History-Aware Planning for Risk-free Autonomous Navigation on Unknown Uneven Terrain

TL;DR

This study presents a layered and systematic pipeline that maintains a tree structure that is dynamically extended with the navigation and develops an evaluation method whose input elements can be efficiently obtained on the layered structure.

Abstract

It is challenging for the mobile robot to achieve autonomous and mapless navigation in the unknown environment with uneven terrain. In this study, we present a layered and systematic pipeline. At the local level, we maintain a tree structure that is dynamically extended with the navigation. This structure unifies the planning with the terrain identification. Besides, it contributes to explicitly identifying the hazardous areas on uneven terrain. In particular, certain nodes of the tree are consistently kept to form a sparse graph at the global level, which records the history of the exploration. A series of subgoals that can be obtained in the tree and the graph are utilized for leading the navigation. To determine a subgoal, we develop an evaluation method whose input elements can be efficiently obtained on the layered structure. We conduct both simulation and real-world experiments to evaluate the developed method and its key modules. The experimental results demonstrate the effectiveness and efficiency of our method. The robot can travel through the unknown uneven region safely and reach the target rapidly without a preconstructed map.

Figures (10)

• Figure 1: The application of the developed pipeline in real-world environments. A local tree and a global graph are maintained along with the navigation.
• Figure 2: System diagram of the developed mapless navigation framework.
• Figure 3: Illustration of the hazardous region generation. In (a), the blue node is checked. It may incur unfeasible edges represented by red virtual lines within an eight-sector space because it is close to hazardous regions. If it is saturated then it will be marked on the map, as shown in (b). (c) showcases the hazardous regions estabilish based on the saturated nodes to avoid unsafe navigation maneuvers.
• Figure 4: The process of pruning nodes and edges. The green line is the current local boundary while the blue line is the boundary of $\mathcal{M}_{L}$ at the last-moment.
• Figure 5: Illustration of graph construction. The green boundary represents the range of $\mathcal{M}_{L}$. The blue lines and small circles are the vertices and edges of $\mathcal{G}$. The yellow lines and rectangles are the nodes and edges of $\mathcal{T}$. The purple line represents the trajectory of the robot and the blue shaded lines are the branches that are to be added to the $\mathcal{G}$.