TARE: A Hierarchical Framework for Efficiently Exploring Complex 3D Environments

Cao, Chao; Zhu, Hongbiao; Choset, Howie; Ji, Zhang

doi:10.15607/rss.2021.xvii.018

Cited by 79 publications

(37 citation statements)

References 19 publications

(27 reference statements)

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“…Exploration algorithms are loosely characterized as information theoretic [6,11,19,34], which are seen as mathematically sound, frontier-based [7,17,20,28,37,40], which are computationally efficient, or graph search-based [8,12,13,38], which are scalable. Our pipeline is primarily frontier-based, but with a new frontier definition and selection heuristic, and also uses graph search for specific behaviors.…”

Section: Related Workmentioning

confidence: 99%

Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots

Best¹,

Garg²,

Keller³

et al. 2022

Robotics: Science and Systems XVIII

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We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard a team of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a new frontier-based exploration algorithm. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to 3 robots that fly up to 3 m/s and distances exceeding one kilometer. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. We provide an extended discussion of lessons learned, release software as open source, and present a video in the supplementary material.

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Section: Related Workmentioning

confidence: 99%

Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots

Best¹,

Garg²,

Keller³

et al. 2022

Robotics: Science and Systems XVIII

View full text Add to dashboard Cite

show abstract

“…The method [19] uses a combination of sampling and frontierbased method to reduce the impact of finding unexplored areas in large scenarios. There are also some two-stage methods [12], [13] to cover the entire environment efficiently by different planning strategies in the global and local map.…”

Section: Related Workmentioning

confidence: 99%

FAEP: Fast Autonomous Exploration Planner for UAV Equipped with Limited FOV Sensor

Zhao¹,

Li²,

Chen³

et al. 2022

Preprint

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Autonomous exploration is one of the important parts to achieve the autonomous operation of Unmanned Aerial Vehicles (UAVs). To improve the efficiency of the exploration process, a fast and autonomous exploration planner (FAEP) is proposed in this paper. We firstly design a novel frontiers exploration sequence generation method to obtain a more reasonable exploration path, which considers not only the flight-level but frontier-level factors into TSP. According to the exploration sequence and the distribution of frontiers, a two-stage heading planning strategy is proposed to cover more frontiers by heading change during an exploration journey. To improve the stability of path searching, a guided kinodynamic path searching based on a guiding path is devised. In addition, a dynamic start point selection method for replanning is also adopted to increase the fluency of flight. We present sufficient benchmark and real-world experiments. Experimental results show the superiority of the proposed exploration planner compared with typical and state-of-the-art methods.

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“…The resulting long re-planning times make them unpractical for time-constrained and fast-paced dynamic scenarios. These computational issues are partially addressed by [9], [10], but as the aforementioned methods, they simplify or do not consider the kinodynamic constraints of the robot.…”

Section: Introductionmentioning

confidence: 99%

Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning

Max¹,

Brito²,

Serra-Gómez³

et al. 2022

Preprint

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Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.

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TARE: A Hierarchical Framework for Efficiently Exploring Complex 3D Environments

Cited by 79 publications

References 19 publications

Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots

Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots

FAEP: Fast Autonomous Exploration Planner for UAV Equipped with Limited FOV Sensor

Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning

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