Title: Dynamic Mission Planning for Multiple Mobile Robots
1Dynamic Mission Planning for Multiple Mobile
Robots
- Barry Brumitt and Anthony Stentz
- 26 Oct, 1999
- AMRS-99 Class Presentation
- Brian Chemel
2Overview
- Problem description
- Mission grammar
- System architecture GRAMMPS
- Brief digression D
- Results
- Analysis and limitations
3Problem Description Environment
- Dynamic, complex environment
- Typical situation
- World state initially unknown
- Runtime observations incorporated into shared
world model
4Problem Description Robots
- Multiple, heterogeneous mobile robots
- Assumptions
- Robots have relatively open workspaces, so
solution set is not too sparse - Effective positioning, communication and
perception are a given
5Problem Description Goals
- Robot task move to specified locations, in
specified order - Reconnaissance example
- Waypoints to be scouted by team of robots
- Warehouse example
- Multiple pickup points, to be followed by
multiple delivery points
6Problem Description GRAMMPS Planner
- Military reconnaissance task
- Outdoor environment
- Multiple robot vehicles (Navlab HMMWVs, in
practice) - Multiple distributed goals, with sequencing
- Requires mission grammar to pass parameters to
distributed planning system
7Mission Grammar
Expression Meaning
Do A, then do B
A OR B
A AND B
Robot i
Goal j
Move robot r to goal g
8Mission Grammar Example
- Move either robot 1 or robot 2 to goals 1, 2, 3,
and 4. Then move both robots 1 and 2 to goal 5.
9System Architecture Overview
Dynamic Planners (one per goal)
- Global (shared) dynamic planners
- Global (shared) mission planner
- Local (individual) plan execution
D
D
D
D
D
Mission Planner
Robot 1
Robot 2
Robot n
10System Architecture Local Navigators
Dynamic Planners (one per goal)
- Input
- Path to assigned goal
- Perception information
- Output
- Steering commands
D
D
D
D
D
Mission Planner
Robot 1
Robot 2
Robot n
11System Architecture Mission Planner
Dynamic Planners (one per goal)
- Input
- Estimated path costs for each (robot,goal) pair
- Output
- Mapping from robots to goals
- Algorithm
- TSP heuristic
D
D
D
D
D
Mission Planner
Robot 1
Robot 2
Robot n
12System ArchitectureDynamic Planners
Dynamic Planners (one per goal)
- Input
- Current world state knowledge
- Output
- Path to each goal for each robot
- Planning algorithm
- D
D
D
D
D
D
Mission Planner
Robot 1
Robot 2
Robot n
13Brief Digression The D Algorithm
- Modification of the A planning algorithm
- Provides efficient, optimal and complete path
planning in unknown, partially known, and
changing environments - As new information about the environment is
learned, cost information is propagated through
state space - Each time new information makes previous path
calculations obsolete, a new path is calculated - Original paper Stentz, ICRA 94
14Example Simulation Initial Plan
15Example Simulation Final Plan
- Goals re-assigned on the fly
- Mission successfully completed
16Highlights
- Demonstration implementation on Navlab HMMWVs
allows real-time, team-based mission planning in
dynamic environments - System scales gracefully up to large numbers of
robots and goals
17Limitations
- Waypoint task structure is very limiting
- No discussion of how to modify TSP approach to
allow heterogeneity - One robot must act as leader of the entire team
- When D fails, system can lock up
18Related Work
- Stentz, T. Optimal and Efficient Path Planning
for Partially-Known Environments. ICRA 94 - Brumitt, B., Stentz, T. GRAMMPS A Generalized
Mission Planner for Multiple Mobile Robots in
Unstructured Environments. ICRA 98 - Brumitt, B., Hebert, M. Experiments in
Autonomous Driving With Concurrent Goals and
Multiple Vehicles. ICRA 98