[Paper Review] Mobile Robot Navigation on Partially Known Maps using a Fast A Star Algorithm Version

Paul Muntean|arXiv (Cornell University)|Apr 29, 2016

Robotic Path Planning Algorithms4 citations

TL;DR

This paper proposes a fast A* algorithm variant for mobile robot navigation in partially known indoor environments using ultrasonic sensors. By optimizing path planning with two operational modes—based on sensor range thresholds—it achieves significant speedups (two orders of magnitude) and improved efficiency, especially in environments with multiple unknown obstacles, demonstrating completeness and real-time feasibility on a Pioneer 2DX robot.

ABSTRACT

Mobile robot navigation in total or partially unknown environments is still an open problem. The path planning algorithms lack completeness and/or performance. Thus, there is the need for complete (i.e., the algorithm determines in finite time either a solution or correctly reports that there is none) and performance (i.e., with low computational complexity) oriented algorithms which need to perform efficiently in real scenarios. In this paper we evaluate the efficiency of two versions of the A star algorithm for mobile robot navigation inside indoor environments with the help of two software applications and the Pioneer 2DX robot. We demonstrate that an improved version of the A star algorithm (we call this the fast A star algorithm) which (a different version of this algorithm is widely used in video games) can be successfully used for indoor mobile robot navigation. We evaluated the two versions of the A star algorithm first, by implementing the algorithms in source code and by testing them on a simulator and second, by comparing two operation modes of the fast A star algorithm w.r.t. path planning efficiency (i.e., completness) and performance (i.e., time need to complete the path traversing) for indoor navigation with the Pioneer 2DX robot. The results obtained with the fast A star algorithm are promising and we think that this results can be further improved by tweaking the algorithm and by using an advanced sensor fusion approach (i.e., combine the inputs of multiple robot sensors) for better dealing with partially known environments.

Motivation & Objective

• Address the challenge of efficient and complete path planning in partially known indoor environments where obstacles are not fully mapped in advance.
• Improve upon traditional A* by developing a faster, optimized variant—'fast A*'—to enhance performance in real-time robotic navigation.
• Evaluate the algorithm’s completeness and computational efficiency using both simulation and real-world experiments with a physical robot.
• Investigate the impact of sensor range settings on path planning performance and recovery behavior in dynamic obstacle scenarios.
• Explore the potential of sensor fusion and algorithm tuning to further improve navigation robustness in complex, partially known environments.

Proposed method

• Implement two versions of the A* algorithm: standard A* and a modified fast A* algorithm with optimized node expansion and heuristic evaluation.
• Use a grid-based representation of the 2D environment, where each cell corresponds to a 10×10 cm area, and define free space (C_free) as passable and obstacle space as non-passable.
• Introduce two operational modes (M1 and M2) of the fast A* algorithm, differing in the use of action limiters and sensor range thresholds (50 mm vs. 225 mm).
• Apply real-time wireless communication via Lantronix WiBox to send navigation commands from a PC to the Pioneer 2DX robot during online testing.
• Use ultrasonic sensors (range 50 mm or 225 mm) to detect unknown obstacles during navigation, dynamically updating the map and path as new data is acquired.
• Compare performance across modes using run-time metrics and recovery action frequency, with offline simulation for initial algorithm validation.

Experimental results

Research questions

• RQ1 Can the fast A* algorithm achieve complete and efficient path planning in partially known indoor environments with real-time constraints?
• RQ2 How does the choice of sensor range (50 mm vs. 225 mm) affect path planning efficiency and the number of recovery actions in dynamic obstacle scenarios?
• RQ3 Which operational mode (M1 or M2) of the fast A* algorithm performs better in environments with one or multiple unknown obstacles?
• RQ4 To what extent does the fast A* algorithm outperform standard A* in terms of computational speed and path planning time?
• RQ5 Can sensor fusion and algorithm tuning further improve performance and robustness in complex, partially known environments?

Key findings

• The fast A* algorithm achieved a two orders of magnitude speedup compared to standard A* in offline simulation, significantly improving computational performance.
• In environments with a single unknown obstacle, Mode 1 (M1, 50 mm sensor range) achieved a path planning time of 47 seconds, outperforming Mode 2 (45 seconds), indicating better efficiency for sparse obstacle scenarios.
• In environments with two unknown obstacles, Mode 2 (M2, 225 mm sensor range) reduced path planning time to 40 seconds, outperforming Mode 1 (61 seconds), due to earlier obstacle detection and fewer recovery actions.
• The increased sensor range in Mode 2 enabled earlier detection of obstacles, reducing the need for recovery maneuvers and improving overall path completion time.
• The fast A* algorithm demonstrated completeness by successfully finding a path in finite time in all tested scenarios, confirming its reliability for real-world navigation.
• The results suggest that Mode 2 is better suited for complex environments with multiple unknown obstacles, while Mode 1 performs better in simpler, less cluttered environments.