Frontier-based Method Research Articles

A heuristic autonomous exploration method based on environmental information gain during quadrotor flight

Autonomous exploration is a widely studied fundamental application in the field of quadrotor, which requires them to automatically explore unknown space to obtain complete information about the environment. The frontier-based method, one of the representative works on autonomous exploration, drives autonomous determination by the definition of frontier information so that complete information about the environment is available to the quadrotor. However, existing frontier-based methods are able to accomplish the task but still suffer from inefficient exploration, and how to improve the efficiency of autonomous exploration is the focus of research nowadays. Slow frontier generation affecting real-time viewpoint determination and insufficient determination methods affecting the quality of viewpoints are typical of these problems. Therefore, to overcome the aforementioned problems, this article proposes a two-level viewpoint determination method for frontier-based autonomous exploration. First, a sampling-based frontier detection method is presented for faster frontier generation, improving the immediacy of environmental representation compared to traditional traversal-based methods. Second, the access to environmental information during flight is considered for the first time, and an innovative heuristic evaluation function is designed to decide on high-quality viewpoint as the next local navigation target in each exploration iteration. Extensive benchmark and real-world tests are conducted to validate our method. The results confirm that our method optimizes the frontier search time by 85%, the exploration time by around 20%–30%, and the exploration path by 25%–35%.

ECHO: An Efficient Heuristic Viewpoint Determination Method on Frontier-Based Autonomous Exploration for Quadrotors

As a popular drone application, autonomous exploration suffers from low efficiency. To address the issue of repeated and unnecessary exploration, especially in a large-scale and cluttered environment, this paper proposes an efficient heuristic viewpoint determination method on frontier-based autonomous exploration, which includes viewpoint generation, evaluation, and refinement. A Gaussian sampler is employed to randomly generate higher-quality initial viewpoints; meanwhile, a fresh heuristic evaluation function is designed to select the next viewpoint; besides, a refinement strategy is presented to improve the viewpoint. Extensive simulations and real-world tests indicate that the proposed method outperforms the state-of-the-art frontier-based method by 15%-25% in almost all scenarios. More details of the experiments can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/Z9dYpEOGXcI</uri> .

Competition in bank-provided payment services

We propose and implement a frontier-based method to assess relative competition in bank-provided payment services. Billion-dollar banks account for around 90% of assets in the US and it is those with around US$4-7 billion in assets that turn out to be both the most and the least competitive in payment services, not the very largest banks.

Mobile Robot Exploration by Using Environmental Boundary Information

We present the method of exploration using environmental boundary information for an indoor map generation problem of a mobile robot. We introduce an exploration method by (i) integration of the exploration method with Reaction-Diffusion Equation on a Graph (RDEG) and connected components labeling and (ii) a replanning framework in updating exploration plan for the currently obtained sensor information. Our approach has been implemented in simulation environments and has been compared with two existing methods: frontier-based exploration method and zig-zag method. The results demonstrate the efficiency of our approach over others. Lastly, the approach was implemented and tested on an actual robot, demonstrating its efficiency in a real-world situation.

Performance based regulation of the electricity supply industry in Hong Kong: An empirical efficiency analysis approach

This paper presents an empirical efficiency analysis approach to analyze the efficiency and performance of utilities and its effects on prices under the price-cap performance based regulation (PBR) model. A data envelopment analysis (DEA)-like frontier-based method is used to compute the total factor productivity with the Malmquist productivity index. Results of case studies support the use of the approach to account for the relation of the X-factor and the PBR model on the two power utilities in Hong Kong's electricity supply industry.