Benjamin Charrow

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Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and(More)
We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain(More)
We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particular item. Because direct feedback does not exist for events(More)
This paper describes the architecture and implementation of a distributed autonomous gardening system. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry(More)
This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and(More)
We address the problem of controlling a small team of robots to estimate the location of a mobile target using non-linear range-only sensors. Our control law maximizes the mutual information between the team’s estimate and future measurements over a finite time horizon. Because the computations associated with such policies scale poorly with the number of(More)
We develop a computationally efficient control policy for active perception that incorporates explicit models of sensing and mobility to build 3D maps with ground and aerial robots. Like previous work, our policy maximizes an information-theoretic objective function between the discrete occupancy belief distribution (e.g., voxel grid) and future(More)
We develop algorithms for estimation and control that allow a team of robots equipped with range sensors to localize an unknown target in a known but complex environment. We present an experimental model for radio-based time-of-flight range sensors. Adopting a Bayesian approach for estimation, we then develop a control law which maximizes the mutual(More)
Providing reliable end-to-end communication for teams of robots requires the integration of novel routing techniques, motion planning algorithms, and transport level communication protocols. In this paper we look at existing robust routing solutions that provide redundancy at the routing layer and develop the Multi-Confirmation Transmission Protocol (MCTP)(More)
This paper addresses the problem of actively controlling robotic teams with range-only sensors to a) discover and b) localize an unknown number of devices. We develop separate information based objectives to achieve both goals, and examine ways of combining them into a unified approach. Despite the computational complexity of calculating these policies for(More)
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