Motion planning: Recent developments

Springer Tracts in Advanced Robotics, 2005

In this paper, we discuss the field of sampling-based motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, sampling-based methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful motion planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how samplingbased motion planning has developed. We then discuss a variety of important issues for sampling-based motion planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in sampling-based motion planning.

IEEE Access, 2014

Motion planning is a fundamental research area in robotics. Sampling-based methods offer an efficient solution for what is otherwise a rather challenging dilemma of path planning. Consequently, these methods have been extended further away from basic robot planning into further difficult scenarios and diverse applications. A comprehensive survey of the growing body of work in sampling-based planning is given here. Simulations are executed to evaluate some of the proposed planners and highlight some of the implementation details that are often left unspecified. An emphasis is placed on contemporary research directions in this field. We address planners that tackle current issues in robotics. For instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed. The aim of this paper is to survey the state of the art in motion planning and to assess selected planners, examine implementation details and above all shed a light on the current challenges in motion planning and the promising approaches that will potentially overcome those problems.

Applied Mechanics and Materials, 2015

As robotic systems evolve and get more sophisticated, expectations of them to accomplish high-level tasks increase gradually and their motion planning becomes more complex and difficult. The motion planning problem has been studied for more than four decades from different aspects such that presently has a vast literature. This paper investigates different components of the robot motion planning (RMP) problem and presents a new comprehensive taxonomy for a wide range of RMP problems. The taxonomy is based on a survey of the literature on RMP problems and applications in robotics and computer science.

Abstract. Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ineffective when the configuration space has a narrow passage. We present a new technique based on a random walk strategy to generate samples in narrow regions quickly, thus improving efficiency of Probabilistic Roadmap Planners. The algorithm substantially reduces instances of collision checking and thereby decreases computational time. The method is powerful even for cases where the structure of the narrow passage is not known, thus giving significant improvement over other known methods.

2019

Generalized robot autonomy requires robots to plan solutions for complex and varied tasks involving interaction with the physical world. Traditionally, the (continuous) geometric and (discrete) symbolic aspects of the problem are studied independently; motion planners solve geometric problems, whereas task planners solve symbolic problems. Integrated Task and Motion Planning (TAMP) seeks to unify the planning problem using information from each component to ease the solution of the other. Most TAMP approaches focus on information flow between task and motion planners; the majority of these approaches integrate the motion planning layer into the task planning layer as a source of constraints and/or a validator for task plans [1, 2, 5, 8]. Validating task plans with a motion planner may require repeated expensive executions of both planners. Getting constraints from the motion planner is more efficient but is often limited by the manual selection of a restricted subset of motion const...

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

: The induced sampling distribution of an augmented kd-tree after 10 4 , 10 5 , and 10 6 samples are shown in (a), (b), and (c), respectively. Whiteblack represent low-high sampling probability density. The actual obstacle configuration appears in (d), obstacles are red.

Electronics, 2016

Motion planning deals with finding a collision-free trajectory for a robot from the current position to the desired goal. For a high-dimensional space, sampling-based algorithms are widely used. Different sampling algorithms are used in different environments depending on the nature of the scenario and requirements of the problem. Here, we deal with the problems involving narrow corridors, i.e., in order to reach its destination the robot needs to pass through a region with a small free space. Common samplers used in the Probabilistic Roadmap are the uniform-based sampler, the obstacle-based sampler, maximum clearance-based sampler, and the Gaussian-based sampler. The individual samplers have their own advantages and disadvantages; therefore, in this paper, we propose to create a hybrid sampler that uses a combination of sampling techniques for motion planning. First, the contribution of each sampling technique is deterministically varied to create time efficient roadmaps. However, this approach has a limitation: The sampling strategy cannot adapt as per the changing configuration spaces. To overcome this limitation, the sampling strategy is extended by making the contribution of each sampler adaptive, i.e., the sampling ratios are determined on the basis of the nature of the environment. In this paper, we show that the resultant sampling strategy is better than commonly used sampling strategies in the Probabilistic Roadmap approach.

Proceedings of the International Symposium on Combinatorial Search, 2021

Motion planning in continuous space is a fundamental robotics problem that has been approached from many perspectives. Rapidly-exploring Random Trees (RRTs) use sampling to efficiently traverse the continuous and high-dimensional state space. Heuristic graph search methods use lower bounds on solution cost to focus effort on portions of the space that are likely to be traversed by low-cost solutions. In this paper, we bring these two ideas together in a technique called f-biasing: we use estimates of solution cost, computed as in heuristic search, to guide sparse sampling, as in RRTs. Estimates of solution cost are quickly computed using an abstract version of the problem, then an RRT is constructed by biasing the sampling toward areas of the space traversed by low cost solutions under the abstraction. We show that f-biasing maintains all of the desirable theoretical properties of RRT and RRT*, such as completeness and asymptotic convergence to optimality. We also present experimental results showing that f-biasing finds cheaper paths faster than previous techniques. We see this new technique as strengthening the connections between motion planning in robotics and combinatorial search in artificial intelligence.

The International Journal of Robotics Research, 2011

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the