Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios

Abstract

This paper presents a path planner for multiple unmanned aerial vehicles (UAVs) based on evolutionary algorithms (EAs) for realistic scenarios. The paths returned by the algorithm fulfill and optimize multiple criteria that 1) are calculated based on the properties of real UAVs, terrains, radars, and missiles and 2) are structured in different levels of priority according to the selected mission. The paths of all the UAVs are obtained with the multiple coordinated agents coevolution EA (MCACEA), which is a general framework that uses an EA per agent (i.e., UAV) that share their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This planner works offline and online by means of recalculating parts of the original path to avoid unexpected risks while the UAV is flying. Its search space and computation time have been reduced using some special operators in the EAs. The successful results of the paths obtained in multiple scenarios, which are statistically analyzed in the paper, and tested against a simulator that incorporates complex models of the UAVs, radars, and missiles, make us believe that this planner could be used for real-flight missions.Index Terms-Aerial robotics, multiobjective evolutionary algorithms (EAs), path planning for multiple mobile robot systems. I. INTRODUCTIONU NMANNED aerial vehicles (UAVs) are aircrafts without onboard pilots that can be remotely controlled or can fly autonomously based on preprogrammed flight plans [1]. The autonomy level achieved by the UAVs depends on the methodology used to control the vehicle and to generate its routes. Although both tasks are equally important (and become especially Manuscript

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

“…Furthermore, connections that create an angle greater than the critical turning or pitching angles of the UAV described in Section 3 were also omitted. The field strengths for obstacles are computed using (15) to (17). The field strengths for time and energy consumption are computed on demand as they depend on the nodal approach direction, wind conditions and other flight characteristics.…”

“…Unfortunately, the research is performed in 2D and does not include other objectives such as fuel or risk. Besada-Portas et al presented an evolutionary algorithm-based on-line planner for multiple UAVs in 3D that includes optimization of multiple mission objectives such as path length, fuel and several flight risks, but has no mention of wind [15]. McManus developed a wavefront expansion type planner with multiple objectives (distance travelled, time taken, fuel consumed) for a UAV in 3D [16].…”

“…Besada-Portas et al presented an evolutionary algorithm-based on-line planner for multiple UAVs in 3D that includes optimization of multiple mission objectives such as path length, fuel and several flight risks, but has no mention of wind [15]. To address the growing complexity of problems with increased constraints such as multiple UAVs, parallel evolutionary algorithms have been implemented to decrease the execution time [19].…”

“…More recently, biologically-inspired tau-based path planning strategies such as the decentralized receding horizon optimization in [20] were developed to include velocities and time dependencies in the trajectory generation. Many path planners including evolutionary algorithms are based on uniform WVFs [21][22][23][24][25][26] or constant vehicle velocities [6,8,15,[27][28][29][30]. The few planners that account for variable wind conditions simplify the environment to two dimensions by assuming a fixed cruise altitude [6,27,28] or assume that the WVF is horizontally planar [5,31,32] with coarse resolution discretization of the flight space (≥1 km).…”

Collision-free Multiple Unmanned Combat Aerial Vehicles Cooperative Trajectory Planning for Time-critical Missions using Differential Flatness Approach

“…The value is predetermined, and can be used to insure the first cost term consistent with others. The second term prd j J , describes the average detection-probability of the n r -radar system to UCAV j , where d (, ) j P tr is the radar detection probability model between the trajectory point of UCAV j at the time t and the r th radar 9 . And the third term dest j J , is the minimal form of the target damage probability, where…”