Although legged locomotion over a moderately rugged terrain can be accomplished by employing simple reactions to the ground contact information, a more effective approach, which allows predictively avoiding obstacles, requires a model of the environment and a control algorithm that takes this model into account when planning footsteps and leg movements. This article 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 onboard perception, provided that a reasonable general path is known. An efficient method for real-time building of a local elevation map from sparse two-dimensional (2D) range measurements of a 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.
Achieving full autonomy in a mobile robot requires combining robust environment perception with onboard sensors, efficient environment mapping, and real‐time motion planning. All these tasks become more challenging when we consider a natural, outdoor environment and a robot that has many degrees of freedom (DOF). In this paper, we address the issues of motion planning in a legged robot walking over a rough terrain, using only its onboard sensors to gather the necessary environment model. The proposed solution takes the limited perceptual capabilities of the robot into account. A multisensor system is considered for environment perception. The key idea of the motion planner is to use the dual representation concept of the map: (i) a higher‐level planner applies the A* algorithm for coarse path planning on a low‐resolution elevation grid, and (ii) a lower‐level planner applies the guided‐RRT (rapidly exploring random tree) algorithm to find a sequence of feasible motions on a more precise but smaller map. This paper contributes a new method that can identify the terrain traversability cost to the benefit of the A* algorithm. A probabilistic regression technique is applied for the traversability assessment with the typical RRT‐based motion planner used to explore the space of traversability values. The efficiency of our motion planning approach is demonstrated in simulations that provide ground truth data unavailable in field tests. However, the simulation‐verified approach is then thoroughly tested under real‐world conditions in experiments with two six‐legged walking robots having different perception systems.
This article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.
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