SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain

Tao, Yuezhan; Wu, Yue; Li, Beiming; Cladera, Fernando; Zhou, Alex; Thakur, Dinesh S.; Kumar, Vijay

doi:10.1109/icra48891.2023.10160295

Cited by 17 publications

(4 citation statements)

References 31 publications

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“…Meanwhile, ECHO (Efficient Heuristic Viewpoint) [17] uses a 2D Gaussian distribution sampler to quickly sample viewpoints and considers constraints such as boundaries, the distance of viewpoints from obstacles, etc. Also based on FUEL, SEER (Safe Efficient Exploration) [9] uses OPNet to predict 3D maps of frontier regions in order to compute an information gain value closer to that in the real environment and simultaneously detect frontier regions that may enable preferential access.…”

Section: Frontier-based Methodsmentioning

confidence: 99%

“…To alleviate this problem, the best candidate target is chosen by either satisfying the dynamic constraints [6] or solving a traveling salesman problem (TSP) [7]. To further improve the global nature of the exploration algorithms, a small subset of the algorithms introduce a priori information about the environment while selecting the next target, e.g., detecting small frontier regions based on known maps to preferentially cover them [8], and predicting environmental maps in real time using deep learning to more accurately calculate information gain [9]. Meanwhile, the algorithms based on exploratory maps are naturally incapable of properly evaluating small frontier clusters as they lack a global view of environmental contour information, which is essential to characterize the importance of small frontier clusters.…”

Section: Introductionmentioning

confidence: 99%

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LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

Hou,

Pan,

et al. 2024

Drones

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In UAV autonomous exploration, large frontier clusters are commonly associated with high information gain and are visited first. In contrast, small and isolated frontier clusters with fewer frontiers are associated with smaller information gain and are thus explored with low priority. However, these small and isolated frontier clusters are often in close proximity to UAVs and surrounded by explored areas, which could result in back-and-forth flights that lower exploration efficiency. This paper proposes LAEA, a LiDAR-assisted and depth camera-dominated UAV exploration algorithm that aims to improve UAV autonomous exploration efficiency. A hybrid map is obtained that characterizes rich environmental profile information in real time, enabling us to detect small and isolated frontier clusters that can lead to repeated visits to explored areas. An environmental information gain optimization strategy is incorporated such that frontier clusters with larger unexplored areas behind them, as well as small and isolated frontier clusters close to the UAV, are assigned higher weights to prioritize their visit order. An optimized flight trajectory is generated to cover unexplored frontier clusters in the immediate vicinity of the UAV while flying to the next target. A comprehensive comparison between the proposed algorithm and state-of-the-art algorithms was conducted via a simulation study, which showed that our algorithm exhibits superior exploration efficiency in various environments. Experiments were also carried out to verify the feasibility of the proposed approach in real-world scenarios.

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Section: Frontier-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

Hou,

Pan,

et al. 2024

Drones

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“…The local path planning module aims to find the best viewpoints to make the robot to follow. Many previous works refine a path by evaluating the cost and reward for efficient exploration, but they consume significant computational resources in information evaluation [1,30]. Thus, we define the potential reward of a candidate viewpoint as a volume of unknown space within its a Field of View (FoV) and propose a simple and fast reward evaluation method based on incremental frontier information structure (FIS).…”

Section: Local Path Planningmentioning

confidence: 99%

Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles

Cao,

Du,

Yang

2024

Drones

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Robot autonomous exploration is a challenging and valuable research field that has attracted widespread research interest in recent years. However, existing methods often encounter problems such as incomplete exploration, repeated exploration paths, and low exploration efficiency when facing large-scale scenes. Considering that many indoor and outdoor scenes usually have a prior topological map, such as road navigation maps, satellite road network maps, indoor computer-aided design (CAD) maps, etc., this paper incorporated this information into the autonomous exploration framework and proposed an innovative topological map-based autonomous exploration method for large-scale scenes. The key idea of the proposed method is to plan exploration paths with long-term benefits by tightly merging the information between robot-collected and prior topological maps. The exploration path follows a global exploration strategy but prioritizes exploring scenes outside the prior information, thereby preventing the robot from revisiting explored areas and avoiding the duplication of any effort. Furthermore, to improve the stability of exploration efficiency, the exploration path is further refined by assessing the cost and reward of each candidate viewpoint through a fast method. Simulation experimental results demonstrated that the proposed method outperforms state-of-the-art autonomous exploration methods in efficiency and stability and is more suitable for exploration in large-scale scenes. Real-world experimentation has also proven the effectiveness of our proposed method.

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“…While Choudhury et al (2017) present an imitation learning approach where an agent imitates an "information-gathering" planner with full information about the world map, other works in Niroui et al (2019); Chen et al (2020); Zhu et al (2018) train reinforcement learning agents to output the next frontiers to visit for autonomous exploration. Furthermore, the works in Tao et al (2023); Georgakis et al (2022) use neural networks to predict the occupancy maps and calculate the informative trajectories to reduce the uncertainties of the map.…”

Section: Visually-attentive Navigationmentioning

confidence: 99%

Uncertainty-aware visually-attentive navigation using deep neural networks

Nguyen,

Andersen,

Boukas

et al. 2023

The International Journal of Robotics Research

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Autonomous navigation and information gathering in challenging environments are demanding since the robot’s sensors may be susceptible to non-negligible noise, its localization and mapping may be subject to significant uncertainty and drift, and performing collision-checking or evaluating utility functions using a map often requires high computational costs. We propose a learning-based method to efficiently tackle this problem without relying on a map of the environment or the robot’s position. Our method utilizes a Collision Prediction Network (CPN) for predicting the collision scores of a set of action sequences, and an Information gain Prediction Network (IPN) for estimating their associated information gain. Both networks assume access to a) the depth image (CPN) or the depth image and the detection mask from any visual method (IPN), b) the robot’s partial state (including its linear velocities, z-axis angular velocity, and roll/pitch angles), and c) a library of action sequences. Specifically, the CPN accounts for the estimation uncertainty of the robot’s partial state and the neural network’s epistemic uncertainty by using the Unscented Transform and an ensemble of neural networks. The outputs of the networks are combined with a goal vector to identify the next-best-action sequence. Simulation studies demonstrate the method’s robustness against noisy robot velocity estimates and depth images, alongside its advantages compared to state-of-the-art methods and baselines in (visually-attentive) navigation tasks. Lastly, multiple real-world experiments are presented, including safe flights at 2.5 m/s in a cluttered corridor, and missions inside a dense forest alongside visually-attentive navigation in industrial and university buildings.

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SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain

Cited by 17 publications

References 31 publications

LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles

Uncertainty-aware visually-attentive navigation using deep neural networks

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