Cognitive Graph for Autonomous Deep Space Mission Planning and Multi-Constraints Collision Detection

Abstract

To deal with the multi-constraints in multi-subsystems coordination mechanism in deep space exploration mission planning, in this paper a cognitive graph architecture and a multi-attributes constraint conflict detection method were proposed for deep space exploration mission planning. In this paper, the graph representation method was adopted to realize knowledge modeling of task planning, the state transition diagram was constructed into triples to realize rule matching during task planning, and a multi-attributes constraint conflict detection algorithm was proposed based on the graph model inference method, so multi-subsystems cognitive reasoning and constraint conflict testing for task planning were realized. Simulation experiments were carried out with different scales of deep space exploration mission planning examples. The experimental results show that compared with genetic algorithm, traditional heuristic algorithm, constrained heuristic algorithm, and evolutionary neural network algorithm, the method proposed in this paper can effectively shorten planning time, and reduce the solution space and memory consumption, effectively improving the success rate and feasibility of deep space exploration mission planning.

Keywords

deep space exploration / cognitive graph / graph model / mission planning / multi-constraints collision detection