Dynamic Environment-driven Autonomous Drone Path Planning via Deep Reinforcement Learning

Path planning is a key enabling technology for drone-based applications, where the drone may encounter situations that require impromptu decisions to avoid task failure. Traditional works usually model it as an optimization problem with provable guarantees in a known map. However, they overlook the real-world uncertain dynamism (e.g. wind, illumination) stemming from the inadequate and inaccurate environmental information, which could pose risks to decision making and reduce the utility of path planning. To alleviate this uncertainty, we propose a Dynamic Environment-driven Path planning-oriented Deep Reinforcement Learning (DEP-DRL) paradigm that can be extended to more unfamiliar environments. We manage to do this by innovating in the following aspects: First, due to the influence of wind on the drone aerodynamic characteristics, we design a predictive drone state representation method which performs probabilistic wind-turbulence reasoning over drone kinematics in a deep reinforcement learning loop to conduct more realistic exploration. Then, we derive a system state transition mechanism for predicting future environmental observations and drone actions to minimize the potential system uncertainty and increase the path-planning success rate. We further propose an adaptive value updating strategy according to environmental illumination-based assessment in an online fashion. The experiments over different environments demonstrate the effectiveness of our method.