Autonomous Robotic Exploration by Incremental Road Map Construction

Wang, Chaoqun; Chi, Wenzheng; Sun, Yuxiang; Meng, Max Q.-H.

doi:10.1109/tase.2019.2894748

Cited by 53 publications

(21 citation statements)

References 39 publications

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“…In [12], a priori knowledge-based dynamic object search strategy was proposed to improve the search efficiency of mobile robot in home environments. An incremental road-map based path planning strategy was developed in [13], where the feasible global path was further optimized with the proposed trajectory optimization method that considered the motion constraints of the robot. In [14], the traditional approach of frontier-based exploration and deep reinforcement learning were combined to allow a robot to autonomously explore unknown cluttered environments.…”

mentioning

confidence: 99%

Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning

Niu

Carrasco

et al. 2020

IEEE Trans. Veh. Technol.

249

119

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Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme.

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mentioning

confidence: 99%

Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning

Niu

Carrasco

et al. 2020

IEEE Trans. Veh. Technol.

249

119

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“…Efficient frontier detection algorithms based on graph search and the latest sensor readings are developed [40]. Samplingbased approaches have been also recently studied to efficiently find frontiers, e.g., rapidly exploring random trees (RRT) [41,42], RRT* [43], and incremental road map [44]. Storing maps with adaptive resolutions is another way of having a compact map representation.…”

Section: More Complex and Efficient Modelingmentioning

confidence: 99%

Exploration and Mapping with Groups of Robots: Recent Trends

2020

Curr Robot Rep

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Purpose of Review Multi-robot exploration-i.e., the problem of mapping unknown features of an environment-is fundamental in many tasks, including search and rescue, planetary exploration, and environmental monitoring. This article surveys recent literature with the aim at identifying current research trends, open challenges, and future directions. Recent Findings Since the first formalization of the exploration problem, current research has extended systems and algorithms to map 3D environments and more complex phenomena and considered real-world constraints. Deep learning-based approaches have seen some preliminary applications in decision-making. Summary While current research has been progressing towards systems that can work in the real world, long-term exploration in large unstructured environments with complex phenomena to map is still an open problem. This introduces opportunities for exciting research, including but not limited to generalized frameworks that bridge the gap between theory and practice, learningbased approaches, and robustness to failure and attacks, so that robots can be deployed safely in real-world environments.

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“…In our paper we assume that the robot knows its pose all the time: in reality, we achieved that by tracking the robot with an external camera system and forwarding the absolute positioning information to the robot through a narrowband communication channel; in simulation such configuration is emulated through the use of a supervisory functionality available in the simulator. However, this is not at all representing a limitation of the algorithm: such localization functionality could be easily replaced by a SLAM method for instance by constructing a graph-structured map along with an exploration process [51] or by leveraging vision-based navigation using signed distance fields [52]. As mentioned in Section I, the proposed algorithm focuses on the process of plume tracking while the plume finding strategy is implemented in a simple way: the robot will perform a crosswind scan, identify the highest concentration point, and start the particle source localization algorithm from inside the plume.…”

Section: Simulationmentioning

confidence: 99%

Particle Source Localization With a Low-Cost Robotic Sensor System: Algorithmic Design and Performance Evaluation

et al. 2020

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In this paper we tackle the problem of finding the source of particulate matter with a mobile robot equipped with a low-cost multi-channel optical particle counting sensor. The proposed method is based on the Infotaxis odor source localization algorithm and makes multiple modifications to adapt it to particle plumes. In particular, we propose three simple but efficient ways to fuse multiple probability maps associated with various particle sizes and use the resulted integrated map to guide the robot's movements. A refined measurement data collection is conducted in a wind tunnel to fit the particle plume model. The method with three proposed integration strategies is evaluated in simulation and in the wind tunnel emulating realistic environmental conditions in a repeatable fashion. In particular, we have investigated the impact of two environmental parameters-the wind speed and source release rate on the algorithm performance. The proposed algorithm with the weighted multi-modality map integration strategy outperforms the original Infotaxis and the other two variants. In high wind speed, the proposed algorithm is able on average to estimate the source location with less than 1 meter error in the 80 m 2 wind tunnel arena.

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Autonomous Robotic Exploration by Incremental Road Map Construction

Cited by 53 publications

References 39 publications

Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning

Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning

Exploration and Mapping with Groups of Robots: Recent Trends

Particle Source Localization With a Low-Cost Robotic Sensor System: Algorithmic Design and Performance Evaluation

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