Posts in Multi-robot collaboration
Environment Model Adaptation for Mobile Robot Exploration

Many modern approaches to robotic exploration reason directly about the information that will be gained from future camera views. Although these approaches have been shown to be effective at reasoning about the effectiveness of exploration actions, evaluating information gain is computationally expensive. In this paper, we describe how to produce compressed representations of the environment that enable more efficient evaluation of information gain. We provide results in simulation and with a wheeled robot.

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Efficient Online Multi-robot Exploration via Distributed Sequential Greedy Assignment

This paper describes a distributed planning approach for multi-robot information gathering and application to robotic exploration. Unlike popular sequential planners, the proposed approach gains in efficiency by allowing robots to plan in parallel. We prove that the proposed planner approaches the performance guarantee for sequential planning if we select good sets of plans from robots planning in parallel and demonstrate that we are able to do so in the simulation results.

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Distributed Matroid-Constrained Submodular Maximization for Multi-Robot Exploration: Theory and Practice

This paper describes a distributed planning approach for multi-robot information gathering and application to robotic exploration. Unlike popular sequential planners, the proposed approach gains in efficiency by allowing robots to plan in parallel. We additionally extend this planning approach to consider the possibility of inter-robot collisions while avoiding deadlock and provide demonstrations in simulation and on a team of three quadrotor robots.

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Efficient Multi-Sensor Exploration Using Dependent Observations and Conditional Mutual Information

This paper presents a method to leverage conditionally dependent sensor observations for multi-modal exploration. A robot equipped with multiple sensors and potentially non-overlapping fields of view selects actions that drive the system to collect observations of occupied regions. The result is better coverage of the environment with the additional, dependent modality.

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Computationally Efficient Information-Theoretic Exploration of Pits and Caves

This paper presents a method to enable exploration in significantly three-dimensional and complex subterranean environments such as caves. A simulated aerial system maximizes information-theoretic measures to maximize coverage of the environment. Information-theoretic measures are also used to compress the environment representation and enable faster exploration performance.

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