Optimal planning for information acquisition

This paper presents an algorithm for active search where the goal is to calculate optimal trajectories for autonomous robots during data acquisition tasks. Formulating the problem as parameter estimation enables us to use Fisher information to create an explicit connection between robot dynamics and the informative regions of the search space. We use optimal control to automate design of trajectories that spend time in regions proportional to the probability of collecting informative data and use acquired data to update the probability closed-loop. Experimental and simulated results use a robotic electrosense platform to localize a feature in one-dimension. We demonstrate that this method is robust with respect to disturbances and initial conditions, and results in successful localization of the feature with a 100% experimental success rate and a 34% reduction in localization time compared to the next best tested controller.

Authors

• Yonatan Silverman
• Lauren M. Miller
• Malcolm A. MacIver
• Todd D. Murphey

Keywords

• Robot sensing systems
• Trajectory
• Measurement
• Search problems
• Entropy
• Simulation Results
• Optimal Control
• Search Algorithm
• Fisher Information
• Active Search
• Trajectory Optimization
• Search Strategy
• Random Variables
• Objective Function
• Active Control
• Expected Value
• Function Parameters
• Measurement Model
• Random Walk
• Control Efforts
• Parameters Of Interest
• Object Location
• Target Object
• Voltage Signal
• Electric Fish
• Minimum Entropy
• Sphere Of Radius
• Distractor Objects
• Electric Vector
• Continuous Trajectory