Efficient Online Multi-robot Exploration via Distributed Sequential Greedy Assignment

@inproceedings{Corah2017EfficientOM,
  title={Efficient Online Multi-robot Exploration via Distributed Sequential Greedy Assignment},
  author={Micah Corah and Nathan Michael},
  booktitle={Robotics: Science and Systems},
  year={2017}
}
This work addresses the problem of efficient online exploration and mapping using multi-robot teams via a distributed algorithm for planning for multi-robot exploration— distributed sequential greedy assignment (DSGA)—based on the sequential greedy assignment (SGA) algorithm. SGA permits bounds on suboptimality but requires all robots to plan in series. Rather than plan for robots sequentially as in SGA, DSGA assigns plans to subsets of robots during a fixed number of rounds. DSGA retains the… 

Figures and Tables from this paper

Distributed matroid-constrained submodular maximization for multi-robot exploration: theory and practice

Real-time simulation and experimental results for teams of quadrotors demonstrate online planning for multi-robot exploration and indicate that collision constraints have limited impacts on exploration performance.

Dec-MCTS: Decentralized planning for multi-robot active perception

This work proposes a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception and extends the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence.

A Versatile Multi-Robot Monte Carlo Tree Search Planner for On-Line Coverage Path Planning

The MCTS planner is shown to perform on par with the conventional Boustrophedon algorithm in simulations varying the number of robots and the density of obstacles in the map and suggests it is well suited to many multi-objective tasks that arise in mobile robotics.

Online planning for multi-robot active perception with self-organising maps

The proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons and demonstrate feasibility for the active perception task of observing a set of 3D objects.

Efficient Multi-robot Exploration via Multi-head Attention-based Cooperation Strategy

A novel approach - Multi-head Attention-based Multi-robot Exploration in Continuous Space (MAMECS) aimed at reducing the state space and automatically learning the cooperation strategies required for decentralized multi-ro robot exploration tasks in continuous space is proposed.

Maximum Information Bounds for Planning Active Sensing Trajectories

This work forms the Active Information Acquisition problem as a deterministic planning problem where algorithms like Dijkstra and $\mathrm{A}^{*}$ can produce optimal solutions and derives a consistent and admissible heuristic as a function of the sensor model which can be used in information acquisition tasks such as actively mapping static and moving targets in an environment with obstacles.

Distributed Submodular Maximization on Partition Matroids for Planning on Large Sensor Networks

This work develops new tools for analysis of submodular maximization problems which are applied to design of randomized distributed planners that provide constant-factor suboptimality approaching that of standard sequential planners.

Volumetric Objectives for Multi-Robot Exploration of Three-Dimensional Environments

This work identifies connections between existing information-theoretic and coverage objectives in terms of expected coverage, and finds, perhaps surprisingly, that coverage objectives can significantly outperform information-based objectives in practice.

For Peer Review The Matroid Team Surviving Orienteers Problem and its Variants : Constrained Routing of Heterogeneous Teams with Risky Traversal

The objective for the MTSO problem has submodular structure, which leads to two polynomial time algorithms which are guaranteed to find a solution with value within a constant factor of the optimum, and can be applied to much broader classes of constraints while maintaining bounds on suboptimality.

Distributed Environmental Modeling and Adaptive Sampling for Multi-Robot Sensor Coverage

A distributed mixture of Gaussian Processes algorithm that enables robots to collaboratively learn the global density function by exchanging only model-related parameters is proposed.

References

SHOWING 1-10 OF 31 REFERENCES

Multi-robot decentralized belief space planning in unknown environments via efficient re-evaluation of impacted paths

  • Tali RegevV. Indelman
  • Computer Science
    2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2016
This work develops a framework to identify and efficiently update only those paths that are actually impacted as a result of an update in the announced path and employs insights from factor graph and incremental smoothing for efficient inference that is required for evaluating the utility of each impacted path.

Planning for robotic exploration based on forward simulation

Information-Theoretic Active Perception for Multi-Robot Teams

A unified estimation and control scheme based on Shannon’s mutual information that lets small teams of robots equipped with range-only sensors track a single static target, and a hierarchical planning approach which incorporates trajectory optimization so that robots can quickly determine feasible and locally optimal trajectories.

Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping

An information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner is proposed and reduces the time to explore an environment by 70% compared to a closest frontier exploration strategy and 57%Compared to an information-based strategy that uses global planning.

Decentralized active information acquisition: Theory and application to multi-robot SLAM

This paper provides a non-greedy centralized solution to the problem of controlling mobile sensing systems to improve the accuracy and efficiency of gathering information autonomously, and decentralizes the control task to obtain linear complexity in the number of sensors and provide suboptimality guarantees.

Efficient Informative Sensing using Multiple Robots

ESIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot, and a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-ro robot problem.

Approximate representations for multi-robot control policies that maximize mutual information

The main contributions of this paper include the control policy, an algorithm for approximating the belief state, and an extensive study of the performance of these algorithms using simulations and real world experiments in complex, indoor environments.

Decentralised Monte Carlo Tree Search for Active Perception

The practical performance of the proposed decentralised variant of Monte Carlo tree search for multi-robot active perception compares favourably to centralised MCTS even with severely degraded communication, and asymptotic convergence under reasonable assumptions is characterised.

Information-theoretic occupancy grid compression for high-speed information-based exploration

  • Erik NelsonNathan Michael
  • Computer Science
    2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2015
We propose information-theoretic strategies for Occupancy Grid (OG) compression to enable high-speed exploration on computationally constrained mobile robots. We first formulate optimal lossy

On mutual information-based control of range sensing robots for mapping applications

It is proved that any controller tasked to maximize a mutual information reward function is eventually attracted to unexplored space and provides an algorithmic implementation for computing mutual information and shows that its worst-case time and space complexities are quadratic and linear, respectively, with respect to the map’s spatial resolution.