Scope and Topics

Nowadays robots are available in common activities as medical scanning devices, prosthesis, cars with some degree of autonomy, at home as cleaning robots and also as toys. To understand what a robot is we take the international federation of robotics definition: a robot is an actuated mechanism programmable in two or more axes with a degree of autonomy, moving within its environment, to perform intended tasks. That is, a robot can manipulate and transport objects but can also assist humans to rehabilitate different limbs and even to replace them, e.g. in surgery or as automated wheelchair. To accomplish these tasks several techniques have been developed to define their movements and to achieve the required degree of autonomy. In that direction optimization plays an important role to obtain reliable paths that allow robots fulfill their tasks in an efficient and safe way.

The goal of the Numerical and Evolutionary Optimization (NEO) workshop series is to bring together people from all optimization fields to discuss, compare and merge their complimentary perspectives. NEO encourages the development of fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of each underlying paradigm, while also being applicable to a broader class of problems. Moreover, NEO fosters the understanding and adequate treatment of real-world problems, particularly in emerging fields that affect us.

Though all the above issues will be highly welcome for this year’s edition, the "NEO Robotics", it will have a particular focus on mobile and cooperative robots, service robots, medical and agricultural applications, design techniques, control and planning algorithms.

Topics of interest include (but are not limited to):

A) Robotics:

  • Mobile robotics: wheels, drones, mobile manipulators, aquatic robots
  • Cooperative robots: manipulators, multivehicle systems in indoors and outdoors
  • Service robots
  • Robots for medical applications
  • Robots in agricultural applications
  • Techniques to design robots
  • Control and planning algorithms

B) Search and Optimization:

  • Single- and multi-objective optimization
  • Advances in evolutionary algorithms and genetic programming
  • Hybrid and memetic algorithms
  • Set oriented numerics
  • Stochastic optimization
  • Robust optimization

C) Real World Problems:

  • Health systems
  • Computer vision and pattern recognition
  • Energy conservation and prediction
  • Modeling and control of real-world systems
  • Smart cities