Real-time guidance of reusable winged space plane using genetic algorithm implemented on field programmable gate array

This paper proposes a rapid trajectory planning algorithm using random search and a dynamics filter based on dynamic inversion. It can rapidly generate a collision-free trajectory for an unmanned aircraft in a highly constrained environment, including complex terrains with obstacles or waypoints. Conventional studies on trajectory generation mainly focus on global optimization; hence, the calculation cost grows exponentially with increased complexity. The proposed trajectory planner consists of two steps to effectively generate a collision-free flight trajectory. First, a sampling-based random search algorithm rapidly determines a collision-free pathway. However, the pathway generated by the random search is calculated geometrically, so the variations in the actual flight are difficult to follow. Therefore, the second step, named dynamics filtering, modifies the initial pathway to a dynamically flyable trajectory with a one-time-only integral calculation. The dynamic inversion technique is utilized to ensure flight-worthiness. In this paper, the implementation and computer simulation results of the proposed trajectory are described. The effectiveness of the proposed method is verified by a six-degrees-of-freedom flight simulation for a Mars exploration aircraft model developed by JAXA and associated research institutes.

Section: Introductionmentioning

confidence: 99%

Rapid Trajectory Planner for Navigating Complex Terrain Using Random Search and Dynamics Filter Based on Dynamic Inversion

Itakura

Yonemoto

Matsumoto

et al. 2015

“…Real-time trajectory generation is therefore employed. The guidance system of the rocket implements a Genetic Algorithm on a FPGA (Field-Programmable Gate Array) 4) , which enables the online creation of an optimized flight trajectory in a simple constrained environment. The results of the simulation are shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Development and Ground Combustion Test of a Subscale Reusable Winged Rocket

Itakura

Sasaki

Miyamoto

et al. 2014

is developing a fully reusable sounding rocket named WIRES (WInged REusable Sounding rocket). The winged rocket incorporates many novel technologies, including a full composite structure and a navigation, guidance, and control system. It is also equipped with an innovative hybrid rocket engine named CAMUI (CAscaded MUltistage Impinging-jet). In such a complex rocket, system integration is difficult to achieve and innovation is imperative. The laboratory is therefore also developing a subscale model of the rocket named WIRES#014 to assess the new navigation, guidance, and control system. This paper describes the procedure and results of a trial and error approach, comprising three ground combustion tests, to integrating the systems of the rocket. In the first and second try of the combustion test, the tests had some troubles mainly about ground support system and avionics. Authors eliminated these errors after the cause analysis; the third combustion test was finally succeeded.

“…On the other hand, there is also an ongoing study for loading a guidance algorithm on a large-scale integration (LSI) device called a field programmable gate array (FPGA) to calculate a GA with a relatively high computation load aboard a spacecraft. 3) A variety of trajectories are generated by the optimized calculation process performed by the GA, which converge into a single best trajectory as the calculation progresses. For actual flights of winged rockets, however, there potentially may be cases where a sudden change of trajectory becomes necessary.…”

Section: Introductionmentioning

confidence: 99%

Dynamically Distributed Genetic Algorithm and Its Application to Diversely Selectable Trajectory Optimization of Winged Rockets

Miyamoto

Matsumoto

Yonemoto

2014

The development of an efficient and flexible guidance system is one of the most important aspects of studies on reusable space transportation systems such as winged rockets. A guidance system that can generate several good trajectories instantly is required for responding to sudden changes in trajectories that may lead to accidents. We therefore propose a flight trajectory optimization method that uses a dynamically distributed genetic algorithm (DGA). DGA optimizes solutions by dynamically dividing and merging the individuals using the convergence situation of population. In most conventional studies on distributed genetic algorithms, the population has been divided into a fixed number of groups. This constraint sometimes deteriorates the growth of the solution because the number of groups and individuals in each group is closely related to the convergence of the solution. Our proposed dynamically DGA, which uses hierarchical clustering, was verified by a computer simulation.