Search for: All records

This article focuses on enabling an aerial robot to fly through multiple openings at high speed using image-based estimation, planning, and control. State-of-the-art approaches assume that the robot’s global translational variables (e.g., position and velocity) can either be measured directly with external localization sensors or estimated onboard. Unfortunately, estimating the translational variables may be impractical because modeling errors and sensor noise can lead to poor performance. Furthermore, monocular-camera-based pose estimation techniques typically require a model of the gap (window) in order to handle the unknown scale. Herein, a new scheme for image-based estimation, aggressive-maneuvering trajectory generation, and motion control is developed for multi-rotor aerial robots. The approach described does not rely on measurement of the translational variables and does not require the model of the gap or window. First, the robot dynamics are expressed in terms of the image features that are invariant to rotation (invariant features). This step decouples the robot’s attitude and keeps the invariant features in the flat output space of the differentially flat system. Second, an optimal trajectory is efficiently generated in real time to obtain the dynamically-feasible trajectory for the invariant features. Finally, a controller is designed to enable real-time, image-based tracking of the trajectory. The performance of the estimation, planning, and control scheme is validated in simulations and through 80 successful experimental trials. Results show the ability to successfully fly through two narrow openings, where the estimation and planning computation and motion control from one opening to the next are performed in real time on the robot.

This article presents a new decentralized multi-agent information-theoretic (DeMAIT) control algorithm for mobile sensors (agents). The algorithm leverages Bayesian estimation and information-theoretic motion planning for efficient and effective estimation and localization of a target, such as a chemical gas leak. The algorithm consists of: (1) a non-parametric Bayesian estimator, (2) an information-theoretic trajectory planner that generates “informative trajectories” for agents to follow, and (3) a controller and collision avoidance algorithm to ensure that each agent follows its trajectory as closely as possible in a safe manner. Advances include the use of a new information-gain metric and its analytical gradient, which do not depend on an infinite series like prior information metrics. Dynamic programming and multi-threading techniques are applied to efficiently compute the mutual information to minimize measurement uncertainty. The estimation and motion planning processes also take into account the dynamics of the sensors and agents. Extensive simulations are conducted to compare the performance between the DeMAIT algorithm to a traditional raster-scanning method and a clustering method with coordination. The main hypothesis that the DeMAIT algorithm outperforms the other two methods is validated, specifically where the average localization success rate for the DeMAIT algorithm is (a) higher and (b) more robust to changes in the source location, robot team size, and search area size than the raster-scanning and clustering methods. Finally, outdoor field experiments are conducted using a team of custom-built aerial robots equipped with gas concentration sensors to demonstrate efficacy of the DeMAIT algorithm to estimate and find the source of a propane gas leak.