RESAMPL: A Region-Sensitive Adaptive Motion Planner

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.

International Journal of Interactive Multimedia and Artificial Intelligence, 2022

Sampling-based motion planning in the field of robot motion planning has provided an effective approach to finding path for even high dimensional configuration space and with the motivation from the concepts of sampling based-motion planners, this paper presents a new sampling-based planning strategy called Optimistic Motion Planning using Recursive Sub-Sampling (OMPRSS), for finding a path from a source to a destination sanguinely without having to construct a roadmap or a tree. The random sample points are generated recursively and connected by straight lines. Generating sample points is limited to a range and edge connectivity is prioritized based on their distances from the line connecting through the parent samples with the intention to shorten the path. The planner is analysed and compared with some sampling strategies of probabilistic roadmap method (PRM) and the experimental results show agile planning with early convergence.

2011

Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance.

2011

Computing globally optimal motion plans requires exploring the configuration space to identify reachable free space regions as well as refining understanding of already explored regions to find better paths. We present the rapidly-exploring roadmap (RRM), a new method for single-query optimal motion planning that allows the user to explicitly consider the trade-off between exploration and refinement. RRM initially explores the configuration space like a rapidly exploring random tree (RRT). Once a path is found, RRM uses a user-specified parameter to weigh whether to explore further or to refine the explored space by adding edges to the current roadmap to find higher quality paths in the explored space. Unlike prior methods, RRM does not focus solely on exploration or refine prematurely. We demonstrate the performance of RRM and the trade-off between exploration and refinement using two examples, a point robot moving in a plane and a concentric tube robot capable of following curved trajectories inside patient anatomy for minimally invasive medical procedures.

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.

We evaluatethe use ofdynamicroadmapsfor on-line motion planning in changing environments. When changes aredetectedintheworkspace,thevaliditystateofaffectededges and nodes of a precomputed roadmap are updated accordingly. Weconcentrateinthispaperonanalyzingthetradeoffsbetween maintaining dynamic roadmaps and applying an on-line bi-directional Rapidly-exploring Random Tree (RRT) planner alone, which requires no preprocessing or maintenance. We ground the analysis in several benchmarks in virtual environmentswithrandomlymovingobstacles.Differentrobotics structures are used, including a 17 degrees of freedom model of NASA's Robonaut humanoid. Our results show that dynamic roadmaps can be both faster and more capable for planning difficultmotionsthanusingon-lineplanningalone.Inparticular, we investigate its scalability to 3D workspaces and higher dimensional configurations spaces, as our main interest is the application of the method to interactive domains involving humanoids.

A key trait of an autonomous robot is the ability to plan its own motion in order to accomplish specified tasks. Often, the objective of motion planning is to change the state of the world by computing a sequence of admissible motions for the robot. For example, in the path planning problem, we compute a collision-free path for a robot to go from an initial position to a goal position among static obstacles. This is the simplest type of motion planning problems; yet it is 1 provably hard computationally . Sometimes, instead of changing the state of the world, our objective is to maintain a set of constraints on the state of the world (e.g., following a target and keeping it in view), or to achieve a certain state of knowledge about the world (e.g., exploring and mapping an unknown environment).

IEEE Transactions on Systems, Man, and Cybernetics, 2011

In this paper, generalized versions of the probabilistic sampling based planners, Probabilistic Road Maps (PRM) and Rapidly exploring Random Tree (RRT), are presented. The generalized planners, Generalized Probabilistic Road Map (GPRM) and the Generalized Rapidly Exploring Random Tree (GRRT), result in hybrid hierarchical feedback planners that are robust to the uncertainties in the robot motion model as well as uncertainties in the robot map/ workspace. The proposed planners are analyzed and shown to be probabilistically complete. The algorithms are tested on fully actuated, as well as underactuated robots, on several maps of varying degrees of difficulty, and the results show that the generalized methods have a significant advantage over the traditional methods when planning under uncertainty.

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.

2008

Sampling-based motion approaches, like Probabilistic Roadmap Methods or those based on Rapidlyexploring Random Trees are giving good results in robot motion planning problems with many degrees of freedom. Following these approaches, several strategies have been proposed for biasing the sampling towards the most promising regions, thus improving the efficiency and allowing to cope with difficult motion planning problems. The success of these planners in solving challenging problems can be explained by the fact that no explicit representation of the free configuration space is required. This paper reviews some of the most influential proposals and ideas, providing indications on their practical and theoretical implications. The contributions made by Mexican researchers in this field are also presented.