International Journal of Robotics Research
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Rigid Body Dynamics Algorithms
Rigid Body Dynamics Algorithms
Operational Space Control: A Theoretical and Empirical Comparison
International Journal of Robotics Research
Learning locomotion over rough terrain using terrain templates
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning locomotion over rough terrain using terrain templates
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Optimization and learning for rough terrain legged locomotion
International Journal of Robotics Research
Learning, planning, and control for quadruped locomotion over challenging terrain
International Journal of Robotics Research
Comprehensive summary of the Institute for Human and Machine Cognition's experience with LittleDog
International Journal of Robotics Research
Learning variable impedance control
International Journal of Robotics Research
Optimal distribution of contact forces with inverse-dynamics control
International Journal of Robotics Research
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Many critical elements for statically stable walking for legged robots have been known for a long time, including stability criteria based on support polygons, good foothold selection, recovery strategies to name a few. All these criteria have to be accounted for in the planning as well as the control phase. Most legged robots usually employ high gain position control, which means that it is crucially important that the planned reference trajectories are a good match for the actual terrain, and that tracking is accurate. Such an approach leads to conservative controllers, i.e. relatively low speed, ground speed matching, etc. Not surprisingly such controllers are not very robust - they are not suited for the real world use outside of the laboratory where the knowledge of the world is limited and error prone. Thus, to achieve robust robotic locomotion in the archetypical domain of legged systems, namely complex rough terrain, where the size of the obstacles are in the order of leg length, additional elements are required. A possible solution to improve the robustness of legged locomotion is to maximize the compliance of the controller. While compliance is trivially achieved by reduced feedback gains, for terrain requiring precise foot placement (e.g. climbing rocks, walking over pegs or cracks) compliance cannot be introduced at the cost of inferior tracking. Thus, model-based control and - in contrast to passive dynamic walkers - active balance control is required. To achieve these objectives, in this paper we add two crucial elements to legged locomotion, i.e., floating-base inverse dynamics control and predictive force control, and we show that these elements increase robustness in face of unknown and unanticipated perturbations (e.g. obstacles). Furthermore, we introduce a novel line-based COG trajectory planner, which yields a simpler algorithm than traditional polygon based methods and creates the appropriate input to our control system. We show results from both simulation and real world of a robotic dog walking over nonperceived obstacles and rocky terrain. The results prove the effectivity of the inverse dynamics/force controller. The presented results show that we have all elements needed for robust all-terrain locomotion, which should also generalize to other legged systems, e.g., humanoid robots.