We created the new improved version of Messor robot called Messor II. The new robot has improved 'power to mass' ratio and mechanical design inspired by insects' physiology. Control board is based on PandaBoard (multi-core, ARM-based, Linux operated, low energy consumption). The robot is equipped with Asus Xtion for environment sensing and Xsens MTi for inclination measurement.

The algorithm called guided RRT for the six-legged walking robot considers the problem of planning a sequence of elementary motions (steps) and its implementation on the real robot. It takes into account that the robot has limited abilities to perceive the environment. The A* algorithm is used for long horizon planning on a map obtained from the stereo camera data. Then, the RRT-Connect method is used to find a sequence of feasible movements for the body and feet of the robot on a more precise map obtained by using the Hokuyo laser rangefinder.

Missions of walking robots in distant and dangerous areas require using of the teleoperation mode. However, the capabilities of a human operator to sense the terrain and to control the robot are limited. Thus, a walking robot should have enough autonomy to take an advantage of its high locomotion capabilities in spite of a limited feedback from the remote operator. This paper presents a method for real-time motion planning on a rugged terrain. The proposed method employs several modules for the planning of the robot’s trunk path, foothold selection, planning trajectories of the robot’s feet, and analysis of the robot stability and the workspace of the legs. By using this method the robot can autonomously find a path to the desired position and discriminate between traversable and non-traversable areas. The Rapidly-exploring Random Trees (RRT) algorithm is used as a backbone of the proposed solution. Results of simulations and experiments on the real Messor robot are presented.

This research verifies the concept of a gait modification for a six-legged robot walking on rough terrain. The robot modifies the sequence of leg’s transfer phases according to the roughness of the terrain. On a flat terrain the robot uses tripod gait and according to the roughness factor the gait is modified to more secure cyclic wave and free gaits. Results of simulations with the motion planning algorithm on rough terrain are provided. The influence of the proposed strategy on the efficiency of the motion planning algorithm is presented.

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.