Yiming Yang

Scaling sampling-based motion planning to humanoid robots

Planning balanced and collision-free motion for humanoid robots is non-trivial, especially when they are operated in complex environments, such as reaching targets behind obstacles or through narrow passages. Research has been done in particular to plan such complex motion on humanoids, however, these approaches are typically restricted to particular robot platforms and environments, which can not be easily replicated nor applied. We propose a method that allows us to apply existing sampling-based algorithms directly to plan trajectories for humanoids by utilizing a customized state space representation, biased sampling strategies, and a steering function based on a robust inverse kinematics solver. Our approach requires no prior offline computation, thus one can easily transfer the work to new robot platforms. We tested the proposed method by solving practical reaching tasks on a 38 degrees-of-freedom humanoid robot, NASA Valkyrie, showing that our method is able to generate valid motion plans that can be executed on advanced full-size humanoid robots.

iDRM: Humanoid motion planning with realtime end-pose selection in complex environments

In this paper, we propose a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base system to find valid and sufficient end-poses in complex and changing environments in real-time. End-pose planning, i.e. finding valid stance locations and collision-free reaching configurations, is an essential problem in humanoid applications, such as providing goal states for walking and motion planners. However, it is non-trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed approach, iDRM, customizes the robot-to-workspace occupation list and uses an online update algorithm to enable efficient reconstruction of the reachability map to guarantee that the selected end-poses are always collision-free. The iDRM was evaluated in a variety of reaching tasks using the 38 degree-of-freedom (DoF) humanoid robot Valkyrie. Our results show that the approach is capable of finding valid end-poses in a fraction of a second.

Real-time motion adaptation using relative distance space representation

Reacting to environment changes is a big challenge for real world robot applications. This paper presents a novel approach that allows the robot to quickly adapt to changes, particularly in the presence of moving targets and dynamic obstacles. Typically, a configuration space replanning or adaptation is required if the environment is changed. Rather, our method aims to maintain a plan, in a relative distance space rather than configuration space, that can be valid in different environments. In addition, we introduce an incremental planning structure that allows us to handle unexpected obstacles that may appear during execution. The main contribution is that the relative distance space representation encodes pose re-targeting, reaching and avoiding tasks within one unified cost term that can be solved in real-time to achieve a fast implementation for high degree of freedom (DOF) robots. We evaluate our method on a 7 DOF LWR robot arm, and a 14 DOF dual-arm Baxter robot.

Efficient Humanoid Motion Planning on Uneven Terrain Using Paired Forward-Inverse Dynamic Reachability Maps

A key prerequisite for planning manipulation together with locomotion of humanoids in complex environments is to find a valid end-pose with a feasible stance location and a full-body configuration that is balanced and collision-free. Prior work based on the inverse dynamic reachability map assumed that the feet are placed next to each other around the stance location on a horizontal plane, and the success rate was correlated with the coverage density of the sampled space, which in turn is limited by the memory required for storing the map. In this letter, we present a framework that uses a paired forward-inverse dynamic reachability map to exploit a greater modularity of the robots inherent kinematic structure. The combinatorics of this novel decomposition allows greater coverage in the high-dimensional configuration space while reducing the number of stored samples. This permits drawing samples from a much richer dataset to effectively plan end-poses for both single-handed and bimanual tasks on uneven terrains. This novel method was demonstrated on the 38-DoF NASA Valkyrie humanoid by utilizing and exploiting whole body redundancy for accomplishing manipulation tasks on uneven terrains while avoiding obstacles.

EXOTica: An Extensible Optimization Toolset for Prototyping and Benchmarking Motion Planning and Control

In this research chapter, we will present a software toolbox called EXOTica that is aimed at rapidly prototyping and benchmarking algorithms for motion synthesis. We will first introduce the framework and describe the components that make it possible to easily define motion planning problems and implement algorithms that solve them. We will walk you through the existing problem definitions and solvers that we used in our research, and provide you with a starting point for developing your own motion planning solutions. The modular architecture of EXOTica makes it easy to extend and apply to unique problems in research and in industry. Furthermore, it allows us to run extensive benchmarks and create comparisons to support case studies and to generate results for scientific publications. We demonstrate the research done using EXOTica on benchmarking sampling-based motion planning algorithms, using alternate state representations, and integration of EXOTica into a shared autonomy system. EXOTica is an open-source project implemented within ROS and it is continuously integrated and tested with ROS Indigo and Kinetic. The source code is available at https://github.com/ipab-slmc/exotica and the documentation including tutorials, download and installation instructions are available at https://ipab-slmc.github.io/exotica.

HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes

In this letter, we first theoretically prove the conditions and boundaries of resolution completeness for deterministic roadmap methods with a discretized workspace. A novel variant of such methods, the hierarchical dynamic roadmap (HDRM), is then proposed for solving complex planning problems. A unique hierarchical structure to efficiently encode the configuration-to-workspace occupation information is introduced and allows the robot to check the collision state of tens of millions of samples on-the-fly—the number of which was previously strictly limited by available memory. The hierarchical structure also significantly reduces the time for path searching, hence, the robot is able to find feasible motion plans in real-time in extremely constrained environments. A rigorous benchmarking shows that HDRM is robust and computationally fast compared with classical dynamic roadmap methods and other state-of-the-art planning algorithms. Experiments on the seven degree-of-freedom KUKA LWR robotic arm integrated with live perception further validate the effectiveness of HDRM in complex environments.