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Accompanying code repository for the paper:

S. Choudhury, J. K. Gupta, M. J. Kochenderfer, D. Sadigh, and J. Bohg. "Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints" Robotics: Science and Systems 2020

In this paper, we present a hierarchical multi-robot allocation algorithm that plans under task completion uncertainty and time window constraints. We call our algorithm SCoBA (Stochastic Conflict-Based Allocation). The lower layer generates single-robot allocation policies with dynamic programming and tree search, while the upper layer uses optimal conflict resolution logic to obtain a valid multi-agent allocation. Please see the extended version on Arxiv for the details or the YouTube Video for an overview.


The GIF below visualizes SCoBA on one of the scenarios from the Multi-Drone Delivery domain, in North San Francisco. The scenario has 5 depots and 30 drones, and the drones allocated to a stream of incoming delivery requests with probability 0.5; details in Section V (Evaluation) of the paper. Delivery locations (gray squares) are randomly generated within a bounding box, and some of them may be slightly offshore.

SF Example

Setup and Usage

Ensure that you have installed Julia from their website.

To the reproduce the experiments in the scripts/ folder, first clone this repository:

git clone SCoBA
cd SCoBA

First instantiate the subproject in scripts/ folder:

julia --project=scripts -e 'using Pkg; Pkg.instantiate()'

Now you can run any scripts in scripts/ folder. For example:

julia --project=scripts scripts/benchmark_routing.jl -n 10 -t 100 -d 15 -e 5 -p 0.5 -w 30 scoba

If you just wish to use the algorithm or domains, from the Julia Pkg mode REPL:

pkg> add 
pkg> add

There are 4 scripts in the scripts/ folder, for the two kinds of evaluations on each of the two domains in the paper. The scripts/benchmark_* scripts are for the unsuccessful task penalty (for all baselines) and the scripts/scaling_* scripts are for the computation time results for the subset of baselines we show in the paper. Each script has an example comment at the end for how it should be called. The scripts should approximately reproduce the corresponding results in the paper, with possible variations in computation time due to versions and hardware. Please open an issue if you have any issues re-running anything.

N.B Running benchmark_conveyor_belt with QLearning (for the saved policy) requires the precomputed policy files. We did not upload them to Github; please open an issue if you need them.

Code Overview

The two sub-folders in src/, i.e., solver/ and domains/ contain the domain-agnostic SCoBA algorithm and the domain-dependent implementations respectively. If you want to implement SCoBA on your own domain, you can just import the solver through using SCoBA.SCoBASolver and refer to the domains/ sub-folders for examples of how to use it.

Within src/solver/, the following are the summaries of the relevant files:

  • solver/types.jl Defines the various domain-agnostic datastructures required by SCoBA (to represent the policy tree, time windows, and so on). The key thing to look at if you want to use SCoBA on another domain is the templated GenericAllocation structure, which it uses to track the assignment of agents to tasks.
  • solver/scoba_tree_search.jl Implements the lower layer of SCoBA, i.e., the per-agent policy tree search. If you want to use just the tree search in a custom manner (as in the conveyor belt domain; check domains/conveyor_belt_scoba.jl), then look at the generate_search_tree! and get_next_attempt_idx methods.
  • solver/scoba_conflict_resolution.jl Implements the higher layer of SCoBA, i.e. the multi-agent conflict resolution. The structure of the coordinate_allocation.jl is very similar to the implementation of the Conflict-Based Search Algorithm in MultiAgentPathFinding.


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