Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure

Abstract

We present a simultaneous sensor-based inspection and footprint coverage (SIFC) planning and control design with applications to autonomous robotic crack mapping and filling. The main challenge of the SIFC problem lies in the coupling of complete sensing (for mapping) and robotic footprint (for filling) coverage tasks. Initially, we assume known target information (e.g., cracks) and employ classic cell decomposition methods to achieve complete sensing coverage of the workspace and complete robotic footprint coverage using the least-cost route. Subsequently, we generalize the algorithm to handle unknown target information, allowing the robot to scan and incrementally construct the target map online while conducting robotic footprint coverage. The online polynomial-time SIFC planning algorithm minimizes the total robot traveling distance, guarantees complete sensing coverage of the entire workspace, and achieves near-optimal robotic footprint coverage, as demonstrated through experiments. For the demonstrated application, we design coordinated nozzle motion control with the planned robot trajectory to efficiently fill all cracks within the robot's footprint. Experimental results illustrate the algorithm's design, performance, and comparisons. The SIFC algorithm offers a high-efficiency motion planning solution for various robotic applications requiring simultaneous sensing and actuation coverage.

Figures (18)

• Figure 1: The illustration of robotic crack inspection and filling setup with unknown crack information in a rectangular workspace $\mathcal{W}$.
• Figure 2: The overview of the SIFC planning and control algorithms.
• Figure 3: (a) An example of the footprint region $\mathcal{M}_{\text{f}}$ (red shaded areas). The solid lines are the separated cracks. (b) The constructed $\mathbb{G}_c$. Points $N_1$ to $N_{12}$ shown in red stars are nodes of $\mathbb{G}_c$, and the solid lines are edges of $\mathbb{G}_c$. The dashed lines are the added edges in the ${\tt GCC}$ algorithm.
• Figure 4: Illustration of crack graphs with different formed angles. Robot footprint $\mathcal{F}$ is illustrated by a red circle. The graph $\mathbb{G}_c$ is shown as dotted green lines, and the red stars $N_1$ and $N_2$ represent its nodes. The pink-shaded areas are the footprint region $\mathcal{M}_{\text{f}}$. The red dashed lines are the shortest paths to connect nodes $N_1$ and $N_2$ within $\mathcal{M}_{\text{f}}$. (a) Every point on $\mathbb{G}_c$ is covered by $\mathcal{F}$ as robot $\mathbf{R}$ travels along the shortest path. (b) With sharp crack angles, robot $\mathbf{R}$ cannot fully cover $\mathbb{G}_c$. (c) By adjusting the Minkowski sum area, the shortest path is achieved to ensure full crack coverage.
• Figure 5: Illustration of the SIFC planner. (a) The MCD with $\mathcal{M}_\text{c}$ (highlighted in yellow). $\mathcal{A}_i$, $C_i$, and $E_i$, $i=1,\cdots,6$, represent the Reeb graph's cells, nodes, and edges, respectively. The graph $\mathbb{G}_c$ is shown in a dotted green line, and red stars represent the nodes. The Reeb graph $\mathbb{G}_\text{w}$ of the MCD is connected with $\mathbb{G}_c$. The red-dash edges are added to the combined $\mathbb{G}_\text{w}$ and $\mathbb{G}_c$ to form an Euler tour. (b) The simplified $\mathbb{G}_\text{w}$ and $\mathbb{G}_c$. Each critical point on the boundary of the $\mathcal{M}_\text{c}$ is combined with its corresponding node in $\mathbb{G}_c$. (c) The robot path $\mathcal{P}_R$ is depicted in dotted lines, with arrows indicating the direction of travel.

Theorems & Definitions (3)

• Lemma 1
• Proposition 1
• Proposition 2