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 measurements that can be made by mobile sensors. However, our work is unique in three ways. First, we show that by using Cauchy-Schwarz Quadratic Mutual Information (CSQMI), we get significant gains in efficiency. Second, while most previous methods adopt a myopic, gradient-following approach that yields poor convergence properties, our algorithm searches over a set of paths and is less susceptible to local minima. In doing so, we explicitly incorporate models of sensors, and model the dependence (and independence) of measurements over multiple time steps in a path. Third, because we consider models of sensing and mobility, our method naturally applies to both ground and aerial vehicles. The paper describes the basic models, the problem formulation and the algorithm, and demonstrates applications via simulation and experimentation.

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 Cauchy-Schwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradient-based trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot's motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach 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, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state.

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 multiple robots over long time horizons, a series of approximations enable all calculations to be performed in polynomial time. We demonstrate the tangible benefits of our approaches through a series of simulations in complex indoor environments.

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 robots, the time horizon associated with the policy, and typical non-parametric representations of the belief, we design approximate representations that enable real-time operation. 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.

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) to take advantage of that redundancy at the transport level. The resulting system that integrates robust routing and MCTP is evaluated in experiments performed in complex environments. The integrated system is observed to provide a robust architecture that allows for near lossless communication while operating in a complex environment with less traffic than standard confirmation protocols.

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 information between the robot's measurements and their current belief of the target position. We describe experimental results for a robot team localizing a stationary target in several representative indoor environments in which the unknown target is reliably localized with an error well below the typical error for individual measurements.

A method and apparatus are provided to permit location discovery, including location discovery in indoor settings. The method may identify a wireless fingerprint present at a geographical location and determine whether the wireless fingerprint corresponds to a previously observed wireless fingerprint associated with the predefined geographical location. A new wireless fingerprint and a geographical location, or bind, may be received by the system and the system may evaluate whether or not the new bind is erroneous or not, or if the new bind is an improvement over existing binds in determining whether or not to accept the new bind.

INFORMATION-THEORETIC ACTIVE PERCEPTION FOR MULTI-ROBOT TEAMS Benjamin Charrow Vijay Kumar Nathan Michael Multi-robot teams that intelligently gather information have the potential to transform industries as diverse as agriculture, space exploration, mining, environmental monitoring, search and rescue, and construction. Despite large amounts of research effort on active perception problems, there still remain significant challenges. In this thesis, we present a variety of information-theoretic control policies that enable teams of robots to efficiently estimate different quantities of interest. Although these policies are intractable in general, we develop a series of approximations that make them suitable for real time use. We begin by presenting 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. By creating approximate representations, we substantially reduce the complexity of this approach, letting the team track a mobile target. We then scale this approach to larger teams that need to localize a large and unknown number of targets. We also examine information-theoretic control policies to autonomously construct 3D maps with ground and aerial robots. By using Cauchy-Schwarz quadratic mutual information, we show substantial computational improvements over similar information-theoretic measures. To map environments faster, we adopt a hierarchical planning approach which incorporates trajectory optimization so that robots can quickly determine feasible and locally optimal trajectories. Finally, we present a high-level planning algorithm that enables heterogeneous robots to cooperatively construct maps.