Informative Path Planning for Multi-Robot Systems

Informative path planning for multi-robot systems involves the design of motion strategies that maximise the acquisition of valuable sensory data while respecting resource and operational constraints. By integrating models of environmental phenomena with information-theoretic objectives, teams of robots autonomously coordinate to explore, monitor and map unknown or dynamic domains. Approaches typically represent the underlying field with statistical models such as Gaussian processes, quantifying the expected information gain along candidate trajectories via metrics like mutual information. Planning algorithms must solve NP-hard combinatorial optimisation problems, balancing trade-offs between exploration, exploitation and inter-robot coordination under constraints on communication, collision avoidance and energy. Recent advances span centralised optimisation heuristics, sequential and distributed allocation schemes, and sampling-based planners in continuous spaces, enabling scalable deployment on heterogeneous robot teams. Application areas range from environmental monitoring of soil moisture and aquatic ecosystems to infrastructure inspection and disaster response. The global significance of this field is underscored by its capacity to deliver rapid, cost-effective data acquisition with minimal human intervention, informing decisions in agriculture, conservation, public safety and beyond.

Research from Nature Portfolio

Recent studies have developed data-driven strategies for autonomous sensor model learning, enabling robots to collect training data without prior knowledge of sensor characteristics or environmental structure. By employing ergodic trajectory planning, agents are guided to sample regions proportionally to their information potential, accelerating the learning of predictive sensor models. This method reduces exploration energy costs and outperforms both random and classic information-maximisation schemes, offering a unified framework that could extend to multi-robot scenarios by distributing ergodic trajectories across collaborative platforms.

Research from all publishers

Foundational work introduced efficient approximation algorithms for multi-robot informative path planning, modelling spatial phenomena with Gaussian processes and maximising mutual information through a sequential allocation mechanism that extends single-robot guarantees to teams. More recent approaches have addressed continuous environments by partitioning unknown domains into Voronoi regions, dynamically balancing workload among robots while preserving map fidelity and minimising overlap. Complementary distributed methods incorporate sampling-based planning with information-theoretic utility functions, leveraging message-passing to satisfy communication and collision constraints, and enabling scalable coordination of multiple agents in complex, unstructured environments.

Informative Path Planning for Multi-Robot Systems publication trend

The graph below shows the total number of articles in informative path planning for multi-robot systems across all publications each year (not limited to Nature Index journals).

Technical terms

Informative Path Planning (IPP): The process of designing robot trajectories to maximise the expected information gained about an environment or phenomenon.

Gaussian Process (GP): A nonparametric Bayesian model used to represent spatial or spatio-temporal fields and predict unobserved values with quantified uncertainty.

Mutual Information: An information-theoretic measure of the expected reduction in uncertainty about a random field given observations along a path.

Voronoi Partitioning: A spatial decomposition that assigns regions to robots based on proximity, used to divide exploration tasks and balance coverage.

Ergodicity: A property of trajectories that ensures sampling time spent in each region is proportional to a desired spatial distribution, optimising information coverage.

References

1. Efficient Informative Sensing using Multiple Robots. Journal of Artificial Intelligence Research (2009).
2. Multi-robot informative path planning in unknown environments through continuous region partitioning. International Journal of Advanced Robotic Systems (2020).
3. Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints†. Sensors (2020).
4. Kriging‐based robotic exploration for soil moisture mapping using a cosmic‐ray sensor. Journal of Field Robotics (2019).
5. Adaptive Visual Information Gathering for Autonomous Exploration of Underwater Environments. IEEE Access (2021).