Persistent Multi-Robot Mapping in an Uncertain Environment

This paper proposes a method to deploy teams of robots with constrained energy capacities to persistently maintain a map of an uncertain environment. Typical occupancy map approaches assume a static world; however, we introduce a decay in confidence that degrades the occupancy probability of grid cells and promotes revisitation. Further, sections of the map whose occupancy differs between observations are visited more frequently, while unchanging areas are scheduled less frequently. While naive planning is intractable through the entire space of multi-agent spatio-temporal states, the proposed algorithm decouples planning such that constraints are resolved separately by solving tracTable subproblems. We evaluate this approach in simulation and show how the uncertainty of our world model is maintained below an acceptable threshold while the algorithm retains a tractable computation time.

Authors

• Derek Mitchell
• Nathan Michael

Keywords

• Robots
• Clustering algorithms
• Indexes
• Planning
• Computational modeling
• Sensors
• Heuristic algorithms
• Uncertain Environment
• Computation Time
• Grid Cells
• Energy Capacity
• Swarm Robotics
• Map Section
• Energy Consumption
• Travel Time
• Mutual Information
• Shortest Path
• Gaussian Process
• Path Planning
• Set Of Cells
• Optimal Cost
• Cluster Centroids
• Free Cells
• Dijkstra’s Algorithm
• Occupancy Grid
• Virtual Nodes
• Set Of Robots