A Comparison of Coordinated Planning Methods for Cooperating Rovers

1. A Comparison of Coordinated Planning Methods for Cooperating Rovers Gregg Rabideau, Tara Estlin, Steve Chien, Anthony Barrett (JPL) AIAA Space Technology Conference September 1999

2. Abstract • Describe and evaluate 3 methods for coordinating multiple agents • Agents interact by working together on a common pool of goals, and by sharing resources • The 3 coordination methods are: centralized, centralized goal allocation with distributed planning, and contract net protocol • Comparison made in a geological science scenario where multiple rovers sample spectra of rocks on Mars

3. Introduction • Motivation: Missions that employ larger sets of robotic workers are being proposed to increase science return and enable new types of observations. These missions will require more autonomy • Three types of advantages for multiple rovers over single rover: force multiplication, simultaneous presence, and system redundancy

4. Multi vs. Single • Force Multiplication: Cooperative tasks – i.e. some tasks are done quicker with multiple robots • Simultaneous Presence: Coordinated tasks – i.e. tasks that are impossible for a single rover to perform • System Redundancy: Higher risk levels become acceptable, and rovers are more likely to survive long missions in harsh environments

5. Issues With Multiple Agents • Interfaces between agents: determines what activities can be planned for each rover • Communication bandwidth – determines how much each rover can share its plan • Group command and control – determines which rovers execute activities in plan • Onboard capabilities – limits independence of each rover

6. Baseline Scenario • 3 identical rovers + lander + orbiter • Science goals to classify rock-types • Objective is to divide goals between rovers so that each rover’s driving is minimized • Low priority goals can be deleted

7. ASPEN Planner • Fukunaga et al. 1997 • Modeling language • Figure 1 has example (page 3) • Conflicts are: unexpanded activities, unspecified parameter values, unsatisfied requirements, and violated constraints • Iterative repair (Zweben et al., 1994)

8. MTSP Heuristics • As certain types of conflicts are resolved, heuristics are used to guide the search into making decisions that will produce optimal schedules • Rovers are not required to return to start position – “path” instead of “tour” • Take unvisited locations and incrementally insert into an existing tour where it causes smallest increase in tour length. See Figure 2 (page 4)

9. Centralized Planning • All planning done on lander • Two heuristics used: (Page 4) • Advantages: Planning is conceptually simplified, allows having only one powerful processor • Disadvantages: monitoring execution and replanning is difficult and communication heavy, single point of failure

10. Distributed Planning with Central Goal Allocation • Central planner develops abstract plan and allocates goals. Each rover develops executable plan • Aggregate resources are divided equally among agents • Advantages: distributed planning, faster reaction time, less communication • Disadvantages: no way to re-assign goals, equal division of resources is not always ideal

11. Contract Net Protocol • Instance of CNP (Smith 1980) • Lander announces tasks to rovers, each rover bids for the tasks, and the tasks are awarded to rovers with best bids (page 6) • Advantages: fast reaction, low communication • Disadvantages: sub-optimal plans due to partitioning of shared resources (page 6)

12. Functional Comparison • Parallel processing • Communication comparison • Degree of autonomy with respect to replanning

13. Empirical Comparison • 10 rockscapes • 20 iterations on each rockscape • 12 goals per iteration • 10 trials per approach • Metrics: Number of goals achieved, avg. distance per goal, comp. Time to generate plans (sum and makespan) • Table 1 (page 7)

14. Related Work • Mostly behavioral approaches • GRAMMPS and MARS are exceptions