Active Removal Missions Research Articles

Space Manipulator Collision Avoidance Using a Deep Reinforcement Learning Control

Recent efforts in on-orbit servicing, manufacturing, and debris removal have accentuated some of the challenges related to close-proximity space manipulation. Orbital debris threatens future space endeavors driving active removal missions. Additionally, refueling missions have become increasingly viable to prolong satellite life and mitigate future debris generation. The ability to capture cooperative and non-cooperative spacecraft is an essential step for refueling or removal missions. In close-proximity capture, collision avoidance remains a challenge during trajectory planning for space manipulators. In this research, a deep reinforcement learning control approach is applied to a three-degrees-of-freedom manipulator to capture space objects and avoid collisions. This approach is investigated in both free-flying and free-floating scenarios, where the target object is either cooperative or non-cooperative. A deep reinforcement learning controller is trained for each scenario to effectively reach a target capture location on a simulated spacecraft model while avoiding collisions. Collisions between the base spacecraft and the target spacecraft are avoided in the planned manipulator trajectories. The trained model is tested for each scenario and the results for the manipulator and base motion are detailed and discussed.

A two-level scheme for multiobjective multidebris active removal mission planning in low Earth orbits

A two-level scheme for multiobjective multidebris active removal mission planning in low Earth orbits

A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO

A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO

A maximal-reward preliminary planning for multi-debris active removal mission in LEO with a greedy heuristic method

An active multi-debris removal mission planning algorithm for low Earth orbit (LEO) debris removal is presented. We assume that a space platform carrying multiple debris-deorbiting nano-satellites will be launched to carry out the debris-removal mission. This platform travels from one debris object to the next using a drift-orbit-transfer strategy. Each debris is assigned a reward value reflecting potential collision risk reductions if the debris is deorbited. The objective of mission planning is to determine an ordering of debris to be deorbited that maximizes the total reward due to debris removal subject to the constraints of (a) the total amount of velocity change for orbit transfer, (b) the total duration of the debris removal mission, and (c) the number of deorbiting nano-satellites carried on board the platform. In this work, a new approach is taken to approximate the response curve of drift-orbit transfers between each debris pair using a numerically interpolated continuous curve. An optimal orbit transfer strategy then is adopted to minimizes the sum of relative Δv consumption and relative transfer duration. The task of multi-debris removal mission planning is formulated as a combinatorial tree search discrete optimization problem using this optimal orbit transfer cost estimate. A greedy heuristic with polynomial time complexity is proposed to select the next debris to deorbit that maximizes the reward-to-cost ratio. The effectiveness of this novel approach is demonstrated using real-world Iridium 33 debris cloud data. It is observed that this algorithm delivers superior performance while consuming a small fraction of computing time compared to state of the art mission planning algorithms.

Fault-tolerant feature-based estimation of space debris rotational motion during active removal missions

One of the key functionalities required by an Active Debris Removal mission is the assessment of the target kinematics and inertial properties. Passive sensors, such as stereo cameras, are often included in the onboard instrumentation of a chaser spacecraft for capturing sequential photographs and for tracking features of the target surface. A plenty of methods, based on Kalman filtering, are available for the estimation of the target's state from feature positions; however, to guarantee the filter convergence, they typically require continuity of measurements and the capability of tracking a fixed set of pre-defined features of the object. These requirements clash with the actual tracking conditions: failures in feature detection often occur and the assumption of having some a-priori knowledge about the shape of the target could be restrictive in certain cases. The aim of the presented work is to propose a fault-tolerant alternative method for estimating the angular velocity and the relative magnitudes of the principal moments of inertia of the target. Raw data regarding the positions of the tracked features are processed to evaluate corrupted values of a 3-dimentional parameter which entirely describes the finite screw motion of the debris and which primarily is invariant on the particular set of considered features of the object. Missing values of the parameter are completely restored exploiting the typical periodicity of the rotational motion of an uncontrolled satellite: compressed sensing techniques, typically adopted for recovering images or for prognostic applications, are herein used in a completely original fashion for retrieving a kinematic signal that appears sparse in the frequency domain. Due to its invariance about the features, no assumptions are needed about the target's shape and continuity of the tracking. The obtained signal is useful for the indirect evaluation of an attitude signal that feeds an unscented Kalman filter for the estimation of the global rotational state of the target. The results of the computer simulations showed a good robustness of the method and its potential applicability for general motion conditions of the target.

Multi-objective optimal preliminary planning of multi-debris active removal mission in LEO

The goal of this paper is to develop a preliminary plan for a multi-nanosatellite active debris removal platform (MnADRP) for low-Earth-orbit (LEO) missions. A dynamic multi-objective traveling salesman problem (TSP) scheme is proposed in which three optimization objectives, i.e., the debris removal priority, the MnADRP orbital transfer energy, and the number of required nanosatellites are modeled respectively. A modified genetic algorithm (GA) is also proposed to solve the dynamic multi-objective TSP. Finally, numerical experiments involving partially real-world the debris data set are conducted to verify the efficacy of the proposed models and the solution method.

Automatic trajectory planning for low-thrust active removal mission in low-earth orbit

In this paper two strategies are proposed to de-orbit up to 10 non-cooperative objects per year from the region within 800 and 1400km altitude in Low Earth Orbit (LEO). The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches. The first strategy is analogous to the Traveling Salesman Problem: the servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that reduces the perigee of the orbit. The second strategy is analogous to the Vehicle Routing Problem: the servicing spacecraft rendezvous and docks with an object, spirals it down to a lower altitude orbit, undocks, and then spirals up to the next target.In order to maximise the number of de-orbited objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimisation algorithm. The optimisation of the resulting sequence is realised using a direct transcription method based on an asymptotic analytical solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the J2 gravitational effect and the atmospheric drag.

粒子フィルタと最小平均二乗誤差法による軌道上浮遊物体の姿勢推定手法の実験的検証

To sustain space environment against the increase of the number of space debris, active removal of large debris is effective method. Attitude estimation of the floating target is one of the key technology for active removal mission. This paper presents experimental validation of attitude estimation with particle filter. In general particle filter method, estimated state values are obtained from the weighted average on the assumption that the state values are independent each other. However, attitude parameters such as quaternion are not independent each other. In this paper, Minimum Mean Square Error Approach (MMSE) is applied to estimate quaternion parameters. The proposed estimation method is verified by experiment.