In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these…
In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands a user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto‐optimal points, instead of a single point. In this paper, Pareto‐based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. These algorithms are based on Continuous Evolutionary Algorithms, which were developed by the authors to solve single‐objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche‐formation method for fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto‐optimal tradeoff surface. Finally, the validity of this method has been demonstrated through some numerical examples.
The purpose of this paper is to present a localisation system for an indoor rotary‐wing micro aerial vehicle (MAV) that uses three onboard LEDs and base station mounted…
The purpose of this paper is to present a localisation system for an indoor rotary‐wing micro aerial vehicle (MAV) that uses three onboard LEDs and base station mounted active vision unit.
A pair of blade mounted cyan LEDs and a tail mounted red LED are used as on‐board landmarks. A base station tracks the landmarks and estimates the pose of the MAV in real time by analysing images taken using an active vision unit. In each image, the ellipse formed by the cyan LEDs is used for 5 degree of freedom (DoF) pose estimation with yaw estimation from the red LED providing the 6th DoF.
About 1‐3.5 per cent localisation error of the MAV at various ranges, rolls and angular speeds less than 45°/s relative to the base station at known location indicates that the MAV can be accurately localised at 9‐12 Hz in an indoor environment.
Line‐of‐sight between the base station and MAV is necessary while limited accuracy is evident in yaw estimation at long distances. Additional yaw sensors and dynamic zoom are among future work.
Provided an unmanned ground vehicle (UGV) as the base station equipped with its own localisation sensor, the developed system encourages the use of autonomous indoor rotary‐wing MAVs in various robotics applications, such as urban search and rescue.
The most significant contribution of this paper is the innovative LED configuration allowing full 6 DoF pose estimation using three LEDs, one camera and no fixed infrastructure. The active vision unit enables a wide range of observable flight as the ellipse generated by the cyan LEDs is recognisable from almost any direction.