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High scientific value areas on celestial bodies such as the Moon and Mars are often located in hazardous terrains. To achieve safe landing exploration, a novel planning method is proposed, which can ensure that the planned trajectory maintains a user-specified distance from obstacles, thus reducing potential collision risk induced by factors such as the body size and model uncertainties. Firstly, the basic model of the trajectory optimization problem and its convexification version is given. The obstacles are modeled as polynomial functions, based on which the "if-then" obstacle avoidance logic explicitly considering the safe distance, is described as a sum of squares constraint. This constraint formulation applies to any obstacle described by a finite number of polynomials, independent of the specific expression of the polynomials (reflecting the shape of the obstacle). Subsequently, the convexification process for the obstacle avoidance constraint is given. Finally, the sequential sum of squares programming problem for the obstacle avoidance trajectory is established, which boils down to a series of semidefinite programming problems. Simulation results show that the closest distance between the planned trajectory and obstacles strictly satisfies the specified distance constraint, and the trajectory could avoid non-convex obstacles. With the promising convergence properties of underlying convex optimization algorithms, advanced autonomous obstacle avoidance guidance schemes are expected to be formed based on the proposed trajectory planning method. • Mars/Moon landings cannot ignore unknown and hazardous surface environments. • The trajectory obtained by traditional avoidance methods is too close to obstacles. • The safe distance constraint can be described as a sum of squares constraint. • The proposed planning method applies to any obstacle described by polynomials. [ABSTRACT FROM AUTHOR]

This paper proposes a guidance method for the powered soft landing of a launcher with non-cluster configured engines, for which it is difficult to maintain a low thrust-to-weight ratio (TWR) preferable for rocket recovery. For rockets in service with one or two engines rather than a cluster of engines in the boosters, it is a challenge to deeply throttle the engines to a low level, especially lower than 30%, and if one of the two engines is shut down to reduce the thrust, the attitude control is also difficult due to thrust asymmetry. The theoretical analysis and simulations show that the higher the TWR is, the smaller the feasible region for a soft landing becomes. The analysis also reveals that the bang–bang solutions of fuel-optimal methods are vulnerable to uncertainties and disturbances. A thrust regulation rate constraint is proposed or convexified to reconstruct the landing problem under high TWRs. An initial guess based on mean thrust and relaxing the initial velocity constraint is proposed, making both the adaptive collocation method and the successive convex programming applicable for real-time landing planning. Simulations under TWRs larger than four show that the proposed methods ensure a safe landing and are more adaptive than fuel-optimal solutions when facing disturbances and uncertainties. • Powered descent guidance under weak throttling ability is studied for the first time. • Effects of HTWR on trajectory planning and online optimization are detailed analyzed. • Thrust variation rate constraints are introduced to ensure soft landing under HTWRs. • Real-time planning by ACM and SCP fueled by smart initial guess based on mean thrust. • Solution is more adaptable to disturbance/uncertainties compared with other methods. [ABSTRACT FROM AUTHOR]

This paper summarizes the autonomous guidance methods (AGMs) for pinpoint soft landing on celestial surfaces. We first review the development of powered descent guidance methods, focusing on their contributions for dealing with constraints and enhancing computational efficiency. With the increasing demand for reusable launchers and more scientific returns from space exploration, pinpoint soft landing has become a basic requirement. Unlike the kilometer-level precision for previous activities, the position accuracy of future planetary landers is within tens of meters of a target respecting all constraints of velocity and attitude, which is a very difficult task and arouses renewed interest in AGMs. This paper states the generalized three- and six-degree-of-freedom optimization problems in the powered descent phase and compares the features of three typical scenarios, i.e., the lunar, Mars, and Earth landing. On this basis, the paper details the characteristics and adaptability of AGMs by comparing aspects of analytical guidance methods, numerical optimization algorithms, and learning-based methods, and discusses the convexification treatment and solution strategies for non-convex problems. Three key issues related to AGM application, including physical feasibility, model accuracy, and real-time performance, are presented afterward for discussion. Many space organizations, such as those in the United States, China, France, Germany, and Japan, have also developed free-flying demonstrators to carry out related research. The guidance methods which have been tested on these demonstrators are briefly introduced at the end of the paper. [ABSTRACT FROM AUTHOR]

It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling’s ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

Traditional studies on Mars entry, descent, and landing usually take a divide-and-conquer approach where each phase is investigated separately. This paper proposed a new approach to achieve optimal integrated guidance for mid-lift high-mass Mars entry and powered descent. First, optimal Mars atmospheric entry and optimal powered descent are respectively modeled. Second, optimal handover problem is formulated to integrate the optimal Mars entry and powered descent problems in a collaborative optimization manner. Third, hp-adaptive pseudospectral method is employed to capture optimal entry guidance and propellant-optimal powered descent guidance. Reinforcement learning methodology is adopted to obtain the optimal handover, according to a large amount of trial results obtained from solving optimal Mars entry and powered descent problems. Finally, numerical simulations verified the optimality, robustness, and accuracy of the proposed technique through comparing with successive convexification approach under the nominal conditions and uncertainties, and the comparison results demonstrated the significant benefit of the integrated optimal guidance. • Optimal integrated guidance for Mars entry and powered descent is designed. • Integrated guidance is produced in a collaborative optimization manner. • Mars entry and powered descent guidance are combined via optimal handover. • Optimal handover is achieved using reinforcement learning approach. [ABSTRACT FROM AUTHOR]

The landing safety and accuracy of a Mars lander will be seriously degraded due to multiple unknown disturbances or perturbations in the powered descent phase. A novel composite guidance algorithm is proposed to improve the landing performance in this paper. The presented guidance algorithm, with a composite hierarchical framework, is developed by the combination of disturbance observer-based control and multiple sliding surfaces guidance theory. The major disturbance owing to the Mars wind could be estimated through a disturbance observer and incorporated in the feed-forward compensation in the inner loop, other disturbances or perturbations could be attenuated by the multiple sliding surfaces technique in the outer loop. The composite guidance algorithm could be utilized without a pre-computed reference trajectory and it has enhanced anti-disturbance capability compared with the multiple sliding surfaces guidance law. Its global stability is verified using a Lyapunov-based approach. Monte Carlo simulation results show that the composite guidance law has a better performance on guiding a Mars lander from the point of engine ignition to the desired landing point in the presence of disturbances and perturbations. [ABSTRACT FROM AUTHOR]

One of the main challenges posed by the next space systems generation is the high level of autonomy they will require. Hazard Detection and Avoidance is a key technology in this context. An adaptive guidance algorithm for landing that updates the trajectory to the surface by means of an optimal control problem solving is here presented. A semi-analytical approach is proposed. The trajectory is expressed in a polynomial form of minimum order to satisfy a set of boundary constraints derived from initial and final states and attitude requirements. By imposing boundary conditions, a fully determined guidance profile is obtained, function of a restricted set of parameters. The guidance computation is reduced to the determination of these parameters in order to satisfy path constraints and other additional constraints not implicitly satisfied by the polynomial formulation. The algorithm is applied to two different scenarios, a lunar landing and an asteroidal landing, to highlight its general validity. An extensive Monte Carlo test campaign is conducted to verify the versatility of the algorithm in realistic cases, by the introduction of attitude control systems, thrust modulation, and navigation errors. The proposed approach proved to be flexible and accurate, granting a precision of a few meters at touchdown. [ABSTRACT FROM AUTHOR]

It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling's ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB. [ABSTRACT FROM AUTHOR]