Is multibody planning always centralized?

No. Physical separation is the defining issue here. Central coordination is one arrangement; information and communication constraints may require decentralized execution or planning.

Is it the same as multiagent planning?

They overlap, but several bodies can act as one decision-making system. Separate agents may also have distinct knowledge, control, or objectives.

Does a good schedule prevent every collision?

No. Collision prevention requires an adequate spatial and temporal model and appropriate execution controls, not just a task list.

AI Glossary / Graphs and symbolic AI / Intermediate

Multibody planning

Definition

Multibody planning is planning for several physically separate bodies, such as a fleet of robots, whose actions must be coordinated to achieve an overall objective. In the AI planning sense used here, the bodies can be treated as parts of one decision-making system when relevant information can support a common world-state estimate and coordinated execution.

What it means

Several robots do not automatically constitute several independent decision makers. A shared planner may assign their tasks and coordinate their movements. The important questions are which actions each body can perform, how those actions interact, and what information is available when the plan is executed.
Physical separation introduces practical issues such as travel, communication, shared passages, and handovers. It also creates opportunities for parallel work. The term concerns planning across separate acting units; it should not be confused with multibody dynamics, which studies the physical motion and forces of systems containing multiple bodies.

How it works

Represent the state of each body and the relevant shared environment. Describe available actions, their prerequisites, duration, and effects. Search for a combination of assignments and action timings that reaches the objective while respecting resource and interaction constraints.
Some actions can overlap because they are independent. Others must be ordered because one enables another or because both require the same limited resource. Execution monitoring checks whether the assumed state still holds. If communication prevents the bodies from maintaining the information needed for coordinated action, decentralized planning and explicit communication policies become important. Merely writing a central schedule does not solve missing information during execution.

Three practical examples

Two warehouse robots can collect different containers in parallel while coordinating access to a single loading station.
An inspection fleet can divide coverage areas and arrange rendezvous points for transferring collected items or reconnecting to a shared controller.
A classroom robotics simulator can compare separate shortest paths with a coordinated plan that prevents two robots from occupying a narrow passage simultaneously.

Think of it this way

Think of one event organizer directing several runners between rooms. The runners are separate bodies, but the organizer coordinates a shared objective and their use of doors and collection points. The analogy has limits: robot planning may require precise geometry, timing, and uncertainty models that a human organizer handles informally.

Worked example

Consider an invented scheduling model with two robots, A and B, initially ready at separate pickup locations. A needs 3 minutes to collect its container; B needs 5 minutes. Each then needs 2 minutes at a shared loading station. Travel time is already included in collection, and the station can serve only one robot at a time.
A can collect during minutes 0-3 and use the station during 3-5. B collects during 0-5 and uses the station during 5-7. Both jobs finish at minute 7. The intervals meet at minute 5 but do not overlap; assume handover is instantaneous in this simplified model.
Performing all of A's work and then all of B's work takes 3 + 2 + 5 + 2 = 12 minutes. Coordinating parallel collection therefore gives a 7-minute feasible schedule under the assumptions. Seven is also a lower bound here because B alone requires 5 + 2 minutes from the common start. This proves optimality for this small scheduling model, not for a physical warehouse with unmodeled travel or collision constraints.

Illustrative case study

In a fictional library, two mobile carriers move returned books toward a sorting station. Separate task lists look efficient until both carriers repeatedly reach the station together. The planner had represented each carrier's route but omitted the station's exclusive capacity.
The team adds a shared resource constraint and tests coordinated schedules in a simulator. It also adds an execution check so a delayed carrier does not rely blindly on its original time slot. The case illustrates a modeling correction, not a reported productivity gain or a claim that scheduling alone establishes safe robot operation.

Common misconceptions and mistakes

Do not assume that individual feasibility implies joint feasibility. Two routes can each be valid when considered alone and conflict when executed together. Likewise, a common objective does not mean communication is perfect or observations are complete.
Another mistake is equating physical separation with strategic independence. Bodies managed by one planner differ from autonomous agents negotiating competing objectives. Conversely, treating independently controlled participants as obedient components can produce a plan they have no reason to follow. State clearly which control and cooperation assumptions apply.

Business use

For operations teams, multibody planning provides a way to reason about coordinated use of equipment, stations, and workspace. It can expose whether a bottleneck comes from assignment, timing, physical interaction, or information availability. This is more useful than assuming that adding another machine necessarily increases throughput.
Evaluate the complete execution arrangement. Include communication delays, action variability, recovery behavior, and shared-resource access in testing where they matter. Separate a high-level task schedule from the motion planning and control needed to carry it out. A credible business case needs measured performance under representative conditions rather than an idealized parallelism calculation alone.

Check your understanding

If B's collection takes 6 minutes while everything else in the worked example stays unchanged, when can both jobs finish?

Answer: A still uses the station during 3-5. B can use it during 6-8, so both finish at minute 8. The station is idle during 5-6 because B is not yet ready.

Related terms

Learn next

Next, add a one-minute station handover requirement to the example. A finishes loading at minute 5, the handover occupies 5-6, and B loads during 6-8. Explain why changing one resource assumption changes the plan even though both collection tasks are unchanged.

Sources

Russell and Norvig, Artificial Intelligence: A Modern Approach, multiagent planning discussion, including multieffector and multibody planning: https://people.engr.tamu.edu/guni/csce642/files/AI_Russell_Norvig.pdf . Original-author text was consulted through indexed passages; the complete PDF was not reviewed for this entry. The scheduling example and library case are original illustrations.

Previous: Multiband blending Next: Multiclass-multioutput classification