Ergodic coverage in constrained environments using stochastic trajectory optimization

In search and surveillance applications in robotics, it is intuitive to spatially distribute robot trajectories with respect to the probability of locating targets in the domain. Ergodic coverage is one such approach to trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. In this work, we extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprint and avoid obstacles and restricted areas in the domain. We demonstrate that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain.

Authors

• Elif Ayvali
• Hadi Salman
• Howie Choset

Keywords

• Robot sensing systems
• Robot kinematics
• Trajectory optimization
• Cost function
• Entropy
• Stochastic Optimization
• Constrained Environments
• Stochastic Trajectory Optimization
• Target Domain
• Robotic Applications
• Domain Area
• Robot Trajectory
• Optimal Control
• Cross-entropy
• Parameter Space
• Nonlinear Dynamics
• Dirac Delta
• Multi-agent Systems
• Obstacle Avoidance
• Fourier Coefficients
• Trajectories In Space
• Second-order System
• Ultrasonic Sensors
• Rapidly-exploring Random Tree
• Number Of Basis Functions
• Bhattacharyya Distance
• Sample Trajectories
• Motion Primitives
• Robot State
• Distribution Of Trajectories
• Constraint In Eq
• Objective Function