Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments

We present a theoretical analysis of a recent whole body motion planning method, the Randomized Possibility Graph, which uses a high-level decomposition of the feasibility constraint manifold in order to rapidly find routes that may lead to a solution. These routes are then examined by lower-level planners to determine feasibility. In this paper, we show that this approach is probabilistically complete for bipedal robots performing quasi-static walking in "semi-unstructured" environments. Furthermore, we show that the decomposition into higher and lower level planners allows for a considerably higher rate of convergence in the probability of finding a solution when one exists. We illustrate this improved convergence with a series of simulated scenarios

Real-Time Navigation for Bipedal Robots in Dynamic Environments

The popularity of mobile robots has been steadily growing, with these robots being increasingly utilized to execute tasks previously completed by human workers. Bipedal robots, a subset of mobile robots, have been a popular field of research due to the large range of tasks for which they can be utilized. For bipedal robots to see a similarly successful integration into society, robust autonomous navigation systems need to be designed. These autonomous navigation systems can generally be divided into three components: perception, planning, and control. A holistic navigation system for bipedal robots must successfully integrate all three components of the autonomous navigation problem to enable robust real-world navigation. Many works expand on fundamental planning algorithms such as A*, RRT, and PRM to address the unique problems of bipedal motion planning. However, many of these works lack several components required for autonomous navigation systems such as real-time perception, mapping, and localization processes. Thus, the goal of this research is to develop a real-time navigation system for bipedal robots in dynamic environments which addresses all components of the navigation problem. To achieve this: a depth-based sensor suite was used for obstacle segmentation, mapping, and localization. Additionally, a two-stage planning system generates collision-free and kinematically feasible trajectories robust to unknown and dynamic environments. Finally, the Digit bipedal robot's default low-level controller is used to execute these feasible trajectories. The navigation system was first validated on a differential drive robot in simulation. Next, the system was adapted for bipedal robots and validated in hardware on the Digit bipedal robot. In both simulation and in hardware experiments, the implemented navigation system facilitated successful navigation in unknown environments and in environments with both static and dynamic obstacles.Undergraduate Honors Committee in the College of EngineeringNo embargoAcademic Major: Computer Science and Engineerin

Planification de pas pour robots humanoïdes : approches discrètes et continues

Dans cette thèse nous nous intéressons à deux types d'approches pour la planification de pas pour robots humanoïdes : d'une part les approches discrètes où le robot n'a qu'un nombre fini de pas possibles, et d'autre part les approches où le robot se base sur des zones de faisabilité continues. Nous étudions ces problèmes à la fois du point de vue théorique et pratique. En particulier nous décrivons deux méthodes originales, cohérentes et efficaces pour la planification de pas, l'une dans le cas discret (chapitre 5) et l'autre dans le cas continu (chapitre 6). Nous validons ces méthodes en simulation ainsi qu'avec plusieurs expériences sur le robot HRP-2. ABSTRACT : In this thesis we investigate two types of approaches for footstep planning for humanoid robots: on one hand the discrete approaches where the robot has only a finite set of possible steps, and on the other hand the approaches where the robot uses continuous feasibility regions. We study these problems both on a theoretical and practical level. In particular, we describe two original, coherent and efficient methods for footstep planning, one in the discrete case (chapter 5), and one in the continuous case (chapter 6). We validate these methods in simulation and with several experiments on the robot HRP-2

An Adaptive Action Model for Legged Navigation Planning

Abstract — Navigation planning for legged robots via foot placement planning has enabled several humanoids to traverse interesting environments autonomously. In this paper we explore methods of adapting foot placement actions to the terrain during the search process, allowing for fuller use of the robot’s capabilities, and better resulting paths. We show the results of these adaptive action models for both the humanoid HRP-2 and the quadruped LittleDog. I