Wind-Aware Path Optimization for an Aerobot in the Atmosphere of Venus Using Genetic Algorithms

This paper presents a path optimization solution for an autonomous aerial robot (aerobot) in the windy atmosphere of Venus. The aircraft is required to travel from its current position to a goal position by following minimum energy paths. The approach proposed in this paper uses genetic algorithms, a heuristic search that, based on a population of initially feasible paths and a set of biologically inspired operations, finds a low-cost path. The proposed cost function accounts for energy expenditure, such as thrust or drag, and also energy accumulation, such as charging with solar panels and gains from potential energy (e.g., due to upward directional winds). Path feasibility is assured by computing local reachability regions based on the wind velocity and the maximum speed of the aerobot. The method is illustrated through a series of simulations that show our results as a function of the number of iterations and path population sizes. A comparison with a previous algorithm is also made.