Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments

Selin, Magnus; Tiger, Mattias; Duberg, Daniel; Heintz, Fredrik; Jensfelt, Patric

doi:10.1109/lra.2019.2897343

Cited by 165 publications

(217 citation statements)

References 15 publications

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“…Subsequent rays only start where their lateral distance to the previous rays is s. This reduces redundant voxel checks in areas of high ray density, speeding up computation by an additional 233% in our experiments, while still detecting all voxels. As shown in [16], [23],…”

Section: A Iterative Ray Castingmentioning

confidence: 86%

“…5 shows the exploration progress of each method in the maze scenario. Even though different paths were taken in different experiments, our method converges to essentially full exploration of the observable map volume after 25 minutes in all runs, showing (a) RH-NBVP [1] (b) AEP [16] (c) Ours its global coverage capabilities. A visual analysis is given in Fig.…”

Section: A Volumetric Indoor Explorationmentioning

confidence: 93%

“…To escape local minima more efficiently, Witting et al [7] keep track of the planner's history as potential areas for reseeding the tree. Similarly, Selin et al [16] use the RH-NBV planner for local exploration and a frontier method for global goal selection.…”

Section: Related Workmentioning

confidence: 99%

“…The value function v(S) addresses the issue of how to connect gains and costs to arrive at a measure of total utility. Commonly used value formulations such as exponential v exp (S) [1], [6], [16] or linear v lin (S) [5], [11], [13] penalties, v exp (S) = v(parent(S)) + g(S) exp (−λc(S)), (7) v lin (S) = v(parent(S)) + g(S) − αc(S),…”

Section: B Global Normalization Valuementioning

confidence: 99%

See 3 more Smart Citations

An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

Schmid

Pantic

Khanna

et al. 2020

IEEE Robot. Autom. Lett.

194

161

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The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this paper, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires segments to maintain the tree and refine intermediate trajectories. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction onboard a Micro Aerial vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment solely with on-board sensing and computation. We make our framework available for future research.

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Section: A Iterative Ray Castingmentioning

confidence: 86%

Section: A Volumetric Indoor Explorationmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: B Global Normalization Valuementioning

confidence: 99%

See 2 more Smart Citations

An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

Schmid

Pantic

Khanna

et al. 2020

IEEE Robot. Autom. Lett.

194

161

View full text Add to dashboard Cite

show abstract

“…Several papers address and implement this strategy. For example, exploration planning strategy for large-scale unknown environments was proposed in [21], and the frontier point selection strategy, based on the frontier point optimisation and multistep path planning was proposed in [22].…”

Section: Environment Exploration Methodsologymentioning

confidence: 99%

Modelling of Autonomous Search and Rescue Missions by Interval-Valued Neutrosophic WASPAS Framework

Semėnas

Baušys

2020

Symmetry

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The application of autonomous robots in search and rescue missions represents a complex task which requires a robot to make robust decisions in unknown and dangerous environments. However, imprecise robot movements and small measurement errors obtained by robot sensors can have an impact on the autonomous environment exploration quality, and therefore, should be addressed while designing search and rescue (SAR) robots. In this paper, a novel frontier evaluation strategy is proposed, that address technical, economic, social, and environmental factors of the sustainable environment exploration process, and a new extension of the weighted aggregated sum product assessment (WASPAS) method, modelled under interval-valued neutrosophic sets (IVNS), is introduced for autonomous mobile robots. The general-purpose Pioneer 3-AT robot platform is applied in simulated search and rescue missions, and the conducted experimental assessment shows the proposed method efficiency in commercial and public-type building exploration. By addressing the estimated measurement errors in the initial data obtained by the robot sensors, the proposed decision-making framework provides additional reliability for comparing and ranking candidate frontiers. The interval-valued multi-criteria decision-making method combined with the proposed frontier evaluation strategy enables the robot to exhaustively explore and map smaller SAR mission environments as well as ensure robot safety and efficient energy consumption in relatively larger public-type building environments.

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Autonomous navigation of MAVs in unknown cluttered environments

Campos-Macías

Aldana‐López

Guardia

et al. 2020

Journal of Field Robotics

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Recently, there have been many advances in the algorithms required for autonomous navigation in unknown environments, such as mapping, collision avoidance, trajectory planning, and motion control. These components have been integrated into drones with high‐end computers and graphics processors. However, further development is required to enable compute‐constrained platforms with such autonomous navigation capabilities. To address this issue, in this paper, we present an autonomous navigation framework for reaching a goal in unknown three‐dimensional cluttered environments. The framework consists of three main components. The first component is a computationally efficient method for mapping the environment from the disparity measurements obtained from a depth sensor. The second component is a stochastic approach to generate a path to a given goal, taking into account the field of view constraints on the space that is assumed to be safe for navigation. The third method is a fast algorithm for the online generation of motion plans, taking into account the robot's dynamic constraints, model and environmental uncertainty, and disturbances. We provide a qualitative and quantitative comparison with existing reaching a goal and exploration methods, showing the superior performance of our approach. Additionally, we present indoors and outdoors experiments using a robotic platform based on the Intel Ready to Fly drone kit, which represents the implementation, in the most computational constrained platform, of autonomous navigation in unknown cluttered environments demonstrated to date. Open source code is available at: https://github.com/IntelLabs/autonomousmavs. The video of the experimental results can be found in https://youtu.be/79IFfQfvXLE.

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Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments

Cited by 165 publications

References 15 publications

An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

Modelling of Autonomous Search and Rescue Missions by Interval-Valued Neutrosophic WASPAS Framework

Autonomous navigation of MAVs in unknown cluttered environments

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