MPPI-based Informative Trajectory Planning for Search and Capture of Drifting Targets with ASVs

2026-06-10

AI Summary

This work addresses the challenge of efficiently searching for and capturing multiple persistently drifting targets—such as debris or distressed objects—in dynamic aquatic environments. The authors propose a Model Predictive Path Integral (MPPI) planning framework that integrates spatiotemporal information-theoretic metrics to unify exploration of unknown regions and tracking of known targets through long-horizon continuous trajectory optimization. During the interception phase, the system seamlessly transitions to a pure pursuit controller to achieve physical capture. A carefully designed multi-objective cost function effectively balances search and tracking priorities, establishing a complete closed-loop pipeline from planning to execution. Simulation results demonstrate superior performance over existing baselines, and real-world field experiments with an autonomous surface vehicle (ASV) in open water validate the system’s effectiveness and practical feasibility.

Abstract

Autonomous surface vehicles offer an efficient solution for environmental cleanup as well as search and rescue operations in open waters. Targets in these settings drift continuously, so efficient search must balance exploration of unobserved regions with tracking of known targets. However, most target tracking and pursuit scenarios consider simple guidance behaviours and short-term predictions for decision-making. In this letter, we address the problem of search and capture of multiple drifting targets, such as litter, in dynamic environments, using a hybrid planning framework. A key aspect of our strategy is a spatiotemporal informative planning method based on model predictive path integral (MPPI) control, a sampling-based model predictive control approach. The planner directly generates kinematic-level commands by optimising continuous trajectories over long horizons. A multi-objective cost balances search and tracking objectives while ensuring safe, feasible trajectories. In the interception stage, we switch to a pure pursuit guidance controller for the physical capture of moving targets. Experiments show that our planner outperforms the chosen planning baselines. Finally, we validate our approach in field trials with an ASV.

Problem

• drifting targets
• search and capture
• autonomous surface vehicles
• informative trajectory planning
• dynamic environments

Innovation

• MPPI
• informative planning
• autonomous surface vehicles
• drifting targets
• hybrid planning framework