Abstract
Field robotics missions often require physical samples to be returned to laboratories for analysis, making path planning inherently load-aware and order-dependent as accumulated samples increase payload and traversal energy costs. In single-robot Load-Aware Informative Path Planning (LIPP), this rigidly couples sensing with hauling: a solitary robot must transport every collected sample, forcing frequent depot returns that severely restrict its spatial coverage. Heterogeneous multi-robot teams can overcome this bottleneck by dividing labor---enabling high-precision samplers to collect while high-capacity carriers handle transport. However, this introduces a complex coordination challenge regarding when, where, what, and to whom handoffs should occur on top of the LIPP problem. To address this tightly coupled problem, we introduce Multi-Agent LIPP (MA-LIPP), which enables teams to cooperate through asynchronous "dead drops," allowing one robot to deposit samples for another to retrieve later without requiring synchronous rendezvous. We formulate MA-LIPP as an exact Mixed-Integer Quadratic Program (MIQP) alongside a scalable Pairwise Large-Neighborhood Search (LNS) heuristic for complex real-world applications. The heuristic matches exact optima in $95.5\%$ of certified cases and reduces weighted posterior variance by $16.1$--$19.8\%$ relative to a sequential baseline on larger instances of up to 12 robots, providing a robust framework for cooperative physical-sampling missions.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Kim, H., Shi, G., & Sukhatme, G. S. (2026). MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams. https://omanscience.com/en/articles/ma-lipp-cooperative-multi-agent-load-aware-informative-path-planning-for-heterogeneous-robot-teams
MLA 9
Kim, Hojune, et al. "MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams." https://omanscience.com/en/articles/ma-lipp-cooperative-multi-agent-load-aware-informative-path-planning-for-heterogeneous-robot-teams.
Chicago (author–date)
Kim, Hojune, Guangyao Shi, and Gaurav S. Sukhatme. 2026. "MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams." https://omanscience.com/en/articles/ma-lipp-cooperative-multi-agent-load-aware-informative-path-planning-for-heterogeneous-robot-teams.
Harvard
Kim, H., Shi, G. and Sukhatme, G. S. (2026) 'MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams', Available at: https://omanscience.com/en/articles/ma-lipp-cooperative-multi-agent-load-aware-informative-path-planning-for-heterogeneous-robot-teams.
Vancouver
Kim H, Shi G, Sukhatme GS. MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams. https://omanscience.com/en/articles/ma-lipp-cooperative-multi-agent-load-aware-informative-path-planning-for-heterogeneous-robot-teams
IEEE
H. Kim, G. Shi, and G. S. Sukhatme, "MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams," https://omanscience.com/en/articles/ma-lipp-cooperative-multi-agent-load-aware-informative-path-planning-for-heterogeneous-robot-teams.