The Ragno and Messor robots tries to predictively avoid obstacles. It requires a model of the environment, and a control algorithm that takes this model into account when planning footsteps and leg movements. This research addresses the issues of terrain perception and modeling, and foothold selection in a walking robot. An integrated system is presented that allows a legged robot to traverse previously unseen, uneven terrain using only on-board perception, provided a reasonable general path is known. An efficient method for real-time building of a local elevation map from sparse 2D range measurements of the miniature 2D laser scanner is described. The terrain mapping module supports a foothold selection algorithm, which employs unsupervised learning to create an adaptive decision surface. The robot can learn from realistic simulations, therefore no a priori expert-given rules or parameters are used. The usefulness of our approach is demonstrated in experiments with the six-legged robot Messor. We discuss the lessons learned in field tests, and the modifications to our system that turned out to be essential for successful operation under real-world conditions.

The algorithm allows to generate various gaits for hexapod walking robots. This method is based on kinematic model of the robot an can be used on flat terrain. An implementation of the tripod gait has been shown although any type of static stable gait can be implemented. The use in teleoperation and trajectory planning system has been shown.

The simulator models the physical characteristics of the robot, including its dynamics, and the way that a real robot interacts with the environment. The simulator is based on the Open Dynamics Engine (ODE). The ODE provides tools for simulation of rigid body dynamics and of complex structures with defined joints. It also provides an engine to detect collisions and to simulate friction between bodies. Each robot link is represented by a cube with the appropriate dimensions and mass. The simulator provides virtual measurements of all the kinematic variables of the robot, as well as the forces/moments in all the joints. Simple visualization of the robot model is implemented by using the OpenGL routines.

The objective of this experiments is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns.

Since 2007 a family of new hexapod robots has been developed at CIE. The Ragno robots are results of Master Theses. The Messor robot was designed in the project funded by the Polish Ministry of Science and Higher Education Grant No. N514 294635. I was mainly responsible for the electronic design and control system of the robots. The robots are used to develop algorithm and methods for autonomous locomotion in an unknown and unstructured environment.