Planetary Landing Research Articles

Onboard Observation Planning for Sequential Image-based Planetary Landing Navigation in Unknown Environments

Autonomous navigation based on sequential images is widely accepted as an effective method to guarantee pinpoint landing and precise obstacle avoidance in planetary landings. Observing a large number of landmarks and processing a large number of images places a computational burden on landers with limited computational resources, but existing studies lack observation planning methods for landmarks in unknown environments that can be operated autonomously on landers. In this paper, we analyze the observability of a vision-aided inertial navigation (VAIN) system and propose an observation planning strategy. The minimum number of landmarks and the minimum number of observations are obtained from the observability analysis, which is the boundary condition for observation planning. The landmark configuration planning in the observation planning strategy optimizes only univariate convex functions to efficiently select a small number of landmarks in unknown environments with high navigation accuracy, and the observation interval planning adaptively reduces the number of observations without significantly affecting the navigation accuracy. Simulation results verify the correctness of the observability analysis results, and it is found that the proposed observation planning strategy outperforms traditional observability degrees in terms of both improved navigation accuracy and computational speed when observing a small number of landmarks, and can effectively reduce the number of observations.

Square-Root Extended Information Filter for Visual-Inertial Odometry for Planetary Landing

A novel sequential information filter formulation for computationally efficient visual-inertial odometry and mapping is developed in this work and applied to a realistic moon landing scenario. Careful construction of the square-root information matrix, in contrast to the full information or covariance matrix, provides easy and exact mean and covariance recovery throughout operation. Compared to an equivalent extended Kalman filter implementation, which provides identical results, the proposed filter does not require explicit marginalization of past landmark states to maintain constant-time complexity. Whereas measurements to opportunistic visual features only provide relative state information, resulting in drift over time unless a priori mapped landmarks are identified and tracked, the tight coupling of the inertial measurement unit provides some inertial state information. The results are presented in a terrain-relative navigation simulation for both a purely orbital case (with no active propulsion) and a landing case with a constant thrust.

Corrigendum to “Barrier Lyapunov Function-Based Planetary Landing Guidance for Hazardous Terrains”

In [1], the authors regret that due to the typing error and mathematical errors in the proof, three equations need to be corrected. Despite these errors, the main contributions of this article as well as the conclusions are correct.

Barrier Lyapunov Function-Based Planetary Landing Guidance for Hazardous Terrains

The landing guidance based on the barrier Lyapunov function (BLF) for hazardous terrains is investigated. Three suitable spatial geometric shapes (frustum-shape, cone-shape, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -step-shape) have been chosen to describe the possible obstacles on the planetary surface. Next, a novel and general form of the barrier function (BF) has been developed using selected spatial geometric shape information, specifically designed to constrain the lateral motion. For these three different spatial geometric shapes, only the segment number of BF is different, and the segment number is determined by the spatial geometric shape. Furthermore, a fixed-time convergent function is selected as the upper boundary to coordinate the vertical motion, guaranteeing that the lander completes the landing mission within the predefined time. Next, a new nonlinear feedback guidance is designed using the asymmetric BLF constructed by the BF, keeping the lander from colliding with the obstacle and achieving the pinpoint soft landing. Finally, numerical simulations with different hazardous terrains are performed to verify the feasibility and effectiveness of the proposed algorithms.

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

Optimal crater landmark selection based on optical navigation performance factors for planetary landing

Planetary craters are natural navigation landmarks that widely exist and are easily observed. Optical navigation based on crater landmarks has become an important autonomous navigation method for planetary landing. Due to the increase in observed crater landmarks and the limitation of onboard computation, the selection of good crater landmarks has gradually become a research hotspot in the field of landmark-based optical navigation. This paper designs a fast crater landmark selection method, which not only considers the configuration observability of crater subsets but also focuses on the influence on navigation performance arising from the measurement uncertainty and the matching confidence of craters, which is different from other landmark selection methods. The factor of measurement uncertainty, which is anisotropic, correlated and nonidentically distributed, is quantified and integrated into selection based on crater pairing detection and localization error evaluation. In addition, the concept of the crater matching confidence factor is introduced, which reflects the possibility of 2D projection measurements corresponding to 3D positions. Combined with the configuration observability factor, the crater landmark selection indicator is formed. Finally, the effectiveness of the proposed method is verified by Monte Carlo simulations.

Obstacle avoidance guidance for planetary landing using convex trajectory and adaptive curvature regulation

Autonomous landing in complex and hazardous terrains is a critical stage of planetary in-situ exploration and sample-return missions. The design of the landing trajectory has to seek a balance between safety and fuel economy. Based on the theorems of convex trajectory and curvature guidance law, this paper proposes an obstacle avoidance guidance method with an adaptive curvature adjusting mechanism. The method remains the advantage in obstacle avoidance of the existed curvature guidance, and can further minimize fuel consumption by adopting a global optimization technique with a specific curvature constraint. Firstly, the nonconvex curvature constraint is transformed into a second-order cone constraint to construct a standard convex programming problem. The curvature adjustment strategy is then designed to adapt the trajectory to varying terrain conditions. By introducing the successive convex technique, the adaptive curvature guidance strategy is also suitable for small celestial body landing problems in nonlinear dynamic environments. Simulations of typical planetary landing scenarios are conducted to verify the effectiveness of the proposed method in improving safety and fuel efficiency.

Vector Trajectory Method for Obstacle Avoidance Constrained Planetary Landing Trajectory Optimization

Considering the requirement of avoiding obstacles in different planetary landing missions, this article presents a generalized method dealing with the obstacle avoidance constrained landing trajectory optimization problem with vectorized strategy, i.e., vector trajectory method (VTM). By introducing a trajectory direction constraint with vectorized description, the character of the obstacle avoidance trajectory can be more specifically depicted. Moreover, by satisfying proper trajectory direction constraint, the lander’s particular performances, such as the observation to the target or probability of a safe flight, can be improved. To this end, the VTM and relevant theorem are first developed, revealing the relationship between the two aspects of obstacle avoidance constraint (i.e., distance constraint and trajectory direction constraint). Then, both aspects of constraints are uniformly transformed as the auxiliary angle magnitude constraint with the vectorized strategy, which significantly simplifies the solving procedure of obstacle avoidance constrained trajectory optimization problem. Finally, the proposed VTM is illustrated in detail through the examples with the planetary landing background of atmospheric entry and powered descent landing.

Semi-Analytical Planetary Landing Guidance with Constraint Equations Using Model Predictive Control

With the deepening of planetary exploration, rapid decision making and descent trajectory planning capabilities are needed to cope with uncertain environmental disturbances and possible faults during planetary landings. In this article, a novel decoupling method is adopted, and the analytical three-dimensional constraint equations are derived and solved, ensuring real-time guidance computation. The three-dimensional motion modes and thrust profiles are analyzed and determined based on Pontryagin’s minimum principle, and a supporting semi-analytical reachability judgment method is presented, which can also be used to determine controllability. The algorithm is embedded in the model predictive control (MPC) framework, and several techniques are adopted to enhance stability and robustness, including thrust averaging, thrust correction after ignition, thrust reservation, and open-loop terminal guidance. Numerical simulations demonstrate that the proposed algorithm can guarantee real-time trajectory generation and meanwhile maintain considerable optimality. In addition, the MPC simulation shows that the algorithm can maintain a good accuracy under external disturbances.

HYBRID ROCKET PROPULSION DEVELOPMENT AND APPLICATION.

Satellite technology is advancing. Satellite propulsion requires several desired rocket properties. This is necessary for 1 kg of payload. Launch vehicles no longer need rocket engines. Application includes satellite movement, orbit transfer, and probe and lander propulsion. Space travel is inventive and exciting. Research, production, and use costs must be decreased to make space transportation systems generally available. Security shouldn't be affected. Rocket propellants must be high-performing, non-toxic, and safe. Rocket engines have other parts. Restarting and throttling are crucial. These are inaccessible to solid rocket motors. Developing a liquid rocket engine is hard and expensive, but it can be restarted and throttled. It's being studied for use in hybrid rocket propulsion and other space applications. Space tourist vehicles, lunar and planetary landers, suborbital launch vehicles, satellite maneuvering systems, etc. are potential applications for microsatellites (including orbit transfer). More of these applications are pending approval. Hybrid propulsion is straightforward, safe, and permits regenerative braking, throttling, and restarting. Hybrid propellants are mostly non-toxic and storable. Separate oxidizer and fuel storage enhance safety. This study examines the history, development, current applications, and future of hybrid rocket propulsion. We'll discuss popular fuels and oxidizers. Explains hybrid rocket motor's low regression rate. The article includes the author research.

Semi-analytical adaptive guidance computation for autonomous planetary landing

A novel algorithm for autonomous landing guidance computation is presented. The trajectory is expressed in polynomial form of minimum order to satisfy a set of 17 boundary constraints, depending on 2 parameters: time-of-flight and initial thrust magnitude. The consequent control acceleration is expressed in terms of differential algebraic (DA) variables, expanded around the point of the domain along the nominal trajectory followed at the retargeting epoch. The DA representation of the objective and constraints gives additional information about their sensitivity to variations of the optimization variables, which is exploited to find the desired fuel minimum solution (if existing) robustly and with a very light computational effort.

Towards Automated Sample Collection and Return in Extreme Underwater Environments

In this report, we present the system design, operational strategy, and results of coordinated multivehicle field demonstrations of autonomous marine robotic technologies in searchfor- life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of remotely operated vehicle (ROV) manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an analog to a planetary lander, which must be capable of high-level autonomy. Our field trials involve two underwater vehicles, the SuBastian ROV and the Nereid Under Ice (NUI) hybrid ROV for mixed initiative (i.e., teleoperated or autonomous) missions, both equipped seven-degrees-of-freedom hydraulic manipulators.We describe an adaptable, hardware-independent computer vision architecture that enables high-level automated manipulation. The vision system provides a three-dimensional understanding of the workspace to inform manipulator motion planning in complex unstructured environments. We demonstrate the effectiveness of the vision system and control framework through field trials in increasingly challenging environments, including the automated collection and return of biological samples from within the active undersea volcano Kolumbo. Based on our experiences in the field, we discuss the performance of our system and identify promising directions for future research.

Onboard fuel-optimal guidance for human-Mars entry, powered-descent, and landing mission based on feature learning

This paper investigates the end-to-end human-Mars entry, powered-descent, and landing (EDL) guidance problem by developing an on-board learning-based optimal control method (L-OCM) to achieve the goal of precise and fuel-efficient planetary landing. First, the end-to-end EDL guidance problem is formulated as a multi-phase optimal control problem with hybrid dynamics and constraints. Then a customized alternating direction method of multipliers is applied to solve the end-to-end EDL guidance problem with varying initial states off-line. After that, the L-OCM is developed to generate real-time optimal guidance commands. To be specific, supported by the optimal control theory, the necessary conditions of optimality for optimal control of the entry phase and powered-descent phase are derived, respectively, which leads to two two-point-boundary-value-problems (TPBVPs). Then, critical parameters are identified to approximate the complete solutions of the TPBVPs. To find the implicit relationship between the initial states and these critical parameters, deep neural networks are constructed to learn the values of these critical parameters in real-time with training data obtained from the off-line solutions. Furthermore, when random disturbances are considered during the EDL process, the single stage L-OCM is extended to the multi-stage L-OCM to regenerate real-time optimal guidance commands. Finally, the proposed L-OCM is implemented in extensive simulation cases to verify the effectiveness and efficiency of the new method.

Structure of optimal control for planetary landing with control and state constraints

This paper studies a vertical powered descent problem in the context of planetary landing, considering glide-slope and thrust pointing constraints and minimizing any final cost. In a first time, it proves the Max-Min-Max or Max-Singular-Max form of the optimal control using the Pontryagin Maximum Principle, and it extends this result to a problem formulation considering the effect of an atmosphere. It also shows that the singular structure does not appear in generic cases. In a second time, it theoretically analyzes the optimal trajectory for a more specific problem formulation to show that there can be at most one contact or boundary interval with the state constraint on each Max or Min arc.

Benchmark Analysis of Semantic Segmentation Algorithms for Safe Planetary Landing Site Selection

This paper presents an in-depth analysis of state-of-the-art semantic segmentation algorithms applied to spacecraft safe planetary landing via hazard detection and avoidance. Several architectures are trained from binary safety maps and the rich dataset of the High-Resolution Imaging Science Experiment (HiRISE) embedded on Mars Reconnaissance Orbiter for realistic purposes. The study incorporates several metrics comparisons such as recognition accuracy, computational complexity, model complexity, and inference time. The proposed performance indices and combinations are analyzed and discussed. The experiments were performed using a Raspberry Pi 4B, which is a relevant commercial-of-the-shelf microcontroller surrogate of NASA’s High-Performance Spaceflight Computer (HPSC) that will thrive within the next decades in space exploration. This paper allows researchers to know what has been tested on the subject and serves as a catalog for users to pick the most relevant architecture for their own application.

Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

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.

Real-time trajectory optimization for powered planetary landings based on analytical shooting equations

Traditionally, numerical trajectory integration for shooting equation calculation and iterations for shooting with randomly guessed initial solutions deteriorate the real-time performance of indirect methods for on-board applications. In this study, the indirect method is improved to achieve real-time trajectory optimization of fuel-optimal powered planetary landings with the help of analytical shooting equation derivations and a practical homotopy technique. Specifically, the contributions of this paper are threefold. First, the analytical expressions for shooting equation calculation are derived to replace the traditional time-consuming trajectory integration. Consequently, the computational efficiency is significantly improved. Second, the original three-dimensional landing problem is connected with a simplified one-dimensional problem that only involves the vertical dynamics, and its analytical solution is obtained based on Pontryagin’s minimum principle. Third, starting with the analytical solution, the accurate solution of the original landing problem can be obtained through an adaptive homotopy process. Simulation results of Earth landing scenarios are given to substantiate the effectiveness of the proposed techniques and illustrate that the developed method can obtain a fuel-optimal landing trajectory in 5 ms with 100% success rate.

Landmark database selection for vision-aided inertial navigation in planetary landing missions

Vision-aided inertial navigation (VAIN) system offers a means to enable high-accuracy autonomous navigation for planetary landing. The VAIN system provides precise state estimates for the landing vehicle by combining measurements from an inertial measurement unit (IMU) with visual observations of the mapped landmarks contained in a predetermined terrain database. Due to the constraints of the onboard storage and computation capabilities of the landing vehicle, a terrain database containing the minimum number of landmarks should be found to guarantee the requirements of the navigation task. In this paper, a landmark database selection algorithm based on linear covariance (LinCov) technique is presented. The algorithm selects landmarks into the terrain database by minimizing the uncertainties of the chosen state parameters at each imaging time point along the nominal trajectory. The uncertainties are calculated by LinCov which is an efficient approach to analyze the effect of landmark databases on the navigation performance and allows for considering the visibility constraints of landmarks prior to actual flight. Extensive simulations are performed to evaluate the proposed landmark selection algorithm, and the results show that the proposed method is effective.

POWERED LANDING GUIDANCE ALGORITHMS USING REINFORCEMENT LEARNING METHODS FOR LUNAR LANDER CASE

Any future planetary landing missions, just as demonstrated by Perseverance in 2021 Mars landing missions require advanced guidance, navigation, and control algorithms for the powered landing phase of the spacecraft to touch down a designated target with pinpoint accuracy (circular error precision < 5 m radius). This requires a landing system capable to estimate the craft’s states and map them to certain thrust commands for each craft’s engine. Reinforcement learning theory is used as an approach to manage the mapping guidance algorithm and translate it to engine thrust control commands. This work compares several reinforcement-learning-based approaches for a powered landing problem of a spacecraft in a two-dimensional (2-D) environment and identifies their advantages or disadvantages. Three methods in reinforcement learning, namely Q-Learning, and its extensions such as DQN and DDQN. They are benchmarked in terms of rewards and training time needed to land the Lunar Lander. It is found that the Q-Learning method also called Heuristic produced the highest efficiency. Another contribution of this paper is to show the combination usage of online weights � between the action selection process and action evaluation process, yields a higher reward, instead of separating them, which significantly enhances their optimization performance. The simulations of the powered guidance performance in a 2-D simulation environment highlight the effectiveness of DQN to handle multiple neural networks better than DDQN.

Comparison of powered descent guidance laws for planetary pin-point landing

This paper conducts a comparison study of constrained powered descent guidance laws for planetary pin-point landing from the viewpoints of guidance performance and algorithm structure. All considered algorithms are reformulated to incorporate the critical elements of the powered descent problem, such as the free flight time, thrust limitation and terrain constraint. With these requirements to meet, the performance of guidance laws is evaluated and compared via simulations. Specifically, the nominal landing simulation verifies the guidance accuracy, fuel consumption, and real-time performance, as well as the hazard avoidance capability. Meanwhile, the robustness of guidance laws is evaluated by the Monte Carlo simulation, and the performance difference in final landing dispersion is interpreted by analyzing the algorithm structure and characteristics. Lastly, the discussion on practicability indicates the promise of optimization-based algorithms in future planetary landing missions.