Multi-Objective Sparse Sensing with Ergodic Optimization

We consider a search problem where a robot has one or more types of sensors, each suited to detecting different types of targets or target information. Often, information in the form of a distribution of possible target locations, or locations of interest, may be available to guide the search. When multiple types of information exist, then a distribution for each type of information must also exist, thereby making the search problem that uses these distributions to guide the search a multi-objective one. In this paper, we consider a multi-objective search problem when the “cost” to use a sensor is limited. To this end, we leverage the ergodic metric, which drives agents to spend time in regions proportional to the expected amount of information there. We define the multi-objective sparse sensing ergodic (MO-SS-E) metric in order to optimize when and where each sensor measurement should be taken while planning trajectories that balance the multiple objectives. We observe that our approach maintains coverage performance as the number of samples taken considerably degrades. Further empirical results on different multi-agent problem setups demonstrate the applicability of our approach for both homogeneous and heterogeneous multi-agent teams.

Authors

• Ananya Rao
• Howie Choset

Keywords

• Measurement
• Sensor fusion
• Robot sensing systems
• Search problems
• Sensor systems
• Sensors
• Trajectory
• Types Of Information
• Multi-objective Optimization
• Multiple Objects
• Sensor Measurements
• Search Problem
• Coverage Performance
• Real-world Data
• Single Agent
• Digital Elevation Model
• Decision Variables
• Dirac Delta
• Multiple Sensors
• Trajectory Optimization
• Multiple Mapping
• Weighting Scheme
• Fourier Coefficients
• Coverage Problem
• Mixed-integer Programming Problem
• Map Objects
• Choropleth Maps
• Sparse Optimization
• Trajectories Of Agents
• Slope Map
• Sensor Suite
• Cuprite
• Integer Programming Problem
• Planetary Exploration