Exploring Large and Complex Environments Fast and Efficiently

This paper describes a novel framework for autonomous exploration in large and complex environments. We show that the framework is efficient as a result of its hierarchical structure, where at one level it maintains a sparse representation of the environment and at another level, a dense representation is used within a local planning horizon around the robot. The exploration path is computed at the two levels, coarsely at the global scale and finely around the robot. Such a framework produces detailed paths in the vicinity of the robot, while trades off data resolution far away from the robot for computational efficiency. In experiments, we evaluate our method with a real robot exploring large and complex indoor and outdoor environments. Results show that our method is twice as efficient in covering spaces while using less than one-fifth of processing in comparison to state-of-the-art methods.

Authors

• Chao Cao
• Hongbiao Zhu
• Howie Choset
• Ji Zhang 0003

Keywords

• Runtime
• Automation
• Electric breakdown
• Conferences
• Planning
• Computational efficiency
• Robots
• Complex Environment
• Global Scale
• Hierarchical Structure
• Urban Planning
• Outdoor Environments
• Indoor Environments
• Representation Of The Environment
• Exploration Path
• Field Of View
• Shortest Path
• Global Plan
• Amount Of Space
• Traveling Salesman Problem
• Planning Cycle
• Priority Queue
• Past Trajectories
• Rapidly-exploring Random Tree