Real-Time Collision Avoidance in the Environment with Moving Obstacles

In this work, a real-time collision avoidance algorithm was presented for autonomous navigation in the presence of fixed and moving obstacles in building environments. The current implementation is designed for autonomous navigation between waypoints of a predefined flight trajectory that would be performed by an UAV during tasks such as inspections or construction progress monitoring. It uses a simplified geometry generated from a point cloud of the scenario. In addition, it also employs information from 3D sensors to detect and position obstacles such as people or other UAVs, which are not registered in the original cloud. If an obstacle is detected, the algorithm estimates its motion and computes an evasion path considering the geometry of the environment. The method has been successfully tested in different scenarios, offering robust results in all avoidance maneuvers. Execution times were measured, demonstrating that the algorithm is computationally feasible to be implemented onboard an UAV.

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Review of Collision Avoidance and Path Planning Methods for Ships Utilizing Radar Remote Sensing

Remote Sensing ◽

10.3390/rs13163265 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3265 ◽

Cited By ~ 1

Author(s):

Agnieszka Lazarowska

Keyword(s):

Remote Sensing ◽

Comparative Analysis ◽

Path Planning ◽

Real Time ◽

Collision Avoidance ◽

Input Data ◽

Research Directions ◽

Radar Remote Sensing ◽

Moving Obstacles ◽

Planning Methods

The paper presents a comparative analysis of recent collision avoidance and real-time path planning algorithms for ships. Compared methods utilize radar remote sensing for target ships detection. Different recently introduced approaches are briefly described and compared. An emphasis is put on input data reception using a radar as a remote sensing device applied in order to detect moving obstacles such as encountered ships. The most promising methods are highlighted and their advantages and limitations are discussed. Concluding remarks include proposals of further research directions in the development of collision avoidance methods utilizing radar remote sensing.

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Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211024048 ◽

2021 ◽

pp. 095440702110240

Author(s):

Ziyu Zhang ◽

Chunyan Wang ◽

Wanzhong Zhao ◽

Jian Feng

Keyword(s):

Real Time ◽

Collision Avoidance ◽

Decentralized Control ◽

Control Strategy ◽

Centralized Control ◽

Lateral Control ◽

Time Performance ◽

Collision Avoidance Control ◽

Avoidance Control ◽

Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

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COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

Journal of Marine Science and Engineering ◽

10.3390/jmse9040405 ◽

2021 ◽

Vol 9 (4) ◽

pp. 405

Author(s):

Raphael Zaccone

Keyword(s):

Path Planning ◽

Real Time ◽

Collision Avoidance ◽

Autonomous Navigation ◽

Optimal Path ◽

Open Water ◽

Optimal Path Planning ◽

Guidance And Control ◽

Real Time Applications ◽

And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

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A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽

10.3390/s21124141 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4141

Author(s):

Wouter Houtman ◽

Gosse Bijlenga ◽

Elena Torta ◽

René van de Molengraft

Keyword(s):

Real Time ◽

Collision Avoidance ◽

Probabilistic Model ◽

Real Life ◽

Human Motion ◽

Model Based ◽

Multiple Hypotheses ◽

Navigation Algorithms ◽

The Right ◽

Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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