Enhancing the Transition-based RRT to deal with complex cost spaces (original) (raw)
Related papers
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014
The Transition-based RRT (T-RRT) is a variant of RRT developed for path planning on a continuous cost space, i.e. a configuration space featuring a continuous cost function. It has been used to solve complex, high-dimensional problems in robotics and structural biology. In this paper, we propose a multiple-tree variant of T-RRT, named Multi-T-RRT. It is especially useful to solve ordering-and-pathfinding problems, i.e. to compute a path going through several unordered waypoints. Using the Multi-T-RRT, such problems can be solved from a purely geometrical perspective, without having to use a symbolic task planner. We evaluate the Multi-T-RRT on several path planning problems and compare it to other path planners. Finally, we apply the Multi-T-RRT to a concrete industrial inspection problem involving an aerial robot.
Transition-based RRT for path planning in continuous cost spaces
Intelligent Robots and Systems, …, 2008
This paper presents a new method called Transition-based RRT (T-RRT) for path planning problems in continuous cost spaces. It combines the exploration strength of the RRT algorithm that rapidly grow random trees toward unexplored regions of the space, with the efficiency of stochastic optimization methods that use transition tests to accept or to reject a new potential state. This planner also relies on the notion of minimal work path that gives a quantitative way to compare path costs. The method also integrates self tuning of a parameter controlling its exploratory behavior. It yields to solution paths that efficiently follow low cost valleys and the saddle points of the cost space. Simulation results show that the method can be applied to a large set of applications including terrain costmap motions or planning low cost motions for free flying or articulated robots.
RRT+ : Fast Planning for High-Dimensional Configuration Spaces
2016
In this paper we propose a new family of RRT based algorithms, named RRT+ , that are able to find faster solutions in high-dimensional configuration spaces compared to other existing RRT variants by finding paths in lower dimensional subspaces of the configuration space. The method can be easily applied to complex hyper-redundant systems and can be adapted by other RRT based planners. We introduce RRT+ and develop some variants, called PrioritizedRRT+ , PrioritizedRRT+-Connect, and PrioritizedBidirectionalT-RRT+ , that use the new sampling technique and we show that our method provides faster results than the corresponding original algorithms. Experiments using the state-of-the-art planners available in OMPL show superior performance of RRT+ for high-dimensional motion planning problems.
Sampling-Based Path Planning on Configuration-Space Costmaps
IEEE Transactions on Robotics, 2000
This paper addresses path planning considering a cost function defined over the configuration space. The proposed Transition-based RRT planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of RRTs with transition tests used in stochastic optimization methods to accept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed.
Addressing cost-space chasms in manipulation planning
2011 IEEE International Conference on Robotics and Automation, 2011
Finding paths in high-dimensional spaces becomes difficult when we wish to optimize the cost of a path in addition to obeying feasibility constraints. Recently the T-RRT algorithm was presented as a method to plan in high-dimensional costspaces and it was shown to perform well across a variety of problems. However, since the T-RRT relies solely on sampling to explore the space, it has difficulty navigating cost-space chasmsnarrow low-cost regions surrounded by increasing cost. Such chasms are particularly common in planning for manipulators because many useful cost functions induce narrow or lowerdimensional low-cost areas. This paper presents the GradienT-RRT algorithm, which combines the T-RRT with a local gradient method to bias the search toward lower-cost regions. GradienT-RRT is effective at navigating chasms because it explores low-cost regions that are too narrow to explore by sampling alone. We compare the performance of T-RRT and GradienT-RRT on planning problems involving cost functions defined in workspace, task space, and C-space. We find that GradienT-RRT outperforms T-RRT in terms of the cost of the final path while maintaining better or comparable computation time. We also find that the cost of paths generated by GradienT-RRT is far less sensitive to changes in a key parameter, making it easier to tune the algorithm. Finally, we conclude with a demonstration of GradienT-RRT on a planning-withuncertainty task on the physical HERB robot.
RRT-HX: RRT With Heuristic Extend Operations for Motion Planning in Robotic Systems
Volume 5A: 40th Mechanisms and Robotics Conference, 2016
This paper presents a sampling-based method for path planning in robotic systems without known cost-to-go information. It uses trajectories generated from random search to heuristically learn the cost-to-go of regions within the configuration space. Gradually, the search is increasingly directed towards lower cost regions of the configuration space, thereby producing paths that converge towards the optimal path. The proposed framework builds on Rapidly-exploring Random Trees for random sampling-based search and Reinforcement Learning is used as the learning method. A series of experiments were performed to evaluate and demonstrate the performance of the proposed method.
An adaptive roadmap guided Multi-RRTs strategy for single query path planning
2010 IEEE International Conference on Robotics and Automation, 2010
During the past decade, Rapidly-exploring Random Tree (RRT) and its variants are shown to be powerful sampling based single query path planning approaches for robots in high-dimensional configuration space. However, the performance of such tree-based planners that rely on uniform sampling strategy degrades significantly when narrow passages are contained in the configuration space. Given the assumption that computation resources should be allocated in proportion to the geometric complexity of local region, we present a novel single-query Multi-RRTs path planning framework that employs an improved Bridge Test algorithm to identify global important roadmaps in narrow passages. Multiple trees can be grown from these sampled roadmaps to explore sub-regions which are difficult to reach. The probability of selecting one particular tree for expansion and connection, which can be dynamically updated by on-line learning algorithm based on the historic results of exploration, guides the tree through narrow passage rapidly. Experimental results show that the proposed approach gives substantial improvement in planning efficiency over a wide range of single-query path planning problems. I. INTRODUCTION OBOT path planning has been one of the fundamental problems over the last couple of decades in such areas as robotics, artificial intelligence, as well as computer graphics. The original description of the problem is to plan a collision-free path for a robot made of an arbitrary number of polyhedral bodies among an arbitrary number of polyhedral obstacles between two collision-free queried positions of the robot, which has been shown to be PSPACE-complete by complex geometric analysis [1]. The well known complete motion planning algorithms, such as cell decomposition and visibility roadmaps, require explicit representation of robot configuration space. They are usually computationally intractable and hard to implement for practical applications [2]. "The curse of dimensionality" has lead to the development of randomized sampling-based motion planners, which can solve many previously considered hard problems successfully and quickly. PRM [3] and RRT [4] are two typical randomized Manuscript received
RRT-connect: An efficient approach to single-query path planning
2000
A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two Rapidly-exploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.
Aalborg Universitet Minimising Computational Complexity of the RRT Algorithm
2011
Sampling based techniques for robot motion planning have become more widespread during the last decade. The algorithms however, still struggle with for example narrow passages in the configuration space and suffer from high number of necessary samples, especially in higher dimensions. A widely used method is Rapidly-exploring Random Trees (RRT’s). One problem with this method is the nearest neighbour search time, which grows significantly when adding a large number of vertices. We propose an algorithm which decreases the computation time, such that more vertices can be added in the same amount of time to generate better trajectories. The algorithm is based on subdividing the configuration space into boxes, where only specific boxes needs to be searched to find the nearest neighbour. It is shown that the computational complexity is lowered from a theoretical point of view. The result is an algorithm that can provide better trajectories within a given time period, or alternatively com...
ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through Space-Time
2022 International Conference on Robotics and Automation (ICRA)
We present a motion planner for planning through space-time with dynamic obstacles, velocity constraints, and unknown arrival time. Our algorithm, Space-Time RRT* (ST-RRT*), is a probabilistically complete, bidirectional motion planning algorithm, which is asymptotically optimal with respect to the shortest arrival time. We experimentally evaluate ST-RRT* in both abstract (2D disk, 8D disk in cluttered spaces, and on a narrow passage problem), and simulated robotic path planning problems (sequential planning of 8DoF mobile robots, and 7DoF robotic arms). The proposed planner outperforms RRT-Connect and RRT* on both initial solution time, and attained final solution cost. The code for ST-RRT* is available in the Open Motion Planning Library (OMPL).