Abstract
The problem of real-time optimal guidance is extremely important for successful autonomous missions. In this paper, the last phases of autonomous lunar landing trajectories are addressed. The proposed guidance is based on the Particle Swarm Optimization, and the differential flatness approach, which is a subclass of the inverse dynamics technique. The trajectory is approximated by polynomials and the control policy is obtained in an analytical closed form solution, where boundary and dynamical constraints are a priori satisfied. Although this procedure leads to sub-optimal solutions, it results in beng fast and thus potentially suitable to be used for real-time purposes. Moreover, the presence of craters on the lunar terrain is considered; therefore, hazard detection and avoidance are also carried out. The proposed guidance is tested by Monte Carlo simulations to evaluate its performances and a robust procedure, made up of safe additional maneuvers, is introduced to counteract optimization failures and achieve soft landing. Finally, the whole procedure is tested through an experimental facility, consisting of a robotic manipulator, equipped with a camera, and a simulated lunar terrain. The results show the efficiency and reliability of the proposed guidance and its possible use for real-time sub-optimal trajectory generation within laboratory applications.
Highlights
In recent years, space agencies have experienced a renewed interest in lunar robotic missions
The presence of craters on the Moon requires an autonomous hazard avoidance [9], which allows for achieving the landing in a safe region outside craters. This means that a new trajectory should be planned as soon as one of the previous conditions occurs; the new guidance should be optimal in terms of the minimum time or the minimum fuel consumption. This is the goal of this paper, i.e., to propose and test, with an experimental application, a sub-optimal guidance based on the well-known metaheuristic algorithm Particle Swarm Optimization (PSO) [10] and the inverse dynamics approach [11] for lunar landing trajectories
In order to test the robustness of the optimization procedure as the initial conditions vary, two Monte Carlo analyses are performed on the approaching phase without considering for the hazard detection
Summary
Space agencies have experienced a renewed interest in lunar robotic missions. The presence of craters on the Moon requires an autonomous hazard avoidance [9], which allows for achieving the landing in a safe region outside craters This means that a new trajectory should be planned as soon as one of the previous conditions occurs; the new guidance should be optimal in terms of the minimum time or the minimum fuel consumption. This is the goal of this paper, i.e., to propose and test, with an experimental application, a sub-optimal guidance based on the well-known metaheuristic algorithm Particle Swarm Optimization (PSO) [10] and the inverse dynamics approach [11] for lunar landing trajectories
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