To provide a baseline solution for the performance evaluation of the proposed solution, we first develop a theoretical analysis for the minimum time‐energy path planning problem in a known environment using the Pontryagin's minimum principle. In this article, we develop a minimum time‐energy path planning solution in continuous state and control input spaces using integral reinforcement learning (IRL). Uncertain environments, such as wind conditions, pose challenges to the design of effective minimum time‐energy path planning solutions. For energy‐constrained robot platforms, path planning solutions are desired with minimum time arrivals and minimal energy consumption. Path planning is a fundamental and critical task in many robotic applications. The process is repeated as soon as a new information on the intruder's position becomes available. Then the tree based path planning is applied to generate safe path inside the safe region taking into account UAV dynamics. This trajectory separates the approved volume into safe and unsafe flight regions along the normal direction to its plane. The presented approach relies on an analytical solution of the differential game problem formulated for two point masses with constraints imposed on the velocities, and generates a safe trajectory on a plane defined by the UAS and non-cooperative airborne dynamic obstacle positions at the measurement instance, and the target way point. Sufficient conditions are derived based on the aerial vehicle's position and velocity, and intruder's position at the detection/estimation time instance, assuming that the intruder's maximum possible velocity is known. In the meantime, it has to stay as close to the given path as possible during the avoidance maneuvers.
The evading vehicle has to travel between given way points, while avoiding the collision and satisfying given constraints. In addition, it is assumed that the intruder's information is available at a much slower rate or not available at all for a some time intervals. It is assumed that the evading vehicle can determine its own state in real time, and estimate the intruder's largest size and center of mass position using on-board sensors. The goal is to generate safe trajectories inside a predefined volume, which is approved for the aerospace operations. The paper presents an approach to avoid non-cooperative airborne dynamic obstacles for unmanned aerial systems using pursuit-evasion differential game framework and tree-based path planning. In this paper the vehicle on-board architecture is described in details and the requirements to fly and interact with the STEReO system is discussed. The vehicle then uses on-board path planners and decision making algorithms for fire monitoring and mapping. The autonomous vehicle connects to the STEReO systems and gathers information of other operation in the vicinity. The simulation consists of a fire drill in the vicinity of Redding airport, one of the test sites for CAL-FIRE. We simulate a complete fire monitoring scenario in an high fidelity simulation environment. In this paper we describe a complete architecture of using UAVs for wild fire monitoring in this STEReO environment. This will facilitate the use of unmanned aerial vehicles in regions where UAVs are currently prohibited to fly. STEReO (Scalable Traffic Management for Emergency Response Operations) project at NASA Ames is designed to provide UTM (UAS Traffic Management) services to unmanned aerial vehicles (UAVs) used for natural disaster response scenarios like wildfire and hurricanes.
0 kommentar(er)
