ARENA: Adaptive Risk-aware and Energy-efficient NAvigation for Multi-Objective 3D Infrastructure Inspection with a UAV

TL;DR

The paper introduces ARENA, an adaptive multi-objective path planning method for UAVs that balances safety, energy, and time in complex 3D environments, improving autonomy and reliability.

Contribution

It proposes a novel online trajectory adaptation approach using 4D NURBS and a genetic algorithm, with a risk-aware voting system for dynamic decision-making.

Findings

Achieves over 95% coverage of Pareto front in simulations and tests.

Estimates power consumption with 14% mean error.

Demonstrates improved UAV autonomy in complex 3D missions.

Abstract

Autonomous robotic inspection missions require balancing multiple conflicting objectives while navigating near costly obstacles. Current multi-objective path planning (MOPP) methods struggle to adapt to evolving risks like localization errors, weather, battery state, and communication issues. This letter presents an Adaptive Risk-aware and Energy-efficient NAvigation (ARENA) MOPP approach for UAVs in complex 3D environments. Our method enables online trajectory adaptation by optimizing safety, time, and energy using 4D NURBS representation and a genetic-based algorithm to generate the Pareto front. A novel risk-aware voting algorithm ensures adaptivity. Simulations and real-world tests demonstrate the planner's ability to produce diverse, optimized trajectories covering 95% or more of the range defined by single-objective benchmarks and its ability to estimate power consumption with a mean error representing 14% of the full power range. The ARENA framework enhances UAV autonomy and reliability in critical, evolving 3D missions.