EA-IPP2n: Energy-Aware Informative Path Planning Algorithm for UAVs
Ahmad Merei, Hamid Mcheick, Alia Ghaddar, Giovanni Beltrame · IEEE Access · 2026
Uncrewed Aerial Vehicles (UAVs) have become increasingly vital in various missions due to their ability to access remote and hazardous environments. Effective deployment of UAVs in time-sensitive and energy-constrained missions requires effective path planning. These strategies must maximize mission utility while operating within strict energy and time budgets. This paper introduces an Energy-Aware Informative Path Planning algorithm (EA-IPP2n) that functions as a pre-mission planner, generating the UAV’s flight path prior to deployment. The algorithm simultaneously accounts for both distance and turning angle costs. This enables the UAVs to plan efficient paths in terms of energy consumption and completion time in reward-driven environments. The method models the environment as a graph of nodes that represent locations of interest with associated rewards. EA-IPP2n employs a two-hop look-ahead mechanism, in which the planner evaluates pairs of consecutive moves before committing to the next step, to prioritize high-reward while accounting for both budgets. Simulations show improved performance over baseline methods, including Hierarchical Informative Path Planning (HIPP), Multiple-UAV Informative Path Planning and Mapping (MIPP), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Monte Carlo Tree Search (MCTS), and Greedy Search. EA-IPP2n achieved the highest total rewards (24.9 ± 3.6), exceeding the baseline algorithms by relative margins between 11% and 66%, while fully satisfying both time and energy constraints across all missions. It also achieves the highest mission performance score (0.380 ± 0.041), with an improvement of approximately 11% compared to the best-performing baseline, and relative gains between 11% and 57% across all evaluated algorithms. Furthermore, EA-IPP2n strictly respected both budgets in 100% of missions. While the time budget was enforced for all algorithms, only the enforced dual-budget formulation enabled HIPP and MIPP to satisfy both constraints in all cases. Greedy and GA violated the energy budget in all missions, and MCTS exceeded its limit in 40% of missions. In contrast, ACO, despite being heuristic and less informed, maintained full energy compliance. Thus, EA-IPP2n was the only method that achieved complete dual-budget compliance while maintaining the highest total reward performance.