4 research outputs found
Robustness analysis of evolutionary controller tuning using real systems
A genetic algorithm (GA) presents an excellent method for controller parameter tuning. In our work, we evolved the heading as well as the altitude controller for a small lightweight helicopter. We use the real flying robot to evaluate the GA's individuals rather than an artificially consistent simulator. By doing so we avoid the ldquoreality gaprdquo, taking the controller from the simulator to the real world. In this paper we analyze the evolutionary aspects of this technique and discuss the issues that need to be considered for it to perform well and result in robust controllers
Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations
For robots to handle the numerous factors that can affect them in the real
world, they must adapt to changes and unexpected events. Evolutionary robotics
tries to solve some of these issues by automatically optimizing a robot for a
specific environment. Most of the research in this field, however, uses
simplified representations of the robotic system in software simulations. The
large gap between performance in simulation and the real world makes it
challenging to transfer the resulting robots to the real world. In this paper,
we apply real world multi-objective evolutionary optimization to optimize both
control and morphology of a four-legged mammal-inspired robot. We change the
supply voltage of the system, reducing the available torque and speed of all
joints, and study how this affects both the fitness, as well as the morphology
and control of the solutions. In addition to demonstrating that this real-world
evolutionary scheme for morphology and control is indeed feasible with
relatively few evaluations, we show that evolution under the different hardware
limitations results in comparable performance for low and moderate speeds, and
that the search achieves this by adapting both the control and the morphology
of the robot.Comment: Accepted to the 2018 Genetic and Evolutionary Computation Conference
(GECCO