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This paper addresses the Informative Path Planning (IPP) algorithm for autonomous robots to explore unknown 2D environments for mapping purposes. IPP can be beneficial to many applications such as search and rescue and cave exploration, where mapping an unknown environment is necessary. Autonomous robots' limited operation time due to their finite battery necessitates an efficient IPP algorithm, however, it is challenging because autonomous robots may not have any information about the environment. In this paper, we formulate a mathematical structure of the IPP problem along with the derivation of the optimal control input. Then, a discretized model for the IPP algorithm is presented as a solution for exploring an unknown environment. The proposed approach provides relatively fast computation time while being applicable to broad robot and sensor platforms. Various simulation results are provided to show the performance of the proposed IPP algorithm.
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This content will become publicly available on November 19, 2026
Fully differentiable sensor placement and informative path planning
Sensor placement (SP) and informative path planning (IPP) problems are prevalent in environmental monitoring. These problems require gathering the most informative data from a limited number of sensing locations, but existing solutions face a difficult trade-off. Existing methods are often either computationally efficient but less informative, or more informative but too computationally expensive for practical use, especially on resource-constrained robots. Furthermore, many approaches are limited by requiring discretization of the environment or relying on slow, derivative-free optimization techniques. This paper introduces a novel, computationally efficient variational formulation for the SP problem. Our approach is differentiable with respect to the sensing locations, enabling fast gradient-based optimization in continuous spaces and delivering performance comparable to MI-based methods at a fraction of the computational cost. We establish our formulation as a special case of sparse Gaussian processes (SGPs). This connection allows us to generalize the method to solve the IPP problem for single and multi-robot systems, efficiently incorporating differentiable path constraints and diverse sensor types. The approach is validated through extensive benchmarks and field experiments with an autonomous surface vehicle (ASV) and an autonomous underwater vehicle (AUV). We also provide SGP-Tools—an open-source Python library—and a companion ROS 2 package for Ardupilot-based mobile robots.
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- Award ID(s):
- 2244424
- PAR ID:
- 10667170
- Publisher / Repository:
- Sage Journals
- Date Published:
- Journal Name:
- The International Journal of Robotics Research
- ISSN:
- 0278-3649
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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