Real-Time Support of Haptic Interaction by Means of Sampling-Based Path Planning

Haptic feedback enables the support of a human during the interaction with an environment. A variety of concepts have been developed to achieve an effective haptic support of the user in specific scenarios, e.g. Virtual Fixtures. However, most of these methods do not enable an adaptive support of the motion from a user within a (real or virtual) environment, which would be desirable in many situations. Especially when dynamical obstacles are involved or when the desired motion of the human is not known beforehand, an online computation of this support is essential, which should be based on a fast and effective determination of feasible motions.In contrast to other methods, sampling-based path planning is applicable to arbitrary interaction scenarios and enables to find a solution if it exists at all. Thus, it seems to be ideally suited for a generic framework that is able to deal with various kinematics, as e.g. a virtual prototyping test bed for the haptic evaluation of mechanisms requires. At such a test bed, the path planner could directly be coupled to the haptic rendering of a virtual scene to assist a user in approaching a target.This motivated the development of SamPP, a sampling-based path planning library with implementations of the most important algorithms. It can be used for nearly arbitrary rigid robots and environments. By performing numerous benchmarks, we prove the effectiveness and efficiency of SamPP. It is shown that a single-threaded version of the path planning can be used for real-time support of the haptic interaction at a novel actuated car door.Furthermore, we enhance the path planning performance for unknown or dynamical environments significantly by the OR-Parallelization of different path planning queries. This Generalized OR-Parallelization is a novel concept that to the best knowledge of the authors has not been proposed beforehand. We show that for the case of dynamic environments the likelihood of a fast path planning result is higher with our approach. Thus, even in dynamic or unknown environments, a real-time support of haptic interaction can be achieved. Finally, we highlight four promising research directions to exploit the concept of Generalized OR-Parallelization: 1) Combination of PRMs and RRTs to achieve a synergy of the advantages of both concepts, 2) concurrent use of different parameter sets of path planning algorithms, 3) online adaptation of these parameter sets and 4) online adaptation of the types and numbers of parallel executed path planning programs

Scaling Robot Motion Planning to Multi-core Processors and the Cloud

Imagine a world in which robots safely interoperate with humans, gracefully and efficiently accomplishing everyday tasks. The robot's motions for these tasks, constrained by the design of the robot and task at hand, must avoid collisions with obstacles. Unfortunately, planning a constrained obstacle-free motion for a robot is computationally complex---often resulting in slow computation of inefficient motions. The methods in this dissertation speed up this motion plan computation with new algorithms and data structures that leverage readily available parallel processing, whether that processing power is on the robot or in the cloud, enabling robots to operate safer, more gracefully, and with improved efficiency. The contributions of this dissertation that enable faster motion planning are novel parallel lock-free algorithms, fast and concurrent nearest neighbor searching data structures, cache-aware operation, and split robot-cloud computation. Parallel lock-free algorithms avoid contention over shared data structures, resulting in empirical speedup proportional to the number of CPU cores working on the problem. Fast nearest neighbor data structures speed up searching in SO(3) and SE(3) metric spaces, which are needed for rigid body motion planning. Concurrent nearest neighbor data structures improve searching performance on metric spaces common to robot motion planning problems, while providing asymptotic wait-free concurrent operation. Cache-aware operation avoids long memory access times, allowing the algorithm to exhibit superlinear speedup. Split robot-cloud computation enables robots with low-power CPUs to react to changing environments by having the robot compute reactive paths in real-time from a set of motion plan options generated in a computationally intensive cloud-based algorithm. We demonstrate the scalability and effectiveness of our contributions in solving motion planning problems both in simulation and on physical robots of varying design and complexity. Problems include finding a solution to a complex motion planning problem, pre-computing motion plans that converge towards the optimal, and reactive interaction with dynamic environments. Robots include 2D holonomic robots, 3D rigid-body robots, a self-driving 1/10 scale car, articulated robot arms with and without mobile bases, and a small humanoid robot.Doctor of Philosoph

The Ariadne's Clew Algorithm

We present a new approach to path planning, called the "Ariadne's clew algorithm". It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments - ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called Search and Explore, applied in an interleaved manner. Explore builds a representation of the accessible space while Search looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing