RIS assisted energy aware multi agent adaptive SAC for UAV aided IoT network path planning and obstacle avoidance

Abstract

Abstract Unmanned aerial vehicles (UAVs) are emerging as critical enablers of next generation Internet of Things (IoT) infrastructures, supporting real-time data collection, wireless relaying, and agile operations in dynamic environments. However, achieving safe and energy efficient multi UAV navigation under sixth generation (6G) communication constraints remains a significant challenge due to dynamic obstacles, limited on board energy, and the high cost of centralized coordination. This study introduces a multi agent soft actor-critic (MASAC) framework for UAV path planning and energy aware coordination in a fixed RIS assisted IoT grid. MASAC integrates entropy regularized actor-critic learning with reconfigurable intelligent surface (RIS) aware reward shaping to support energy aware navigation, RIS assisted recharging, and connectivity guided trajectory optimization. A lightweight convolutional policy network is used to encode spatial information from the grid environment, including obstacle locations, dynamic obstacle states, exploration memory, and UAV position, enabling efficient policy learning under constrained navigation settings. Extensive simulations in RIS assisted, 6G enabled IoT environments demonstrate that MASAC achieves a 100% mission success rate, where mission success is defined as reaching the fixed goal cell before energy depletion and within the maximum episode horizon of 500 steps. Compared with the strongest baseline success rate of 75%, this corresponds to a 25%-point absolute improvement and a 33.3% relative improvement under the same evaluation protocol and identical environmental settings. Within the adopted grid level energy abstraction, MASAC also achieves approximately 33% higher RIS recharge utilization. It also provides 6% greater grid level 6G connectivity and 23% higher cumulative reward. Meanwhile, it maintains a low simulation time evaluation latency of approximately 38 ms per UAV. Statistical analysis confirms these gains as significant ( $$p < 0.05$$ ). The proposed framework offers a simulation level benchmark for energy efficient UAV navigation in RIS assisted IoT environments. It also supports future deployment oriented research under realistic operational constraints.