The design goal for the rovers was 600 meters of travel; in fact, they have traveled jointly more than 50 kilometers. After scouting around its rim, Opportunity drove down the steep walls into an impact crater called Victoria, then succeeded with some difficulty in climbing back out to resume its route (Figure 10.15). Dust covering the rovers’ solar cells caused a drop in power, but when a seasonal dust storm blew away the dust, the rovers resumed full operation. In order to survive winter, the rovers were positioned on slopes to maximize solar heating and power generation. In 2006, Spirit lost power on one of its wheels, and subsequently became stuck in the sand, where it continued operation as a fixed ground station. Meanwhile, in 2008, Phoenix (a spacecraft “reborn” of spare parts from a previous Mars mission that had failed) landed near the edge of the north polar cap, at latitude 68°, and directly measured water ice in the soil. In 2011, NASA launched its largest (and most expensive) Mars mission since Viking (see Figure 10.1).

A computational tool for conceptual design and optimization of planetary rovers The design process of a Mars rover is driven by multiple design constraints, namely overall mass, power consumption and volume (dimensions). Various systems, such as mobility, manipulation, handling, power, thermal, communication, navigation, avionics and science instruments, together make a complete rover vehicle and they should function collectively to perform a given task. Each of the subsystems can be thought of as modular building blocks that are integrated together to form a fully functional rover vehicle. When approaching the design of such a vehicle, the designer should take into account of cross design dependencies existent between different subsystems and technology limitations. Performing any particular task, would lead to many design possibilities. Choosing the final design from many feasible solutions is arguably a daunting task. In order to make this process simple and convenient, as well as to understand the design non-linearity existing in this solution space, the authors have employed a systems engineering approach to develop a tool comprising subsystem models. Aravind SEENI, Bernd SCHÄFER 86 INCAS BULLETIN, Volume 15, Issue 1/ 2023 Power module The power system modelled here is a photovoltaic system or solar arrays acting as the primary power source. The solar arrays must generate a sufficient amount of power to satisfy subsystem charging and battery recharging demands. Batteries supply energy during peak operations and during periods when there is no sunlight . Batteries should also maintain the temperature of rover systems during cold nights. Solar arrays consist of sola r cells that are available in different technologies. They are responsible for converting solar energy in to electricity through the photovoltaic effect. Here GaInP/GaAs/Ge triple junction cell technology with a conversion efficiency of 26.8% is assumed. The solar arrays are assumed inclinable or tiltable to suitable angles by means of actuators. This method enables the capability of tracking the sun to maintain maximum solar radiation reaching the cells during the entire sol. Likewise, the battery is assumed to be of Li-ion technology with an operational efficiency of 95%. Although solar cells suggest a primary element of the solar array, it may not contribute significantly to overall mass. The structure ( Marraystruct), cover glass, interconnects and substra te forms the significant portion of the overall array mass. The relationships for determining power subsystem mass is given as follows: 𝑀𝑀𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑒𝑒𝑡𝑡𝑐𝑐𝑡𝑡𝑒𝑒𝑡𝑡 = 6𝑚𝑚𝑐𝑐𝑒𝑒𝑒𝑒𝑒𝑒 ⋅ 𝐴𝐴𝑒𝑒𝑡𝑡 (8) 𝑀𝑀𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑎𝑎𝑒𝑒𝑡𝑡𝑡𝑡𝑎𝑎𝑐𝑐𝑡𝑡 = 0.0.82𝑀𝑀𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑒𝑒𝑡𝑡𝑐𝑐𝑡𝑡𝑒𝑒𝑡𝑡 (9) 𝑀𝑀𝑎𝑎𝑡𝑡𝑡𝑡 = Aravind SEENI, Bernd SCHÄFER 90 INCAS BULLETIN, Volume 15, Issue 1/ 2023 A case to design a rover concept that should carry scientific payloads of 50 kg in mass is considered to demonstrate the feasibility of the approach. Genetic Algorithm is chosen as the optimization technique because of the large number of design v ariables and serving the purpose of efficiently handling global optimization problems. GA works on the principle of life evolution process based on the Darwinian principle. Goldberg first introduced it as a meta- heuristic, numerical optimization technique [10]. In GA, a set of operations is performed on a population of encoded solutions known as individuals or chromosomes. Each possible solution is en coded as a set of genes. During each iteration (or generation), the individuals in the population undergo selection, crossover, mutation and fitness evaluation operations. The global search of design solutions followed in GA prevents convergence of solutions or trapped in the local optima. Unlike other optimization methods, GA does not require gradients or derivatives of the function to be minimized. Also it does not require initial guess values of the design variables. Only the boundary values to the design space are needed. GA varies the values of design variables over a number of generations until satisfying a set of criteria. In GA, the fitness function represents the objective functions and constraints. A higher fitness indicates better solution. In the fitness function, the constraints are handled such that unfeasible solutions are penalized by a penalty factor. With each GA generation, the fitness of the population is improved. The best solution selected is the individual with the best fitness at the end of the last generation. Mission requirements definition The model requires mission level inputs from the user that informs the environment and vehicle operational conditions on Mars. They help in understanding the soil type that is described by Bekker’s friction and cohesion properties [11] . Also the terrain conditions are specified to understand whether the rover should operate on slopes. Operational requirements define the nominal and peak power consumption of systems. Battery recharge duration available per sol is also specified. Temperature conditions to be maintained inside WEB should be also provided. Table 3 - Assumed operational requirements for one experimental cycle Nominal Peak Day power consumption per exp. cycle Communication (W) 30 255 Drilling (W) 45 80 Science (W) 21 21 Thermal control (W) 8 8 Night power consumption per exp. cycle Thermal control (Whr) 96 540 Operational hours per exp. Cycle Communication (hrs) 1 1 Drilling (hrs) 1 1 Science (hrs) 1 1 Thermal control (hrs) 1 1 Battery recharge plan: [day 1, day 2, ..,day 12] (hrs) [2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 3, 3] Temperature inside WEB (K) 293 Environmental conditions The mission specifications are as follows: The rover is expected to operate at 20° latitude. Areocentric longitude of Mars about Sun during landing is assumed to be 180°. The rover should be capable of climbing and des cending slopes up to 35° and also safely traverse