With the rapid evolution of space technologies and increasing thirst for knowledge about the origin of life and the universe, the need for deep space missions as well as for autonomous solutions for complex, time-critical mission operations becomes urgent. Within this context, the project KaNaRiA aims at technology development tailored to the ambitious task of space resource mining on small planetary bodies using increased autonomy for on-board mission planning, navigation and guidance. This paper focuses on the specific challenges as well as first solutions and results corresponding to the KaNaRiA mission phases (1) interplanetary cruise, (2) target identification and characterization and (3) proximity operations. Based on the KaNaRiA asteroid mining mission objectives, initially, a mission reference scenario as well as a reference mission architecture are described in this paper. KaNaRiA has been proposed as a multi-spacecraft mission to the asteroid main belt. Composed of a flock of prospective scout spacecraft, a mother ship carrying the mining payload and several service modules placed on a 2.8 AU parking orbit around the Sun, KaNaRiA intends to characterize main belt asteroid properties, identify targets for mining and perform a soft-landing for in-situ characterization and mining. Subsequently, the autonomous navigation system design of KaNaRiA for the interplanetary cruise is presented. The navigation challenges, which arise in phases (1) to (3), are discussed. Particular attention is given to the sensor-technology readiness-level, accuracy, applicability range, mass and power budgets. In order to navigate in the vicinity of an asteroid, an information fusion algorithm is required that aggregates multi-sensor data as well as a-priori knowledge and solves the task known as simultaneous localization and mapping (SLAM). In order to deal with uncertain and inconsistent information and to explicitly represent different dimensions of uncertainty, a belief-function-based SLAM approach is used, which is a generalization of the popular FastSLAM algorithm. The objective of the guidance task is the autonomous planning of optimal transfer trajectories according to mission driving criteria, e.g. transfer time and fuel consumption. Optimal control problems and the calculation of trajectory sensitivities for on-board stability analysis as well as real-time optimal control are explained. Bringing cognitive autonomy to a spacecraft requires an on-board computational module as a central spacecraft component. This module is responsible for state evaluation, mission planning and decision-making regarding selection of potential targets, trajectory selection and FDIR. A knowledge-base serves as a database for decision making processes. With the aim to validate and test our methods, we create a virtual environment in which humans can interact with the simulation of the mission. In order to achieve real-time performance, we propose a massively-parallel software system architecture, which enables very efficient and easily adaptable communication between concurrent software modules within KaNaRiA.    «

With the rapid evolution of space technologies and increasing thirst for knowledge about the origin of life and the universe, the need for deep space missions as well as for autonomous solutions for complex, time-critical mission operations becomes urgent. Within this context, the project KaNaRiA aims at technology development tailored to the ambitious task of space resource mining on small planetary bodies using increased autonomy for on-board mission planning, navigation and guidance. This p...    »