Posts tagged Micah Corah
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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Active Estimation of Mass Properties for Safe Cooperative Lifting

This paper presents a method by which a team of aerial robots can lift an unknown object by learning about the mass distribution of the object while it is still on the ground. The robots are able to learn about the mass and center-of-mass of the object by applying forces at different locations and by tracking the force required to budge the object or else whether they are not able to do so. We then describe how to select highly informative interactions while simultaneously attaching robots in a configuration that is able to lift the object.

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

This paper provides an efficient and distributed algorithm for planning for multi-robot sensor coverage. Sensor coverage is ubiquitous in robotics and models, for example, the objects that robots will observe with their cameras as they move through an environment. Popular sequential algorithms for multi-robot coverage planning guarantee good performance but require increasing numbers of planning steps for large teams of robots. We propose a randomized algorithm that uses only a fixed number of planning steps and approaches the performance of sequential planning when dependencies between agents decrease with distance.

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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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