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An adaptive multi-objective task splitting algorithm for agricultural multi-robot task allocation

Peng Chen et al.

IEEE Transactions on Evolutionary Computation2026https://doi.org/10.1109/tevc.2026.3659072article
AJG 4
Weight
0.37

What the paper says

In smart farming, the development of multi-robot systems is essential for improving harvesting efficiency and addressing labor shortages, yet achieving optimal coordination presents significant scheduling complexities. This paper investigates a critical multi-robot task allocation problem in the context of orchard harvesting, focusing on multi-objective optimization (makespan and energy consumption) while considering task divisibility, robot re-utilization for multiple routes, and load-dependent energy consumption. Balancing these conflicting objectives across transport and operations, while managing the immensely expanded decision space due to task splitting, poses severe challenges to algorithm efficiency. To deal with these issues, we propose an adaptive multi-objective task splitting algorithm (AMTSA), which employs a hybrid encoding method to clearly demonstrate robot-route assignments and task-splitting information. Regarding the search modes, AMTSA adopts an adaptive strategy: the early phase emphasizes diverse exploration through route structural optimization, while the later phase shifts computational resources towards in-depth task splitting optimization (TSO) to accelerate convergence. Furthermore, the TSO process integrates two coordinated splitting mechanisms to collaboratively optimize the spatial layout and temporal balance of the task allocation by targeting the bottleneck robot, thereby addressing multi-objective conflicts. The effectiveness and robustness of AMTSA have been validated through comparative experiments on one real-world case study and 15 newly constructed test instances. Generally, this research advances the theoretical comprehension of multi-robot coordination and provides practical insights relevant to agricultural automation.

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https://doi.org/https://doi.org/10.1109/tevc.2026.3659072

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@article{peng2026,
  title        = {{An adaptive multi-objective task splitting algorithm for agricultural multi-robot task allocation}},
  author       = {Peng Chen et al.},
  journal      = {IEEE Transactions on Evolutionary Computation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tevc.2026.3659072},
}

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An adaptive multi-objective task splitting algorithm for agricultural multi-robot task allocation

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.