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This paper addresses the problem of robotic exploration of unknown indoor environments with deadlines. Indoor exploration using mobile robots has typically focused on exploring the entire environment without considering deadlines. The objective of the prioritized exploration in this paper is to rapidly compute the geometric layout of an initially unknown environment by exploring key regions of the environment and returning to the home location within a deadline. This prioritized exploration is useful for time-critical and dangerous environments where rapid robot exploration can provide vital information for subsequent operations. For example, firefighters, for whom time is of the essence, can utilize the map generated by this robotic exploration to navigate a building on fire. In our previous work, we showed that a priority-based greedy algorithm can outperform a cost-based greedy algorithm for exploration under deadlines. This paper models the prioritized exploration problem as an Orienteering Problem (OP) and a Profitable Tour Problem (PTP) in an attempt to generate exploration strategies that can explore a greater percentage of the environment in a given amount of time. The paper presents simulation results on multiple graph-based and Gazebo environments. We found that in many cases the priority-based greedy algorithm performs on par or better than the OP and PTP-based algorithms. We analyze the potential reasons for this counterintuitive result.
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Topological hotspot identification for informative path planning with a marine robot
In this work, we present a novel method for constructing a topological map of biological hotspots in an aquatic environment using a Fast Marching-based Voronoi segmentation. Using this topological map, we develop a closed form solution to the scheduling problem for any single path through the graph. Searching over the space of all paths allows us to compute a maximally informative path that traverses a subset of the hotspots, given some budget. Using a greedy-coverage algorithm we can then compute an informative path. We evaluate our method in a set of simulated trials, both with randomly generated environments and a real-world environment. In these trials, we show that our method produces a topological graph which more accurately captures features in the environment than standard thresholding techniques. Additionally, We show that our method can improve the performance of a greedy-coverage algorithm in the informative path planning problem by guiding it to different informative areas to help it escape from local maxima.
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- Award ID(s):
- 1723924
- PAR ID:
- 10064508
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation
- Page Range / eLocation ID:
- 4865-4872
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
