectivity or semantic information,
which can be useful for long-term planning. Despite these challenges, occupancy grids
remain a reliable choice for many robotic applications due to their simplicity and accuracy.
Topological decomposition maps are an efficient approach to address some limitations
of occupancy grid maps in mobile robot navigation [
17
,
39
]. These maps rely on extracting
key environmental features, such as walls, corners, and obstacles, to build a graph-based
representation of the environment. One major advantage is their reduced memory require-
ment, as they do not store fine-grained spatial data but rather abstract connections between
significant features. This results in faster computation for path planning, making them ideal
for large-scale environments where computational efficiency is critical. Additionally, topo-
logical maps simplify navigation tasks by focusing on connectivity between nodes, which
ensures that the robot can move along predefined paths with less processing overhead.
Despite these advantages, topological decomposition maps have certain limitations.
Since they focus on abstract representations, they lack the precision needed to accurately
depict the surrounding environment. This can lead to challenges in navigation, especially
in tight spaces or areas with complex obstacles. Furthermore, the robot may struggle
with precise localization and obstacle avoidance when relying solely on topological maps,
as they do not provide detailed spatial resolution. Constructing an accurate topological
map also requires robust feature detection algorithms, which may not perform well in
unstructured or highly dynamic environments. Nevertheless, these maps remain a valuable
tool for high-level navigation, particularly when combined with other methods to address
their limitations.
Appl. Sci. 2025,15, 2414 5 of 12
3. Proposed Methods
In this section, we describe a hybrid map that takes advantage of both the occupancy
grid map and the topology map. The proposed RC-Map (Rectangle Map) is a hybrid map
that analyzes the occupancy grid map of Figure 1generated by the robot to generate a fast
path with less memory usage. When a robot equipped with the RC-Map algorithm is given
a space, it acquires surrounding information using its own sensors, generates a map based
on this, and updates it. The algorithm to generate RC-Map was described in a previous
study [40].
Figure 1. Hybrid RC-MAP based on a grid map.
3.1. Hybrid Map
In this paper, we describe the structure of Hybrid RC-Map and show that it can be
used to quickly patrol a given space by making a path plan. Figure 1shows the difference
between RC-Map and an occupied grid map. Hybrid RC-Map divides the free area of
the occupied grid map into squares and expresses each square as a node. Since RC-Map
maintains only the coordinate information of squares, it can express the environment using
less memory than a cell-based occupied grid map.
Figure 2a shows a visual representation using the data structure of RC-Map. In the
actual implementation, RC-Map is stored as a graph-type data structure as shown in
Figure 2b
. This structure was chosen for its low memory usage and fast search. RC-Map
uses V and A nodes as internal data structures. V node means an empty space in the
hybrid map where the robot can move. TV-nodes maintain node numbers, coordinates
on the environmental map, and pointer information pointing to a list of adjacent nodes to
accurately represent environmental information. An A node means a node adjacent to a V
node. A node has the node number, AP (Adjacent Point), V node number, and a pointer to
the next A node. The proposed hybrid map RC-Map functions as follows from the existing
topology map. In a general topology map, a node only maintains basic information about
the area it wants to express, but in RC-Map, a square is referred to as a node, and coordinate
information about the area is maintained to maintain the accurate expression of the grid
map. In addition, an AP is used to indicate the connection between nodes and to generate
a movement path between nodes. AP is explained in the next subsection,
Path Generation.
Appl. Sci. 2025,15, 2414 6 of 12
(a) (b)
Figure 2. Implementation of RC-MAP. (a) Visual representation of the RC-MAP data structure.
(b) Internal data structure of RC-MAP.
3.2. Path Generation
In RC-Map, the concept of AP (Adjacent Point) is introduced for fast path generation.
An AP refers to the center point of the adjacent parts when two areas are adjacent. In the
actual implementation of the method, one area is expressed as a node. Assume that two
nodes A and B are as in Equations (1) and (2). As in Figure 3a, the AP of the adjacent nodes
can be expressed by Equation (3), and the AP of two nodes adjacent to each other vertically,
as in Figure 3b, can be expressed by Equation (4).
Coo rdinates o f are a A :A(Ax1,Ay1,Ax2,Ay2)(1)
Coo rdinates o f are a B :B(Bx1,By1,Bx2,By2)(2)
AP(x,y) = Bx1,Ay1+By2
2(3)
AP(x,y) = Ax1+Bx2
2,By1(4)
(a) (b)
Figure 3. Creating a path using APs. (a) Generate a path when A and B are adjacent to each other on
the left and right. (b) Generate a path when A and B are adjacent up and down.
After RC-Map is generated, the mobile robot can utilize the AP to move through the
area. If the AP is obtained through Equations (3) and (4), a path can be quickly generated
that can pass through the AP and arrive at position G regardless of where it is in area A.
Appl. Sci. 2025,15, 2414 7 of 12
For example, when there is a robot in area A on the left of Figure 3a and the robot moves
to position G of node B, the robot in node A can move directly to the AP and then to
position G. Using this method, faster path generation is possible compared to grid-based
path generation. Figure 3visually shows the path created using the generated AP.
In RC-Map, the movement of a mobile robot from the current space to the next final
destination can be viewed as a movement between nodes. It can move from the current
location to the AP of the immediately adjacent space, and then to the AP of the adjacent
node, and repeat this process to reach the final destination. At that time, moving to the AP
may not be the shortest path. However, considering the safe movement of the robot, moving
from a node in a free space to the AP is an appropriate choice in the robot’s situation.
The fast path generation algorithm using RC-Map requires data in the form of the
topology of G(V, A) and a start node and a destination node to find the optimal path.
RC-Map is in the form of general graph data, so various graph search algorithms can be
utilized. In this paper, Dijkstra’s algorithm, which is widely used to solve the shortest path
problem in graphs, was modified to operate according to the RC-Map data structure [
40
].
Algorithm 1demonstrates how to generate a fast shortest path by utilizing RC-Map.
Algorithm 1: Shortest path generation using RC-MAP
Data: G(V,A),Start node,Goal node
1while each node v in V do
2distance[v] = cost[start][v];
3path[v] = start;
4f oun d[v] = 0;
5end
6f oun d[start] = 1;
7distance[start] = 0;
8while each node v in V do
9u=choice();
10 f oun d[u] = 1;
11 while each node v in V do
12 if f oun d[w] == 0then
13 if distance[u] + cost[u][w]<distance[w]then
14 distance[w] = distance[u] + cost[u][w];
15 path[w] = u;
16 end
17 end
18 end
19 end
4. Results
In order to demonstrate that fast path generation is possible using the RC-Map pro-
posed in this paper, a simulation experiment was performed. Figure 4a is a floor plan of an
actual residential environment, and mapping was performed using a robot in that space.
Figure 4b is a virtual residential space created for simulation, and it was used to check the
robot’s movement path.
Figure 5shows the experimental environment of Figure 4a expressed in RC-Map.
About 80 V nodes and 204 A nodes were used, and 8768 bytes of memory were used. This
corresponds to about 3% of the (300 ×300)-sized grid map.
Appl. Sci. 2025,15, 2414 8 of 12
(a) (b)
Figure 4. Experimental environment. (a) Typical residential floor plan in Korea (100 m
2
). (b) Simpli-
fied floor plan for the experiment.
Figure 5. Visual representation using internal data of RC-MAP.
Figure 6shows the path generation results using RC-Map and the path generation
results using Distance Transform path generation in the experimental environment of
Figure 4a. The total moving distance was 7.19 m when RC-Map was used, and the total
moving distance was 6.18 m when the Distance Transform method was used. In terms of
total moving distance, RC-Map moved a longer distance, but the number of turns during
the movement was 6 for RC-Map and 40 for Distance Transform. This shows that the robot
using Distance Transform moved along a narrow path and made many turns, as in the
middle part of Figure 6b. This caused the robot to use more battery. In contrast, RC-Map
moved to the destination by moving nodes in the empty area where the robot could move
freely, as in Figure 6a. In order to generate the path in Figure 6, RC-Map took a total of
10 ms and Distance Transform took 740 ms. This can be an important issue for mobile
robots using embedded boards.
Appl. Sci. 2025,15, 2414 9 of 12
(a) (b)
Figure 6. Path generation results in a real residential environment. (S: start position, G: Goal position)
(a) Path generation using RC-MAP. (b) Path generation using Distance Transform.
Figure 7shows the path generation results using RC-Map and Distance Transform
path generation in the experimental environment of Figure 4b. When RC-Map was used,
the total moving distance was 18.87 m, the total number of turns was 10, and the path
generation time was 15 ms. When Distance Transform was used, the total moving distance
was 9.64 m, the total number of turns was 19, and the path generation time was 1238 ms. The
experimental results show the same results as in Figure 6above. However, depending on
the complexity of the grid map, the path generation calculation time of Distance Transform
showed a large difference compared to RC-Map. When using a grid map like this, you can
see that the Distance Trans path generation time also increased as the map resolution and
complexity increased.
(a) (b)
Figure 7. Path generation results in a real residential environment. (S: start position, G: Goal position)
(a) Path generation using RC-MAP. (b) Path generation using Distance Transform.
Appl. Sci. 2025,15, 2414 10 of 12
5. Conclusions
In this paper, we proposed a hybrid map that can be used in mobile robots. The
proposed hybrid map can generate fast paths and can be used in mobile robots and service
robots. The proposed path generation method uses a graph-type hybrid map and consumes
less memory than the existing grid map. It solves the problem of path generation time
increasing due to the resolution and complexity of the grid map. Additionally, the RC-Map
introduces the concept of Adjacent Points (APs), enabling faster and safer path planning for
mobile robots. This approach not only reduces computational complexity but also ensures
efficient traversal of large spaces.
In the future, we plan to apply RC-Map to various fields, including dynamic and
real-time navigation systems. We also aim to enhance the adaptability of RC-Map for
environments with irregular obstacles or dynamically changing layouts. Furthermore, we
plan to modify RC-Map so that it can generate more optimized paths while maintaining a
balance between efficiency and safety.
Author Contributions: Conceptualization, J.K.P. and Y.B.; methodology, J.K.P.; software, J.K.P. and
Y.B.; validation, Y.B. and J.K.P.; formal analysis, J.K.P.; investigation, J.K.P.; resources, J.K.P.; data
curation, J.K.P.; writing—original draft preparation, J.K.P. and Y.B.; writing—review and editing, Y.B.;
visualization, J.K.P. and Y.B.; supervision, J.K.P.; project administration, J.K.P. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable
Informed Consent Statement: Not applicable
Data Availability Statement: The original contributions presented in this study are included in the
article. Further inquiries can be directed to the corresponding author.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
Lee, S. An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance. IEEE Access 2023,11,
33757–33767. [CrossRef]
2.
Zhang, J.; Wu, Y.; Zhou, M. Cooperative Dual-Task Path Planning for Persistent Surveillance and Emergency Handling by
Multiple Unmanned Ground Vehicles. IEEE Trans. Intell. Transp. Syst. 2024,25, 16288–16299. [CrossRef]
3.
Scherer, J.; Schoellig, A.P.; Rinner, B. Min-Max Vertex Cycle Covers with Connectivity Constraints for Multi-Robot Patrolling.
IEEE IEEE Robot. Autom. Lett. 2022,7, 10152–10159. [CrossRef]
4.
Dai, H.; Zheng, R.; Ma, X.; Lu, Z.; Sun, G.; Xu, Z. Adaptive Tracking Strategy for the Positioning of Millimeter-Wave Radar
Security Robots. IEEE Sens. J. 2024,24, 21321–21330. [CrossRef]
5.
Li, Y.; He, J.; Chen, C.; Guan, X. Intelligent Physical Attack Against Mobile Robots with Obstacle-Avoidance. IEEE Trans. Robot.
2023,39, 253–272. [CrossRef]
6.
Queralta, J.P.; Li, Q.; Ferrer, E.C.; Westerlund, T. Secure Encoded Instruction Graphs for End-to-End Data Validation in Au-
tonomous Robots. IEEE Internet Things J. 2022,9, 18028–18040. [CrossRef]
7.
Li, X.; Xu, Z.; Su, Z.; Wang, H.; Li, S. Distance- and Velocity-Based Simultaneous Obstacle Avoidance and Target Tracking for
Multiple Wheeled Mobile Robots. IEEE Trans. Intell. Transp. Syst. 2024,25, 1736–1748. [CrossRef]
8.
Mugisha, S.; Guda, V.K.; Chevallereau, C.; Chablat, D.; Zoppi, M. Motion Prediction with Gaussian Processes for Safe Hu-
man–Robot Interaction in Virtual Environments. IEEE Access 2023,12, 67470–67485. [CrossRef]
9.
Yau, K.A.; Lee, H.J.; Chong, Y.; Ling, M.H.; Syed, A.R.; Wu, C. Augmented Intelligence: Surveys of Literature and Expert Opinion
to Understand Relations Between Human Intelligence and Artificial Intelligence. IEEE Access 2021,9, 136744–136761. [CrossRef]
10.
Alshammari, R.F.N.; Arshad, H.; Rahman, A.H.A.; Albahri, O.S. Robotics Utilization in Automatic Vision-Based Assessment
Systems From Artificial Intelligence Perspective: A Systematic Review. IEEE Access 2022,12, 77537–77570. [CrossRef]
11.
Song, C.S.; Jo, B.W.; Kim, Y.K.; Park, S.H. A Manual: Developing Artificial Social Intelligence (ASI) Lite-Scale for Service Robots.
IEEE Robot. Autom. Lett. 2024,9, 7158–7165. [CrossRef]
Appl. Sci. 2025,15, 2414 11 of 12
12.
Aoki, S.; Yonezawa, T.; Kawaguchi, N. RobotNEST: Toward a Viable Testbed for IoT-Enabled Environments and Connected and
Autonomous Robots. IEEE Sens. Lett. 2022,6, 1–4. [CrossRef]
13.
Galdelli, A.; Pietrini, R.; Mancini, A.; Zingaretti, P. Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path
Planning. IEEE Access 2024,12, 50154–50164. [CrossRef]
14.
Shahriari, M.; Biglarbegian, M. Toward Safer Navigation of Heterogeneous Mobile Robots in Distributed Scheme: A Novel
Time-to-Collision-Based Method. IEEE Trans. Cybern. 2022,52, 9302–9315. [CrossRef]
15.
Heuvel, J.; Zeng, X.; Shi, W.; Sethuraman, T.; Bennewitz, M. Spatiotemporal Attention Enhances Lidar-Based Robot Navigation in
Dynamic Environments. IEEE Robot. Autom. Lett. 2024,9, 4202–4209. [CrossRef]
16.
Adwitiya, M.; Kim, B. Using Proxemics as a Corrective Feedback Signal during Robot Navigation. In Proceedings of the HRI’24:
Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, Boulder, CO, USA, 12–15 March 2024;
pp. 732-–736.
17.
Zhao, W.; Lin, R.; Dong, S.; Cheng, Y. A Study of the Global Topological Map Construction Algorithm Based on Grid Map
Representation for Multirobot. IEEE Trans. Autom. Sci. Eng. 2023,20, 2822–2835. [CrossRef]
18.
Wei, Y.; Zhang, H.; Zhong, H.; Liu, L.; Jiang, Y.; Wang, Y. Indoor Environment Mapping of Mobile Robot Based on Efficient
DSM-PF Method. IEEE Sens. J. 2022,22, 3672–3685. [CrossRef]
19.
Dong, H.; Weng, C.Y.; Guo, C.; Yu, H.; Chen, I.M. Real-Time Avoidance Strategy of Dynamic Obstacles via Half Model-Free
Detection and Tracking with 2D Lidar for Mobile Robots. IEEE/ASME Trans. Mechatronics 2021,26, 2215–2225. [CrossRef]
20.
Hong, Y.; Ding, Z.; Yuan, Y.; Chi, W.; Sun, L. Obstacle Avoidance Learning for Robot Motion Planning in Human–Robot Integration
Environments. IEEE Sens. J. 2023,15, 2169–2178. [CrossRef]
21.
Hewawasam, H.S.; Ibrahim, M.Y.; Appuhamillage, G.K. Past, Present and Future of Path-Planning Algorithms for Mobile Robot
Navigation in Dynamic Environments. IEEE Open J. Ind. Electron. Soc. 2022,3, 353–365. [CrossRef]
22.
Loganathan, A.; Ahmad, N.S. A Hybrid HHO-AVOA for Path Planning of a Differential Wheeled Mobile Robot in Static and
Dynamic Environments. IEEE Access 2024,12, 25967–25979. [CrossRef]
23.
Pei, M.; An, H.; Liu, B.; Wang, C. An Improved Dyna-Q Algorithm for Mobile Robot Path Planning in Unknown Dynamic
Environment. IEEE Trans. Syst. Man Cybern. Syst. 2022,52, 4415–4425. [CrossRef]
24.
Liu, X.; Ge, S.S.; Zhao, F.; Mei, X. Optimized Impedance Adaptation of Robot Manipulator Interacting with Unknown Environment.
IEEE Trans. Control Syst. Technol. 2021,29, 411–419. [CrossRef]
25.
Ray, A.K.; Behera, L.; Jamshidi, M. Sonar-Based Rover Navigation for Single or Multiple Platforms: Forward Safe Path and Target
Switching Approach. IEEE Syst. J. 2008,2, 258–272. [CrossRef]
26.
Ayawli, B.B.K.; Mei, X.; Shen, M.; Appiah, A.Y.; Kyeremeh, F. Mobile Robot Path Planning in Dynamic Environment Using
Voronoi Diagram and Computation Geometry Technique. EEE Access 2019,7, 86026–86040. [CrossRef]
27.
Lee, D.H.; Choi, S.; Na, K.I. ASAP: Agile and Safe Pursuit for Local Planning of Autonomous Mobile Robots. IEEE Access 2024,12,
99600–99613. [CrossRef]
28.
Chen, C.; Frey, J.; Arm, P.; Hutter, M. SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments.
IEEE Robot. Autom. Lett. 2023,8, 7170–7177. [CrossRef]
29.
Zheng, X.; Liu, M.; Jin, L.; Yang, C. Distributed Collaborative Control of Redundant Robots Under Weight-Unbalanced Directed
Graphs. IEEE Trans. Ind. Inform. 2024,20, 681–690. [CrossRef]
30.
Zhao, L.; Deng, X.; Li, R.; Gui, X.; Sun, J.; Li, T.; Zhang, B. Graph-Based Robust Localization of Object-Level Map for Mobile
Robotic Navigation. IEEE Trans. Ind. Electron. 2024,71, 697–707. [CrossRef]
31.
Zhang, H.; Cheng, J.; Zhang, L.; Li, Y.; Zhang, W. H2GNN: Hierarchical-Hops Graph Neural Networks for Multi-Robot
Exploration in Unknown Environments. IEEE Robot. Autom. Lett. 2022,7, 3435–3442. [CrossRef]
32.
Guruji, A.K.; Agarwal, H.; Parsediya, D.K. Time-efficient A* Algorithm for Robot Path Planning. Procedia Technol. 2016,23,
144–149. [CrossRef]
33.
Xiang, D.; Lin, H.; Ouyang, J.; Huang, D. Combined improved A* and greedy algorithm for path planning of multi-objective
mobile robot. Sci. Rep. 2022,12, 13273. [CrossRef] [PubMed]
34.
Qin, H.; Shao, S.; Wang, T.; Yu, X.; Jiang, Y.; Cao, Z. Review of Autonomous Path Planning Algorithms for Mobile Robots. Drones
2023,7, 211. [CrossRef]
35.
Yuan, C.; Zhang, W.; Liu, G.; Pan, X.; Liu, X. A Heuristic Rapidly-Exploring Random Trees Method for Manipulator Motion
Planning. IEEE Access 2020,8, 900–910. [CrossRef]
36.
Chen, Y.; Lai, S.; Cui, J.; Wang, B.; Chen, B.M. GPU-Accelerated Incremental Euclidean Distance Transform for Online Motion
Planning of Mobile Robots. IEEE Robot. Autom. Lett. 2022,7, 6894–6901. [CrossRef]
37.
Durrant-whyte, H.; Bailey, T. Simultaneous Localization and Mapping: Part I. IEEE Robot. Autom. Mag. 2006,13, 99–110.
[CrossRef]
38.
Oh, S.I.; Kang, H.B. Fast Occupancy Grid Filtering Using Grid Cell Clusters From LIDAR and Stereo Vision Sensor Data. IEEE
Sens. J. 2016,16, 7258–7266. [CrossRef]
Appl. Sci. 2025,15, 2414 12 of 12
39.
Muravyev, K.; Yakovlev, K. Evaluation of Topological Mapping Methods in Indoor Environments. IEEE Access 2016,11,
132683–132698. [CrossRef]
40.
Lee, C.W.; Lee, J.D.; Ahn, J.; Oh, H.J.; Park, J.K.; Jeon, H.S. A Low Overhead Mapping Scheme for Exploration and Representation
in the Unknown Area. Appl. Sci. 2019,9, 3089. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
Available via license: CC BY 4.0
Content may be subject to copyright.
