Obstacle Avoidance Guidance Research Articles

Collision probability sliding mode guidance for spacecraft autonomous obstacle avoidance under state uncertainty

Spacecraft must possess the capability of autonomous obstacle avoidance to ensure flight safety. Currently, the available methods for obstacle avoidance guidance are primarily deterministic and will fail with large state estimation errors, which necessitates the robustness of guidance algorithms towards uncertainties. A collision probability sliding mode guidance method is presented for the autonomous obstacle avoidance of spacecraft. Based on the multiple sliding mode surfaces guidance law, a sliding mode structure is proposed to realize obstacle avoidance in the presence of state uncertainty, taking into account the collision probability constraint. First, A multiple power reaching law is designed to enhance the convergence speed of the first sliding mode surface, so that the spacecraft system reaches the target state within a finite time. Subsequently, the collision probability gradient information function is defined according to the relative relation between the spacecraft and obstacles. This function is then combined with the improved reaching law to develop the second sliding mode surface. The sliding mode surface can quantify the collision probability in real-time, and generate corresponding obstacle avoidance guidance commands to prompt the spacecraft to move away from the obstacles according to the magnitude of collision probability, which is robust to the state uncertainty. The capability of the proposed guidance algorithm is validated by a series of simulations, including scenarios involving asteroid landing and spacecraft rendezvous, and the effectiveness and robustness in the face of uncertainties are confirmed.

A powered descent trajectory planning method with quantitative consideration of safe distance to obstacle

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.

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.

Autonomous Guidance and Flight Control on a Partial-Authority Black Hawk Helicopter

An autonomous guidance and flight control system was integrated and flight tested on a Black Hawk helicopter as part of an effort to provide all weather capability for U.S. Army fleet rotorcraft. The guidance and flight-control system components were previously flight tested on a full-authority helicopter, and were adapted to fly on the partial-authority test helicopter of this paper. The main autonomy guidance components consisted of the Risk Minimizing Obstacle Field Navigation algorithm, Safe Landing Area Determination algorithm, Mission Manager, and Integrated Cueing Environment. These components provided reactive obstacle avoidance guidance, landing site selection, pilot/autonomy interaction, and pilot situational awareness for degraded visual environments. In addition to the autonomy components, the Autonomous Partial-Authority Flight Control System provided a fully stabilized path-following capability to the guidance components. This paper describes how these components were adapted to a partial-authority helicopter that is typical of the current U.S. Army fleet. To test the system, a laser detection and ranging unit was used as a surrogate ranging device in lieu of an all-weather sensor system that was concurrently under development. Flight test results are presented from the fully integrated system navigating through terrain, selecting landing sites, and autonomously landing, while simultaneously keeping the pilot situationally aware of the autonomy’s intent.

Artificial Potential Function Safety and Obstacle Avoidance Guidance for Autonomous Rendezvous and Docking with Noncooperative Target

The problem of artificial potential function (APF) safety and obstacle avoidance guidance for autonomous rendezvous and docking of chaser spacecraft with noncooperative spacecraft is studied. The relative motion equation of the chaser and the target is established based on the line-of-sight coordinate system, the reference state is designed, and the corresponding state error is deduced. The attitude motion equation of the noncooperative target spacecraft in space is established. The safety and obstacle avoidance guidance problem of autonomous rendezvous and docking with noncooperative target is transformed into a path planning problem in a dynamic environment. The attractive potential function is designed according to the state error. In order to ensure that the chaser can safely approach the noncooperative target spacecraft, a safe corridor with ellipse cissoid is designed in the final approaching stage of autonomous rendezvous and docking. The obstacle is assumed to be a sphere with a certain radius to avoid its influence in the approach, and the obstacle potential function is designed based on the Gaussian function method. The total potential function of the system is designed according to the attractive potential function, the safe potential function, and the obstacle potential function. The total potential function of the system is modified to ensure that the reference state is the minimum of the total potential function of the system. The stability of the system is proven according to the Lyapunov stability principle, and the conditions for satisfying the monotonic decrease in the total potential function of the system are deduced. Finally, the effectiveness of the proposed method is verified by three sets of numerical simulations.

Image Segmentation-Based Unmanned Aerial Vehicle Safe Navigation

This work proposes a vision-based guidance scheme for an unmanned aerial vehicle navigating through urban environments while seeking a predefined goal point. Optical flow of image corner feature points is considered to segment obstacles from the image. An obstacle-avoidance guidance law is proposed to avoid the segmented obstacles. Additionally, detecting open space between segmented obstacles, a passage-following guidance law also presented for intelligent decision making. Analytic comparison with an existing methodology is carried out to highlight superior obstacle-avoidance properties of the proposed strategy. Simulations are carried out in a three-dimensional environment for single and multiple obstacles. Results comply with the analytic findings and present a much improved avoidance performance as compared to existing optical flow-based methods.

Discrete range clustering using Monte Carlo methods

For automatic obstacle avoidance guidance during rotorcraft low altitude flight a reliable model of the nearby environment is needed. Such a model may be constructed by applying surface fitting techniques to the dense range map obtained by active sensing using radars. However, for covertness passive sensing techniques using electro-optic sensors is desirable. As opposed to the dense range map obtained via active sensing, passive sensing algorithms produce reliable range at sparse locations and, therefore, surface fitting techniques to fill the gaps in the range measurement are not directly applicable. Both, for automatic guidance and as a display for aiding the pilot, these discrete ranges need to be grouped into sets which correspond to objects in the nearby environment. The focus of this paper is on using Monte Carlo methods for clustering range points into meaningful groups. We compare three different approaches and present results of application of these algorithms to an image sequence acquired by onboard cameras during a helicopter flight. Starting with an initial grouping, these algorithms are iteratively applied with a new group creation algorithm to determine the optimal number of groups and the optimal group membership. The results indicate that the simulated annealing methods do not offer any significant advantage over the basic Monte Carlo method for this discrete optimization problem.

Vision-based obstacle detection and grouping for helicopter guidance

Electro-optical sensors can be used to compute range to objects in the flight path of a helicopter. The computation is based on the optical flow/motion at different points in the image. The motion algorithms provide a sparse set of ranges to discrete features in the image sequence as a function of azimuth and elevation. For obstacle avoidance guidance and display purposes, these discrete set of ranges, varying from a few hundreds to several thousands, need to be grouped into sets which correspond to objects in the real world. This paper presents a new method for object segmentation based on clustering the sparse range information provided by motion algorithms together with the spatial relation provided by the static image. The range values are initially grouped into clusters based on depth. Subsequently, the clusters are modified by using the K-means algorithm in the inertial horizontal plane and the minimum spanning tree algorithms in the image plane. The object grouping allows interpolation within a group and enables the creation of dense range maps. Researchers in robotics have used densely scanned sequence of laser range images to build three-dimensional representation of the outside world. Thus, modeling techniques developed for dense range images can be extended to sparse range images. The paper presents object segmentation results for a sequence of flight images.

Integration of active and passive sensors for obstacle avoidance

A study is made of the automatic obstacle-avoidance guidance problem under the operational constraints imposed by the rotorcraft nap-of-the-earth (NOE) environment. The topic is discussed under two different circumstances. The first one assumes that a full range map is available, irrespective of the type of sensor being used. Two approaches are proposed to extend a two-dimensional obstacle-avoidance concept presented in a previous report. The situation where only a sparse range map is available from a passive sensor is also treated. An integrated approach to augment the passive sensor with an active one is discussed, along with the problem of data fusion and how it is affected by the characteristics of NOE flight.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>