A Comparison of
Coordinated Planning
Methods for
Cooperating Rovers
Gregg Rabideau, Tara Estlin,
Steve Chien, Anthony Barrett
(JPL)
AIAA Space Technology Conference
September 1999
Abstract
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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
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
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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
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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
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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
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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)
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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)
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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
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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
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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)
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Functional Comparison
Parallel processing
 Communication comparison
 Degree of autonomy with respect to
replanning
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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)
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Related Work
Mostly behavioral approaches
 GRAMMPS and MARS are exceptions
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