LIPP: Load-Aware Informative Path Planning with Physical Sampling

2026-03-06

AI Summary

This study addresses the limitation of conventional informative path planning (C-IPP), which neglects the impact of sampling payload on energy consumption and thus struggles to maximize information gain under energy constraints. The authors propose, for the first time, a load-sensitive informative path planning (LIPP) framework that explicitly models the coupling between information gain and payload-dependent energy consumption. LIPP jointly optimizes the trajectory, visitation sequence, and number of samples collected. A mixed-integer quadratic program (MIQP) is formulated to unify traversal cost and uncertainty reduction objectives, and theoretical bounds are established to characterize the trade-off between path length and energy efficiency. Evaluated across 2,000 simulation scenarios, LIPP reduces to C-IPP at zero sampling quality and demonstrates significantly improved information-per-energy yield as sampling quality increases.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

In classical Informative Path Planning (C-IPP), robots are typically modeled as mobile sensors that acquire digital measurements such as images or radiation levels. In this model - since making a measurement leaves the robot's physical state unchanged - traversal costs are determined solely by the path taken. This is a natural assumption for many missions, but does not extend to settings involving physical sample collection, where each collected sample adds mass and increases the energy cost of all subsequent motion. As a result, IPP formulations that ignore this coupling between information gain and load-dependent traversal cost can produce plans that are distance-efficient but energy-suboptimal, collecting fewer samples and less data than the energy budget would permit. In this paper, we introduce Load-aware Informative Path Planning (LIPP ), a generalization of C-IPP that explicitly models this coupling and the resulting order-dependent traversal costs. We formulate LIPP as a Mixed-Integer Quadratic Program (MIQP) that jointly optimizes routing, visitation order, and per-location sampling count under an energy budget. We show that LIPP strictly generalizes C-IPP: as sample unit mass $\lambda \to 0$, the load-dependent energy model reduces exactly to the classical distance budget constraint, recovering C-IPP as a special case. We further derive theoretical bounds on the path-length increase of LIPP relative to C-IPP, characterizing the trade-off for improved energy efficiency. Finally, through extensive simulations across 2000 diverse mission scenarios, we demonstrate that LIPP matches the behavior of C-IPP at zero sample mass and progressively achieves higher uncertainty reduction per unit energy as sample mass increases.

Problem

• Informative Path Planning
• Physical Sampling
• Load-aware Planning
• Energy Budget
• Order-dependent Cost

Innovation

• Informative Path Planning
• Load-aware Planning
• Physical Sampling
• Mixed-Integer Quadratic Program
• Energy-aware Robotics