The Combined and Path Allocation Technique with the use of Space Robotics

Space robots have a wide range of possible applications in the aerospace sector. However, they are bounded with limited fuel and cannot recharge, given that they operate in their special working environment. Thus, in this way, path planning that is effective is necessary to maximize the operation of space robots. In this paper, simultaneous exploration task allocation and path planning are considered to reduce the completion delay of the exploration task. The VGTA algorithm proposed for variable genetic task allocation in this paper provides a general approach to the task allocation problem in a complex environment. It makes use of genetic principles to generate and optimize the task sequences, and its intrinsic conflict resolution ensures that the solutions are robust against the conflicts. The VGTA, with the help of the random mutation, greedy crossover, and conflict handling mechanisms, provides reliable and efficient results near to the optimal values. The evaluation metrics PDbest, PDav, and the cost-effectiveness ratio (CER) define the performance of VGTA in realistic applications by which, with a complex map, VGTA operates quite well in delivering solutions. VGTA is one of the preferable solutions in terms of any kind of task allocation optimization, as with a complex map, it gives a good balance in computation time and quality of the solution obtained.