Communication-Efficient Planning and Mapping for Multi-Robot Exploration in Large Environments

Corah, Micah; O’Meadhra, Cormac; Goel, Kshitij; Michael, Nathan

doi:10.1109/lra.2019.2897368

Cited by 81 publications

(71 citation statements)

References 26 publications

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“…A trade-off between information gain and navigation cost was considered in the local planner. In another study [18], a Gaussian mixture model for global mapping to model complex environment geometries was proposed. A small memory footprint was maintained which enables distributed operation with a low volume of communication.…”

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.

252

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.

252

119

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“…Improving the phases described in Section 3 (pose identification, optimal goal selection and navigation and checking) can reduce the time needed to finish the procedure, shorten the distance traveled, or enhance the fidelity of the resulting map [ 88 , 89 , 90 ]. Thus, different techniques have been developed, which may focus on one stage or another.…”

Section: Optimization Trendsmentioning

confidence: 99%

Active Mapping and Robot Exploration: A Survey

Lluvia

Lazkano

Ansuategi

2021

Sensors

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Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.

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“…The approximation of the mutual information of range sensors [24] also led to the development of exploration approaches based on probabilistic occupancy maps with entropy reduction, such as decMCTS [25] or SGA [26]. Recently, in [27], [28], a finite-horizon decentralized planner (DGSA) has been designed using sampling-based Monte Carlo Tree Search (MCTS) [29]. The trajectories of the robots are assigned by solving a submodular maximization problem over matroid constraints with greedy assignment heuristics, for which polynomial-time algorithms and suboptimality bounds can be established [30].…”

Section: Related Workmentioning

confidence: 99%

Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs

Hardouin

Moras

Morbidi

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we propose a novel cluster-based Next-Best-View path planning algorithm to simultaneously explore and inspect large-scale unknown environments with multiple Unmanned Aerial Vehicles (UAVs). In the majority of existing informative path-planning methods, a volumetric criterion is used for the exploration of unknown areas, and the presence of surfaces is only taken into account indirectly. Unfortunately, this approach may lead to inaccurate 3D models, with no guarantee of global surface coverage. To perform accurate 3D reconstructions and minimize runtime, we extend our previous online planner based on TSDF (Truncated Signed Distance Function) mapping, to a fleet of UAVs. Sensor configurations to be visited are directly extracted from the map and assigned greedily to the aerial vehicles, in order to maximize the global utility at the fleet level. The performances of the proposed TSGA (TSP-Greedy Allocation) planner and of a nearest neighbor planner have been compared via realistic numerical experiments in two challenging environments (a power plant and the Statue of Liberty) with up to five quadrotor UAVs equipped with stereo cameras.

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Communication-Efficient Planning and Mapping for Multi-Robot Exploration in Large Environments

Cited by 81 publications

References 26 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

Active Mapping and Robot Exploration: A Survey

Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs

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