In this manuscript, we tackle the problem of a continuous localization of a legged robot. We propose a novel, optimization-based procedure for the state estimation of the robot using measurements from internal sensors (legged odometry). Then, we propose the optimization-based integration of the legged odometry and the visual SLAM output. The proposed multi-modal localization system can continuously estimate the pose of the robot in various conditions despite fast motions of the robot, slippages or image motion blur. We provide the results of the real-time implementation of the proposed method on a multi-legged walking robot. We compare the proposed localization method to other state of the art localization systems and provide the dataset for future comparisons.
We propose a new mapping method based on Normal Distribution Transform Occupancy Maps (NDT-OM) for environment exploration. Our goal is to propose a new architecture which can be used by an industrial mobile robot in a priori unknown environment. The mobile robot introduced in a new environment has to explore the workspace, localize itself and build a map. Current state of the art methods require storing all data collected during this stage and finally build a dense model of the environment. We propose a method which allows building local dense maps of the environment which are organized in a graph-like structure. The change in the registered trajectory of the robot, which may occur after loop closure detection, can be easily utilized by our architecture. Finally, we build a global map which can be later used for collision checking and motion planning.
We propose a new mammal-like mechanical design of the compliant robotic leg. We propose the application of elastic components to reduce the mechanical impact during landing phase and protect the gearboxes of the servomotors. We also use the elastic tendon which stores the energy in springs. The stored energy is then released at the beginning of the flight phase to increase the height of the jump. We propose and verify the dynamic model of the leg. Finally, in the series of experiments, we show the mechanical properties of the leg.
This paper considers motion planning for a six-legged walking robot in rough terrain, considering both the geometry of the terrain and its semantic labeling. The semantic labels allow the robot to distinguish between different types of surfaces it can walk on, and identify areas that cannot be negotiated due to their physical nature. The proposed environment map provides to the planner information about the shape of the terrain, and the terrain class labels. Such labels as “wall” and “plant” denote areas that have to be avoided, whereas other labels, “grass”, “sand”, “concrete”, etc. represent negotiable areas of different properties. We test popular classification algorithms: Support Vector Machine and Random Trees in the task of producing proper terrain labeling from RGB-D data acquired by the robot. The motion planner uses the A∗ algorithm to guide the RRT-Connect method, which yields detailed motion plans for the multi-d.o.f. legged robot. As the A∗ planner takes into account the terrain semantic labels, the robot avoids areas which are potentially risky and chooses paths crossing mostly the preferred terrain types. We report experimental results that show the ability of the new approach to avoid areas that are considered risky for legged locomotion.
We propose the gait control strategy for a six-legged robot walking on rough terrain. To walk efficiently on rough terrain the robot uses proprioceptive sensors only. The robot detects contact with the ground and uses Attitude and Heading Reference System (AHRS) unit to measure the inclination of the platform. We propose a single-step procedure to compute inclination of the robot's platform taking into account the terrain slope and kinematic margin of each robot's leg. Additionally, we use a procedure, which keeps the robot stable during walking on rough terrain. We show in the experiments that the robot is capable of climbing slopes inclined by 25 deg and walking efficiently on rough terrain.
Page 1 of 5