Obstacle avoidance in space robotics: Review of major challenges and proposed solutions

Rybus, Tomasz

doi:10.1016/j.paerosci.2018.07.001

Cited by 81 publications

(41 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the authors of [32] provide a systematic review of sensors and vision-based techniques for vehicle detection and tracking for collision avoidance systems on the specific context of on-road driving. Another interesting review can be found in [33], where the authors focus on space robotics and identify the major challenges in collision-free trajectory planning for manipulators mounted on large orbital structures, small satellites, and space robots that navigate in proximity to large orbital structures. Other authors have reviewed the main techniques for improving the autonomy of unnamed surface vehicles with regard to navigation, guidance, control and motion planning with respect to the international regulations for avoiding collisions at sea [34].…”

Section: Related Workmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

View full text Add to dashboard Cite

Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

show abstract

Section: Related Workmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

View full text Add to dashboard Cite

show abstract

“…An additional single point-to-point trajectory is presented for the purpose of showing the static obstacle avoidance ability of A* in the 4D space. The obstacle defined for this purpose is a cube (see figure 15) that makes it impossible for the robot to directly move from coordinates ( , , , ) = (1,30,9,24) to coordinates ( , , , ) = (1,44,9,24) in an approximately straight-line trajectory (see figure 15, above). The robot configuration chosen is with wrist orientation CENTER and it appears in the Matlab ® simulation in blue color both for the trajectory (see figure 15, above) and the volume occupancy (see figure 15, below).…”

Section: F Code and Benchmarkingmentioning

confidence: 99%

“…The necessity of a third dimension to describe the industrial robot movements in Cartesian space has already been challenged by many researchers introducing controls in the high-dimensional configuration space [7][8][9][10][11]. However, the advantages of using the configuration space are traded off with a greater difficulty in visualizing and operating in such space, to the point that representing other interacting objects in the configuration space became a self-standing topic for study [1,2,[12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications

Giorgio

Wang

2020

IEEE Access

View full text Add to dashboard Cite

This paper argues that an efficient artificial intelligence control algorithm needs the built-in symmetries of an industrial robot manipulator to be further characterized and exploited. The product of this enhancement is a four-dimensional (4D) discrete cylindrical grid space that can directly replace complex robot models. A* is chosen for its wide use among such algorithms to study the advantages and disadvantages of steering the robot manipulator within the 4D cylindrical discrete grid. The study shows that this approach makes it possible to control a robot without any specific knowledge of the robot kinematic and dynamic models at planning and execution time. In fact, the robot joint positions for each grid cell are pre-calculated and stored as knowledge, then quickly retrieved by the pathfinding algorithm when needed. The 4D cylindrical discrete space has both the advantages of the configuration space and the three-dimensional Cartesian workspace of the robot. Since path optimization is the core of any search algorithms, including A*, the 4D cylindrical grid provides for a search space that can embed further knowledge in form of cell properties, including the presence of obstacles and volumetric occupancy of the entire industrial robot body for obstacle avoidance applications. The main trade-off is between a limited capacity for pre-computed grid knowledge and the path search speed. This innovative approach encourages the use of search algorithms for industrial robotic applications, opens up to the study of other robot symmetries present in different robot models and lays a foundation for the application of dynamic obstacle avoidance algorithms.

show abstract

“…The pose estimation of uncooperative space objects is a key technology for many space missions such as target recognition and on-orbit service [1,2]. In contrast to other state-of-the-art systems, a monocular camera ensures pose estimation under low power and low hardware complexity [3].…”

Section: Introductionmentioning

confidence: 99%

Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

Ren

Jiang

Wang

2020

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Estimating the 3D pose of the space object from a single image is an important but challenging work. Most of the existing methods estimate the 3D pose of known space objects and assume that the detailed geometry of a specific object is known. These methods are not available for unknown objects without the known geometry of the object. In contrast to previous works, this paper devotes to estimate the 3D pose of the unknown space object from a single image. Our method estimates not only the pose but also the shape of the unknown object from a single image. In this paper, a hierarchical shape model is proposed to represent the prior structure information of typical space objects. On this basis, the parameters of the pose and shape are estimated simultaneously for unknown space objects. Experimental results demonstrate the effectiveness of our method to estimate the 3D pose and infer the geometry of unknown typical space objects from a single image. Moreover, experimental results show the advantage of our approach over the methods relying on the known geometry of the object.

show abstract

Obstacle avoidance in space robotics: Review of major challenges and proposed solutions

Cited by 81 publications

References 69 publications

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications

Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

Contact Info

Product

Resources

About