Journal
ROBOTICS AND AUTONOMOUS SYSTEMS
Volume 54, Issue 11, Pages 898-910Publisher
ELSEVIER
DOI: 10.1016/j.robot.2006.05.008
Keywords
Nonlinear Model Predictive Control; simultaneous localization and map building (SLAM); target localization; Extended Kalman Filter (EKF); Extended Information Filter (EIF); optimization
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This paper considers trajectory planning problems for autonomous robots in information gathering tasks. The objective of the planning is to maximize the information gathered within a finite time horizon. It is assumed that either the Extended Kalman Filter (EKF) or the Extended Information Filter (EIF) is applied to estimate the features of interest and the information gathered is expressed by the covariance matrix, or information matrix. It is shown that the planning process can be formulated as an optimal control problem for a nonlinear control system with a gradually identified model. This naturally leads to the Model Predictive Control (MPC) planning strategy, which uses the updated knowledge about the model to solve a finite horizon optimal control problem at each time step and only executes the first control action. The proposed MPC framework is demonstrated through solutions to two challenging information gathering tasks: (1) Simultaneous planning, localization, and map building (SPLAM) and (2) Multi-robot Geolocation. It is shown that MPC can effectively deal with dynamic constraints, multiple robots/features and a range of objective functions. (C) 2006 Elsevier B.V. All rights reserved.
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