Autonomous Exploration and Map Construction of a Mobile Robot Based on the TGHM Algorithm.

Area of Science:

• Robotics
• Artificial Intelligence
• Computer Science

Background:

• Mobile robots often operate in unknown environments lacking a priori maps.
• Manual map construction in large-scale environments is labor-intensive and inefficient.
• Autonomous exploration algorithms are crucial for robots to navigate and map new areas independently.

Purpose of the Study:

• To propose an efficient autonomous exploration and mapping method for mobile robots.
• To develop a novel hybrid map representation for enhanced exploration capabilities.
• To achieve high coverage and efficiency in map construction for unknown environments.

Main Methods:

• Introduced an incremental caching topology-grid hybrid map (TGHM) integrating topological and grid-based map features.
• Employed geometry-based rules for rapid candidate target point generation at the start of each exploration round.
• Evaluated information gain for candidate topology nodes and selected the optimal next target point, updating the map incrementally.

Main Results:

• The proposed TGHM-based autonomous exploration method demonstrated high efficiency.
• The algorithm achieved high coverage of the explored environment.
• Simulations and experiments validated the effectiveness of the approach in robot autonomous exploration and map construction.

Conclusions:

• The TGHM offers a robust framework for efficient autonomous robot exploration and mapping.
• The method effectively balances information gain and motion cost for optimal exploration strategies.
• This approach significantly reduces the workload associated with mapping large-scale unknown environments.