Autonomous Sensor Path Planning and Control for Active Information Gathering
Sensor path planning and control refer to the problems of determining the trajectory and feedback control law that best support sensing objectives, such as monitoring, detection, classification, and tracking. Many autonomous systems developed, for example, to conduct environmental monitoring, search-and-rescue operations, demining, or surveillance, consist of a mobile vehicle instrumented with a suite of proprioceptive and exteroceptive sensors characterized by a bounded field-of-view (FOV) and a performance that is highly dependent on target and environmental conditions and, thus, on the vehicle position and orientation relative to the target and the environment. As a result, the sensor performance can be significantly improved by planning the vehicle motion and attitude in concert with the measurement sequence. This dissertation develops a general and systematic approach for deriving information-driven path planning and control methods that maximize the expected utility of the sensor measurements subject to the vehicle kinodynamic constraints.
The approach is used to develop three path planning and control methods: the information potential method (IP) for integrated path planning and control, the optimized coverage planning based on the Dirichlet process-Gaussian process (DP-GP) expected Kullback-Leibler (KL) divergence, and the optimized visibility planning for simultaneous target tracking and localization. The IP method is demonstrated on a benchmark problem, referred to as treasure hunt, in which an active vision sensor is mounted on a mobile unicycle platform and is deployed to classify stationary targets characterized by discrete random variables, in an obstacle-populated environment. In the IP method, an artificial potential function is generated from the expected conditional mutual information of the targets and is used to design a closed-loop switched controller. The information potential is also used to construct an information roadmap for escaping local minima. Theoretical analysis shows that the closed-loop robotic system is asymptotically stable and that an escaping path can be found when the robotic sensor is trapped in a local minimum. Numerical simulation results show that this method outperforms rapidly-exploring random trees and classical potential methods. The optimized coverage planning method maximizes the DP-GP expected KL divergence approximated by Monte Carlo integration in order to optimize the information value of a vision sensor deployed to track and model multiple moving targets. The variance of the KL approximation error is proven to decrease linearly with the inverse of the number of samples. This approach is demonstrated through a camera-intruder problem, in which the camera pan, tilt, and zoom variables are controlled to model multiple moving targets with unknown kinematics by nonparametric DP-GP mixture models. Numerical simulations as well as physical experiments show that the optimized coverage planning approach outperforms other applicable algorithms, such as methods based on mutual information, rule-based systems, and randomized planning. The third approach developed in this dissertation, referred to as optimized visibility motion planning, uses the output of an extended Kalman filter (EKF) algorithm to optimize the simultaneous tracking and localization performance of a robot equipped with proprioceptive and exteroceptive sensors, that is deployed to track a moving target in a global positioning system (GPS) denied environment.
Because active sensors with multiple modes can be modeled as a switched hierarchical system, the sensor path planning problem can be viewed as a hybrid optimal control problem involving both discrete and continuous state and control variables. For example, several authors have shown that a sensor with multiple modalities is a switched hybrid system that can be modeled by a hierarchical control architecture with components of mission planning, trajectory planning, and robot control. Then, the sensor performance can be represented by two Lagrangian functions, one function of the discrete state and control variables, and one function of the continuous state and control variables. Because information value functions are typically nonlinear, this dissertation also presents an adaptive dynamic programming approach for the model-free control of nonlinear switched systems (hybrid ADP), which is capable of learning the optimal continuous and discrete controllers online. The hybrid ADP approach is based on new recursive relationships derived in this dissertation and is proven to converge to the solution of the hybrid optimal control problem. Simulation results show that the hybrid ADP approach is capable of converging to the optimal controllers by minimizing the cost-to-go online based on a fully observable state vector.
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Rights for Collection: Duke Dissertations