Hypergame-based Adaptive Behavior Path Planning for Combined Exploration and Visual Search

Abstract

In this work, we present an adaptive behavior path planning method for autonomous exploration and visual search of unknown environments. As volumetric exploration and visual coverage of unknown environments, with possibly different sensors, are non-identical objectives, a principled combination of the two is proposed. In particular, the method involves three distinct planning policies, namely exploration, and sparse or dense visual coverage. A hypergame formulation is proposed which allows the robot to select for the next-best planning behavior in response to the currently encountered environment challenges in terms of geometry and visual conditions, alongside a self-assessment of its performance. The proposed planner is evaluated in a collection of experimental and simulation studies in diverse environments, while comparative results against a state-of-the-art exploration method are also presented.

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

“…A rich body of work has focused on the problems of autonomous single and multi-robot exploration [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Early work in single-robot exploration included the sampling of "next-best-views" [25], and the detection of frontiers [26], while recent efforts have focused on powerful planning techniques such as random trees and graphs possibly combined with volumetric calculations [22,23,[36][37][38][39], receding horizon techniques [23], multi-objective optimization [36,37], information-theoretic schemes [40], learning-based methods [41], and approaches that account for the likelihood of accumulating localization drift [22,42]. In multi-robot exploration, the seminal work in [30] presented a strategy for multi-robot coordination exploiting a grid map and a planning policy that tries to minimize the collective exploration time by considering both the cost of reaching a certain frontier cell and the "exploration utility" of each such cell as a function of the number of robots moving to that cell.…”