Parallelizing RRT on distributed-memory architectures

This paper addresses the problem of improving the performance of the Rapidly-exploring Random Tree (RRT) algorithm by parallelizing it. For scalability reasons we do so on a distributed-memory architecture, using the message-passing paradigm. We present three parallel versions of RRT along with the technicalities involved in their implementation. We also evaluate the algorithms and study how they behave on different motion planning problems

Parallelizing RRT on large-scale distributed-memory architectures

This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance

A Motion Planning Processor on Reconfigurable Hardware

Motion planning algorithms enable us to find feasible paths for moving objects. These algorithms utilize feasibility checks to differentiate valid paths from invalid ones. Unfortunately, the computationally expensive nature of such checks reduces the effectiveness of motion planning algorithms. However, by using hardware acceleration to speed up the feasibility checks, we can greatly enhance the performance of the motion planning algorithms. Of course, such acceleration is not limited to feasibility checks; other components of motion planning algorithms can also be accelerated using specially designed hardware. A Field Programmable Gate Array (FPGA) is a great platform to support such an acceleration. An FPGA is a collection of digital gates which can be reprogrammed at run time, i.e., it can be used as a CPU that reconfigures itself for a given task. In this paper, we study the feasibility of an FPGA based motion planning processor and evaluate its performance. In order to leverage its highly parallel nature and its modular structure, our processor utilizes the probabilistic roadmap method at its core. The modularity enables us to replace the feasibility criteria with other ones. The reconfigurability lets us run our processor in different roles, such as a motion planning co-processor, an autonomous motion planning processor or dedicated collision detection chip. Our experiments show that such a processor is not only feasible but also can greatly increase the performance of current algorithms

Efficient Configuration Space Construction and Optimization for Motion Planning

The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this paper, we survey some of our recent work on solving two important challenges related to configuration spaces

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

A Framework For Parallelizing Sampling-Based Motion Planning Algorithms

Motion planning is the problem of finding a valid path for a robot from a start position to a goal position. It has many uses such as protein folding and animation. However, motion planning can be slow and take a long time in difficult environments. Parallelization can be used to speed up this process. This research focused on the implementation of a framework for the implementation and testing of Parallel Motion Planning algorithms. Additionally, two methods were implemented to test this framework. The results showed a reasonable amount of speed-up and coverage and connectivity similar to sequential methods

Asymptotically near-optimal RRT for fast, high-quality, motion planning

We present Lower Bound Tree-RRT (LBT-RRT), a single-query sampling-based algorithm that is asymptotically near-optimal. Namely, the solution extracted from LBT-RRT converges to a solution that is within an approximation factor of 1+epsilon of the optimal solution. Our algorithm allows for a continuous interpolation between the fast RRT algorithm and the asymptotically optimal RRT* and RRG algorithms. When the approximation factor is 1 (i.e., no approximation is allowed), LBT-RRT behaves like RRG. When the approximation factor is unbounded, LBT-RRT behaves like RRT. In between, LBT-RRT is shown to produce paths that have higher quality than RRT would produce and run faster than RRT* would run. This is done by maintaining a tree which is a sub-graph of the RRG roadmap and a second, auxiliary graph, which we call the lower-bound graph. The combination of the two roadmaps, which is faster to maintain than the roadmap maintained by RRT*, efficiently guarantees asymptotic near-optimality. We suggest to use LBT-RRT for high-quality, anytime motion planning. We demonstrate the performance of the algorithm for scenarios ranging from 3 to 12 degrees of freedom and show that even for small approximation factors, the algorithm produces high-quality solutions (comparable to RRG and RRT*) with little running-time overhead when compared to RRT