2 research outputs found
Dynamic-Aware Autonomous Exploration in Populated Environments
Autonomous exploration allows mobile robots to navigate in initially unknown
territories in order to build complete representations of the environments. In
many real-life applications, environments often contain dynamic obstacles which
can compromise the exploration process by temporarily blocking passages, narrow
paths, exits or entrances to other areas yet to be explored. In this work, we
formulate a novel exploration strategy capable of explicitly handling dynamic
obstacles, thus leading to complete and reliable exploration outcomes in
populated environments. We introduce the concept of dynamic frontiers to
represent unknown regions at the boundaries with dynamic obstacles together
with a cost function which allows the robot to make informed decisions about
when to revisit such frontiers. We evaluate the proposed strategy in
challenging simulated environments and show that it outperforms a
state-of-the-art baseline in these populated scenarios.Comment: 7 pages, 5 figures, Accepted to the 2021 IEEE International
Conference on Robotics and Automation (ICRA 2021
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap
Autonomous exploration requires robots to generate informative trajectories
iteratively. Although sampling-based methods are highly efficient in unmanned
aerial vehicle exploration, many of these methods do not effectively utilize
the sampled information from the previous planning iterations, leading to
redundant computation and longer exploration time. Also, few have explicitly
shown their exploration ability in dynamic environments even though they can
run real-time. To overcome these limitations, we propose a novel dynamic
exploration planner (DEP) for exploring unknown environments using incremental
sampling and Probabilistic Roadmap (PRM). In our sampling strategy, nodes are
added incrementally and distributed evenly in the explored region, yielding the
best viewpoints. To further shortening exploration time and ensuring safety,
our planner optimizes paths locally and refine them based on the Euclidean
Signed Distance Function (ESDF) map. Meanwhile, as the multi-query planner, PRM
allows the proposed planner to quickly search alternative paths to avoid
dynamic obstacles for safe exploration. Simulation experiments show that our
method safely explores dynamic environments and outperforms the benchmark
planners in terms of exploration time, path length, and computational time.Comment: 8 Pages, 9 Figures, and 5 Tables. Video Link:
https://youtu.be/ileyP4DRBjU. Github Link: https://github.com/Zhefan-Xu/DE