The probabilistic roadmap (PRM) planner is a popular method for robot motion planning problems with many degrees of freedom. However, it has been shown that the method performs less well in situations where the robot has to pass through a narrow passage in the scene. This is mainly due to the uniformity of the sampling used in the planner; it places many samples in large open regions and too few in tight passages. In this paper, a technique based on a robot independent cell decomposition of the free workspace is proposed to guide the probabilistic sampling, such that the distribution of samples tends more toward the interesting regions in the scene. It is shown that this leads to improved performance on di#cult planning problems in 2D and 3D workspaces.

Automatic motion planning has applications ranging from traditional robotics to computer-aided design to computational biology and chemistry. Randomized planners, such as probabilistic roadmap methods (prms), have been highly successful in solving these high degree of freedom problems. However, the traditional prm framework fails to address several practical issues. One of the most important issues is the difficulty of deciding what size roadmap is required to solve a given problem efficiently. prms do not provide an automated way to determine appropriate roadmap size. In this paper, we propose a new prm-based framework called Incremental Map Generation (img) to address this problem. Our strategy is to break the map generation into independent processes. Each process generates an independent roadmap component. img proceeds by adding independent roadmap components to an existing roadmap until some user defined criteria are satisfied. In addition to addressing the roadmap size problem, this framework supports roadmap reproducibility in that any of the roadmap increments can be reproduced by using the same set of seeds. Finally, these independent processes Automatic motion planning has applications in many areas such as robotics [26], computer animation, computeraided design/virtual prototyping, and computational biology and chemistry. Although many deterministic motion

The last decade, sampling based planners like the Probabilistic Roadmap Method have proved to be successful in solving complex motion planning problems. We give a reachability based analysis for these planners which leads to a better understanding of the success of the approach and enhancements of the techniques suggested. This also enables us to study the effect of using new local planners.

Abstract — In robot motion planning, many algorithms have been proposed that create a roadmap from which a path for a moving object can be extracted. These algorithms generally do not give guarantees on the quality of the roadmap, i.e. they do not promise that a path will always be found in the roadmap if one exists in the world. Furthermore, such roadmaps often become very large which can cause memory problems and high query times. We present a new efficient algorithm that creates small roadmaps for two- and three-dimensional problems. The algorithm ensures that a path is always found (if one exists) at a given resolution. These claims are verified on a broad range of environments. The results also give insight in the structure of covering roadmaps. Keywords—motion planning, small roadmaps, PRM I.

One of the fundamental tasks robots have to perform is planning their motions while avoiding collisions with obstacles in the environment. This is the central topic of the thesis. We restrict ourselves to motion planning for two- and three-dimensional rigid bodies and articulated robots moving in static and known virtual environments. This thesis has been divided into two parts. The first part deals with comparing and analyzing sampling-based motion planning techniques, in particular variants of the Probabilistic Roadmap Method (PRM). The PRM consists of two phases: a construction and a query phase. In the construction phase, a roadmap (graph) is built, approximating the motions that can be made in the environment. First, a free random sample is created. Such a sample describes a particular placement of the moving object (robot) in the workspace. Then, a simple local planner is employed to connect the sample to some neighbors. Samples and connections are added to the graph until the roadmap is dense enough. In the query phase, the start and goal samples are connected to the graph. The path is obtained by a Dijkstra's shortest path algorithm. Many variants of the PRM have been developed over the past decade. Using both time-based as well as reachability-based analysis, we compare some of the most prominent techniques. The results are surprising in the sense that techniques often perform differently than claimed by the designers. In addition, contrary to general belief, the main challenge is not getting the free space covered but getting the nodes connected, especially when the problems get more complicated, e.g. when a narrow passage is present. By using this knowledge, we can tackle the narrow passage problem by incorporating a more powerful local planner, a refined neighbor selection strategy and a hybrid sampling strategy. The analysis also shows why the PRM successfully deals with many motion planning problems. The second part deals with quality aspects of paths and roadmaps. A good path is relatively short, keeps some distance (clearance) from the obstacles, and is smooth. We will provide algorithms that increase path clearance. A big advantage of these algorithms is that high-clearance paths can now be efficiently created without using complex data structures and algorithms. We also elaborate on algorithms that successfully decrease path length. Then, we introduce the Reachability Roadmap Method which creates small roadmaps for two- and three-dimensional problems. Such a small roadmap has many advantages over a roadmap that is created by the PRM. In particular, the method assures low query times, low memory consumption, and the roadmap can be optimized easily. The algorithm also ensures that a path is always found (if one exists) at a given resolution. We unify the techniques to create high-quality roadmaps for interactive virtual environments. That is, we use the Reachability Roadmap Method to create an initial roadmap. We add useful cycles to provide alternative routes and short paths, and we add clearance to the roadmap to obtain high-clearance paths in real-time.

Abstract — In this work, we investigate solutions to the following question: Given two motion planning problems W1 and W2 with the same robot and similar obstacles, can we reuse the computation from W1 to solve W2 more efficiently? While the answer to this question can find many practical applications, all current motion planners ignore the correspondences between similar environments. Our study shows that by carefully storing and reusing the computation we can gain significant efficiency. I.

Abstract – We present a path planner capable of efficient and real-time handling of known and unknown obstacles in highly dynamic workspaces. Known obstacles are acquired offline and stored in a world model, unknown obstacles are acquired online by one or multiple sensors. This is a typical situation for many applications. The method presented here exploits this distinction by building a static roadmap based on known obstacle information. This enables efficient path planning and real-time performance using bounded lazy evaluation thus reducing the number of costly collision test. The dynamics of the workspace are addressed by invalidation/revalidation of roadmap edges based on sensoric input. Several revalidation strategies are evaluated. The proposed path planner is probabilistically complete and utilizes global environment information to assure goal arrival, if the goal is reachable. Our approach is realized using standard PC hardware with computational requirements allowing real-time performance. Experimental results show the validity of our approach. robots Index Terms – motion planning, multisensor systems, I.

Abstract — This paper presents a novel sampling-based path planning method called Cell-based Probabilistic Roadmaps (CPRM). The algorithm has both anytime and replanning characteristics and is superior to classical approaches in that it incrementally builds and reuses the roadmap. Because it maintains a graph structure that is tailored to the queries, CPRM is very fast in answering repeated queries, e.g. in replanning scenarios. Different design parameters allow to control and fine-tune the behavior of the algorithm. I.

Randomized, sampling-based planning has a long history of success, and although the benefits associated with this use of randomization are widely-recognized, its costs are not well-understood. We examine a variety of problem instances solved with the Rapidly-exploring Random Tree algorithm, demonstrating that heavy-tailed runtime distributions are both common and potentially exploitable. We show that runtime mean and variability can be reduced simultaneously by a straightforward strategy such as restarts and that such a strategy can apply broadly across sets of queries. Our experimental results indicate that several-fold improvements can be achieved in the mean and variance for restrictive problem environments.