OBBTree: a hierarchical structure for rapid interference detection
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Nonsmooth analysis and control theory
Nonsmooth analysis and control theory
Robot Motion Planning
A Comparison of Decision Making Criteria and Optimization Methods for Active Robotic Sensing
NMA '02 Revised Papers from the 5th International Conference on Numerical Methods and Applications
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Planning Algorithms
Simulation-based optimal sensor scheduling with application to observer trajectory planning
Automatica (Journal of IFAC)
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach
Proceedings of the 24th international conference on Machine learning
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Architectures for Cooperative Airborne Simultaneous Localisation and Mapping
Journal of Intelligent and Robotic Systems
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Maneuver-based motion planning for nonlinear systems with symmetries
IEEE Transactions on Robotics
Adaptive evolutionary planner/navigator for mobile robots
IEEE Transactions on Evolutionary Computation
Planning multiple paths with evolutionary speciation
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
This article considers the optimal estimation of the state of a dynamic observable using a mobile sensor. The main goal is to compute a sensor trajectory that minimizes the estimation error over a given time horizon taking into account uncertainties in the observable dynamics and sensing, and respecting the constraints of the workspace. The main contribution is a methodology for handling arbitrary dynamics, noise models, and environment constraints in a global optimization framework. It is based on sequential Monte Carlo methods and sampling-based motion planning. Three variance reduction techniques-utility sampling, shuffling, and pruning-based on importance sampling, are proposed to speed up convergence. The developed framework is applied to two typical scenarios: a simple vehicle operating in a planar polygonal obstacle environment and a simulated helicopter searching for a moving target in a 3-D terrain.