Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition

@article{Solovey2021FastNH,
  title={Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition},
  author={Kiril Solovey and Saptarshi Bandyopadhyay and Federico Rossi and Michael T. Wolf and Marco Pavone},
  journal={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2021},
  pages={9117-9123}
}
Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering significantly higher performance than single-robot systems. A fundamental building block in most multi-robot systems is task allocation: assigning robots to tasks (e.g., patrolling an area, or servicing a transportation request) as they appear based on the robots’ states to maximize reward. In many practical situations, the… 

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References

SHOWING 1-10 OF 48 REFERENCES
A comprehensive taxonomy for multi-robot task allocation
TLDR
A new, comprehensive taxonomy for task allocation in multi-robot systems is presented that explicitly takes into consideration the issues of interrelated utilities and constraints, and draws important parallels between robotics and these fields.
Task and Path Planning for Multi-Agent Pickup and Delivery
TLDR
Two novel offline Multi-Agent Pickup-and-Delivery algorithms are presented that improve a state-of-the-art online MAPD algorithm with respect to task planning, path planning, and deadlock avoidance for the offline MAPD problem.
Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics
TLDR
The combination of ILP model based algorithms and the heuristics proves to be highly effective, allowing the computation of 1.x-optimal solutions for problems containing hundreds of robots, densely populated in the environment, often in just seconds.
The dynamic team forming problem: Throughput and delay for unbiased policies
Min-Max Tours and Paths for Task Allocation to Heterogeneous Agents
TLDR
The heterogeneous agent path problem (HAPP) where agents can start from arbitrary locations and are not constrained to return to their start location is considered, and a 5-approximation algorithm is provided to solve this problem.
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
TLDR
Two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS) are presented and it is shown that they solve all well-formed MAPD instances, a realistic subclass ofMAPD instances.
Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs
TLDR
The NP-hardness proof for the time optimal versions of the discrete multi-robot path planning problem shows that these problems remain NP- hard even when there are only two groups of robots (i.e. robots within each group are interchangeable).
Efficient Routing for Precedence-Constrained Package Delivery for Heterogeneous Vehicles
TLDR
The package delivery optimization problem is shown to be NP-hard, which clearly shows the need for creative approximation algorithms to solve the problem and the constructed lower bound on the optimal time to serve all the customers helps to clarify for practitioners the limiting performance of a feasible solution.
Multi-Task Allocation and Path Planning for Cooperating UAVs
TLDR
An approach to the combined resource allocation and trajectory optimization aspects of the fleet coordination problem which calculates and communicates the key information that couples the two and permits some steps to be distributed between parallel processing platforms for faster solution.
Conflict-Based Search with Optimal Task Assignment
We consider a variant of the Multi-Agent Path-Finding problem that seeks both task assignments and collision-free paths for a set of agents navigating on a graph, while minimizing the sum of costs of
...
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2
3
4
5
...