Flocking and collision avoidance problem of a singular Cucker-Smale model with external perturbations

2021 ◽  
pp. 126718
Author(s):  
Rundong Zhao ◽  
Qiming Liu ◽  
Huazong Zhang
Keyword(s):  
Collision Avoidance ◽  
External Perturbations ◽  
Avoidance Problem

Automatic Control of Underway Replenishment Maneuvers in a Random Sea

1978 ◽  
Vol 22 (01) ◽  
pp. 20-28
Author(s):  
Reidar Alvestad
Keyword(s):  
Computer Simulation ◽  
Automatic Control ◽  
Collision Avoidance ◽  
Nonlinear Interaction ◽  
Interaction Forces ◽  
Random Seas ◽  
Close Proximity ◽  
Controller Performance ◽  
Hybrid Computer ◽  
Avoidance Problem

This paper describes a hybrid computer simulation of two ships performing replenishment operations in random seas. Such operations present collision hazards due to the nonlinear interaction forces and moments which result from close proximity maneuvering while underway. Maneuvers are simulated to demonstrate automatic controller performance during station-keeping, station-changing, and the approach and breakaway phases of typical underway replenishment (UNREP) operations. Results indicate that automatic control should be considered as a possible solution to the UNREP collision avoidance problem.

scholarly journals Velocity Obstacle Approaches for Multi-Agent Collision Avoidance

2019 ◽  
Vol 07 (01) ◽  
pp. 55-64 ◽  
Author(s):  
James A. Douthwaite ◽  
Shiyu Zhao ◽  
Lyudmila S. Mihaylova
Keyword(s):  
Collision Avoidance ◽  
Critical Analysis ◽  
Monte Carlo Analysis ◽  
The Other ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Velocity Obstacle ◽  
Scalable Computation ◽  
Avoidance Problem

This paper presents a critical analysis of some of the most promising approaches to geometric collision avoidance in multi-agent systems, namely, the velocity obstacle (VO), reciprocal velocity obstacle (RVO), hybrid-reciprocal velocity obstacle (HRVO) and optimal reciprocal collision avoidance (ORCA) approaches. Each approach is evaluated with respect to increasing agent populations and variable sensing assumptions. In implementing the localized avoidance problem, the author notes a problem of symmetry not considered in the literature. An intensive 1000-cycle Monte Carlo analysis is used to assess the performance of the selected algorithms in the presented conditions. The ORCA method is shown to yield the most scalable computation times and collision likelihood in the presented cases. The HRVO method is shown to be superior than the other methods in dealing with obstacle trajectory uncertainty for the purposes of collision avoidance. The respective features and limitations of each algorithm are discussed and presented through examples.

Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling

2016 ◽  
Vol 36 (3) ◽  
pp. 318-332 ◽  
Author(s):  
Zhenyu Wu ◽  
Guang Hu ◽  
Lin Feng ◽  
Jiping Wu ◽  
Shenglan Liu
Keyword(s):  
Mobile Robot ◽  
Collision Avoidance ◽  
Potential Field ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Artificial Potential Field ◽  
Content Type ◽  
Negative Pole ◽  
Avoidance Problem ◽  
Three Dimensional Space

Purpose This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the obstacles with a new method named the obstacle envelope modelling (OEM). Design/methodology/approach The obstacles of arbitrary shapes are enveloped in OEM using the primitive, which is an ellipse in a two-dimensional plane or an ellipsoid in a three-dimensional space. As the surface details of obstacles are neglected elegantly in OEM, the workspace of a mobile robot is made simpler so as to increase the capability of APF in a clustered environment. Findings Further, a dipole is applied to the construction of APF produced by each obstacle, among which the positive pole pushes the robot away and the negative pole pulls the robot close. Originality/value As a whole, the dipole leads the robot to make a derivation around the obstacle smoothly, which greatly reduces the local minima and trajectory oscillations. Computer simulations are conducted to demonstrate the effectiveness of the proposed approach.

Online collision avoidance trajectory planning for spacecraft proximity operations with uncertain obstacle

2021 ◽  
pp. 095441002110561
Author(s):  
Run-de Zhang ◽  
Wei-wei Cai ◽  
Le-ping Yang ◽  
Cheng Si
Keyword(s):  
Collision Avoidance ◽  
Predictive Control ◽  
Trajectory Planning ◽  
Three Dimensional ◽  
Performance Criteria ◽  
Proximity Operations ◽  
Online Planning ◽  
Planning Framework ◽  
Planning Methods ◽  
Avoidance Problem

The spacecraft relative motion trajectory planning is one of the enabling techniques for autonomous proximity operations, especially in the increasingly complicated mission environments. Most traditional trajectory planning methods focus on improving the performance criteria in the deterministic conditions, whereas various uncertain elements in practice would significantly degrade the trajectory performance. Considering the uncertainties underlying the collision avoidance constraints, this paper suggests a model predictive control based online trajectory planning framework in which the obstacle information in higher-precision would be consistently updated by the onboard sensor. To improve the computational efficiency of the online planning framework, the rotating hyperplane (RH) technique is utilized to transform the nonlinear ellipsoidal keep-out zone constraints into convex formulations. And the concept of rotation window is introduced to eliminate the unexpected mismatch between the spacecraft motion and hyperplane rotation in the conventional RH method, which in sequence improves the RH method’s capability for multiple obstacle avoidance problem. Moreover, a three-dimensional (3-D) extension strategy is proposed to simplify the computation procedure when applying the RH method for a 3-D collision avoidance problem. Numerical simulations are carried out to validate the performance of the proposed online trajectory planning framework in addressing the uncertain collision avoidance constraints.

Sign in / Sign up

Export Citation Format

Share Document