Exploiting a Variable-Sized Map and Vicinity-Based Memory for Dynamic Real-Time Planning of Autonomous Robots
Abstract
:1. Introduction
- A unified autonomous navigation system that dynamically adapts to complex environments with robust obstacle avoidance. Its low computational overhead enables onboard processing for visual odometry, local mapping, and trajectory planning without requiring prior knowledge of the environment or reliance on complex sensors.
- An innovative, adaptive local ESDF map that dynamically adjusts its size and resolution based on vehicle velocity, enabling high-frequency updates for efficient and responsive path planning.
- A novel mechanism combining adaptive offset map positioning based on angular velocity and short-term map memory to retain information from previously observed areas at the vicinity of the robot, minimizing redundant calculations and preventing unnecessary maneuvers.
2. Mapping and Path Planning Algorithm
2.1. Local Mapping
Algorithm 1 Occupancy update algorithm |
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Algorithm 2 ESDF update algorithm |
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2.2. Path Planning
3. The Developed UGV
3.1. Hardware
3.2. Control
4. Experimental Results
4.1. Simulation Experiments
4.2. Real-World Experiments
5. Discussion
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
GPS | Global Positioning System |
ESDF | Euclidean Signed Distance Field |
RGB-D | Red Green Blue–Depth |
TSDF | Truncated Signed Distance Field |
3D | Three-Dimensional |
LiDAR | Light Detection And Ranging |
UGV | Unmanned Ground Vehicle |
CPU | Central Processing Unit |
CPR | Counts Per Revolution |
CAN | Controller Area Network |
PID | Proportional–Integral–Derivative |
PP | Projected Point |
RAM | Random-Access Memory |
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Method | 4S | 7S | 4S-1D | 4S-2D |
Proposed | 1.100 | 1.113 | 1.266 | 1.153 |
FAST Planner | 1.095 | 1.128 | 1.268 | 1.154 |
Method | Narrow | Winding | Sparse |
Proposed | 0.83 | 1.0 | 0.8 |
FAST-Planner | 0.0 | 0.0 | 0.0 |
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Geladaris, A.; Papakostas, L.; Mastrogeorgiou, A.; Polygerinos, P. Exploiting a Variable-Sized Map and Vicinity-Based Memory for Dynamic Real-Time Planning of Autonomous Robots. Robotics 2025, 14, 44. https://doi.org/10.3390/robotics14040044
Geladaris A, Papakostas L, Mastrogeorgiou A, Polygerinos P. Exploiting a Variable-Sized Map and Vicinity-Based Memory for Dynamic Real-Time Planning of Autonomous Robots. Robotics. 2025; 14(4):44. https://doi.org/10.3390/robotics14040044
Chicago/Turabian StyleGeladaris, Aristeidis, Lampis Papakostas, Athanasios Mastrogeorgiou, and Panagiotis Polygerinos. 2025. "Exploiting a Variable-Sized Map and Vicinity-Based Memory for Dynamic Real-Time Planning of Autonomous Robots" Robotics 14, no. 4: 44. https://doi.org/10.3390/robotics14040044
APA StyleGeladaris, A., Papakostas, L., Mastrogeorgiou, A., & Polygerinos, P. (2025). Exploiting a Variable-Sized Map and Vicinity-Based Memory for Dynamic Real-Time Planning of Autonomous Robots. Robotics, 14(4), 44. https://doi.org/10.3390/robotics14040044