11 accepted papers
In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfy…
This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These mot…
Multirotor unmanned aerial vehicles (UAVs) are rapidly gaining popularity for many applications. However, safe operation in partially unknown, unstructured environments remains an open question. In this paper, we present a continuous-time trajectory optimization method for real-time collision avoida…
This paper reports on a data-driven motion planning approach for interaction-aware, socially-compliant robot navigation among human agents. Autonomous mobile robots navigating in workspaces shared with human agents require motion planning techniques providing seamless integration and smooth navigati…
This paper reports on an algorithm to support autonomous vehicles in reasoning about occluded regions of their environment to make safe, reliable decisions. In autonomous driving scenarios, other traffic participants are often occluded from sensor measurements by buildings or large vehicles like bus…
This paper reports on the inclusion of a probabilistic channel model within a cooperative localization planning framework. Underwater cooperative localization reduces positioning errors by sharing sensor data across a team of underwater vehicles. Relative range constraints between vehicles are measu…
This paper reports on a system for dynamic obstacle tracking for autonomous vehicles. In this work, we seek to simultaneously estimate both the trajectory of the obstacle and the obstacle's shape. These two tasks are inherently coupled-given only noisy partial views, one cannot accurately estimate t…
Real-world autonomous driving in city traffic must cope with dynamic environments including other agents with uncertain intentions. This poses a challenging decision-making problem, e.g., deciding when to perform a passing maneuver or how to safely merge into traffic. Previous work in the literature…
To operate reliably in real-world traffic, an autonomous car must evaluate the consequences of its potential actions by anticipating the uncertain intentions of other traffic participants. This paper presents an integrated behavioral inference and decision-making approach that models vehicle behavio…
We present a framework for planning collision-free paths online for autonomous underwater vehicles (AUVs) in unknown environments. It is composed of three main modules (mapping, planning and mission handler) that incrementally explore the environment while solving start-to-goal queries. We use an oc…
This paper reports on a Gaussian belief-space planning formulation for mobile robots that includes random measurement acquisition variables that model whether or not each measurement is actually acquired. We show that maintaining the stochasticity of these variables in the planning formulation leads…