This paper studies the design of planetary aerobots with different types and shapes under various atmospheric conditions. The design framework and specifications are discussed. The development of a simulation tool is described, which is used for analyzing the behaviour of aerobots on Venus, Mars and Titan. The software is verified through the comparison of its performance with some experimental data as well as the state-of-the-art simulation tools. Based on the simulation results, some recommendations are made for different aerobot exploration missions

Zero-pressure balloon ascent prediction is a critical issue for Swedish Space Corporation(SSC) as targeted test ights are important part of SSC activities. This paper introducesa data-driven approach for estimating the balloon ascent. An output-constrained fuzzylogic approach is applied to the zero-pressure balloon data set from the past 10 years fromEsrange Space Center and rules are formulated that can express the balloon ascent. Theserules are veried by performing a root mean square analysis of estimated ascent againstthe real ascent trajectory. The result shows that the proposed fuzzy model is reliable andis able to estimate the balloon ascent with an acceptable accuracy.

This paper studies the specifications of balloons for the exploration of bodies with different atmospheric conditions. Three types of balloons, i.e., zero-pressure, super-pressure, and over-pressurized, with four different shapes, i.e., sphere, oblate, prolate, and airship, were analysed. First, the development of a simulation tool is described, which was used for analysing the behaviour of balloons for different exploration missions. Next, the developed software was verified by comparing its output with recorded data from a set of flights at the Esrange Space Center. Based on the simulation results, recommendations are given for different balloon types and shapes for operation on Mars, Venus, and Titan.

The horizontal motion of the balloon is governed by the winds at the float altitude. In order to navigate, and change the direction of balloon flight, knowledge of the wind environment around the balloon is needed. The real time navigation and control of zero-pressure balloons is a challenging task as there are no sensors that can be used onboard the balloon to provide real knowledge of the wind environment. Further, their is no active actuation possible and the resources available for passive actuation are limited. These constraints makes the balloon flight difficult and inflexible. In this paper, a solution to this problem of balloon navigation, and its path planning is presented by using data from ECMWF in combination with reinforcement learning. Data from ECMWF gives an overview of almost real-time environment and a reinforcement learning algorithm help in optimizing the passive actuation resources.