Results 1  10
of
43
Resolution complete rapidlyexploring random trees
 In Proc. IEEE Int’l Conf. on Robotics and Automation
, 2002
"... ..."
(Show Context)
Adaptive RRTs for validating hybrid robotic control systems
 in Algorithmic Foundations of Robotics VI
, 2005
"... Abstract. Most robot control and planning algorithms are complex, involving a combination of reactive controllers, behaviorbased controllers, and deliberative controllers. The switching between different behaviors or controllers makes such systems hybrid, i.e. combining discrete and continuous dyna ..."
Abstract

Cited by 43 (3 self)
 Add to MetaCart
(Show Context)
Abstract. Most robot control and planning algorithms are complex, involving a combination of reactive controllers, behaviorbased controllers, and deliberative controllers. The switching between different behaviors or controllers makes such systems hybrid, i.e. combining discrete and continuous dynamics. While proofs of convergence, robustness and stability are often available for simple controllers under a carefully crafted set of operating conditions, there is no systematic approach to experimenting with, testing, and validating the performance of complex hybrid control systems. In this paper we address the problem of generating sets of conditions (inputs, disturbances, and parameters) that might be used to ”test ” a given hybrid system. We use the method of Rapidly exploring Random Trees (RRTs) to obtain test inputs. We extend the traditional RRT, which only searches over continuous inputs, to a new algorithm, called the Rapidly exploring Random Forest of Trees (RRFT), which can also search over time invariant parameters by growing a set of trees for each parameter value choice. We introduce new measures for coverage and tree growth that allows us to dynamically allocate our resources among the set of trees and to plant new trees when the growth rate of existing ones slows to an unacceptable level. We demonstrate the application of RRFT to testing and validation of aerial robotic control systems. 1
SamplingBased Motion Planning with Differential Constraints

, 2005
"... Since differential constraints which restrict admissible velocities and accelerations of robotic systems are ignored in path planning, solutions for kinodynamic and nonholonomic planning problems from classical methods could be either inexecutable or inefficient. Motion planning with differential c ..."
Abstract

Cited by 29 (4 self)
 Add to MetaCart
(Show Context)
Since differential constraints which restrict admissible velocities and accelerations of robotic systems are ignored in path planning, solutions for kinodynamic and nonholonomic planning problems from classical methods could be either inexecutable or inefficient. Motion planning with differential constraints (MPD), which directly considers differential constraints, provides a promising direction to calculate reliable and efficient solutions. A large amount of recent efforts have been devoted to various samplingbased MPD algorithms, which iteratively build search graphs using sampled states and controls. This thesis addresses several issues in analysis and design of these algorithms. Firstly, resolution completeness of path planning is extended to MPD and the first quantitative conditions are provided. The analysis is based on the relationship between the reachability graph, which is an intrinsic graph representation of a given problem, and the search graph, which is built by the algorithm. Because of sampling and other complications, there exist mismatches between these two graphs. If a solution exists in the reachability graph, resolution complete algorithms must construct a solution path encoding the solution or its approximation in the search graph
An rrtbased algorithm for testing and validating multirobot controllers
 In Robotics: Science and Systems
, 2005
"... Abstract — We address the problem of testing complex reactive control systems and validating the effectiveness of multiagent controllers. Testing and validation involve searching for conditions that lead to system failure by exploring all adversarial inputs and disturbances for errant trajectories. ..."
Abstract

Cited by 28 (1 self)
 Add to MetaCart
(Show Context)
Abstract — We address the problem of testing complex reactive control systems and validating the effectiveness of multiagent controllers. Testing and validation involve searching for conditions that lead to system failure by exploring all adversarial inputs and disturbances for errant trajectories. This problem of testing is related to motion planning, with one main difference. Unlike motion planning problems, systems are typically not controllable with respect to disturbances or adversarial inputs and therefore, the reachable set of states is a small subset of the entire state space. In both cases however, there is a goal or specification set consisting of a set of points in state space that is of interest, either for demonstrating failure or for validation. In this paper we consider the application of the Rapidlyexploring Random Tree algorithm to the testing and validation problem. Because of the differences between testing and motion planning, we propose three modifications to the original RRT algorithm. First, we introduce a new distance function which incorporates information about the system’s dynamics to select nodes for extension. Second, we introduce a weighting to penalize nodes which are repeatedly selected but fail to extend. Third, we propose a scheme for adaptively modifying the sampling probability distribution based on tree growth. We demonstrate the application of the algorithm via three simple and one large scale example and provide computational statistics. Our algorithms are applicable beyond the testing problem to motion planning for systems that are not small time locally controllable. I.
From dynamic programming to RRTs: Algorithmic design of feasible trajectories
 Control Problems in Robotics
, 2002
"... Abstract. This paper summarizes our recent development of algorithms that construct feasible trajectories for problems that involve both differential constraints (typically in the form of an underactuated nonlinear system), and global constraints (typically arising from robot collisions). Dynamic pr ..."
Abstract

Cited by 21 (0 self)
 Add to MetaCart
(Show Context)
Abstract. This paper summarizes our recent development of algorithms that construct feasible trajectories for problems that involve both differential constraints (typically in the form of an underactuated nonlinear system), and global constraints (typically arising from robot collisions). Dynamic programming approaches are described that produce approximatelyoptimal solutions for lowdimensional problems. Rapidlyexploring Random Tree (RRT) approaches are described that can find feasible, nonoptimal solutions for higherdimensional problems. Several key issues for future research are discussed. 1
Design and verification of controllers for airships
 In IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV
, 2003
"... endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution m ..."
Abstract

Cited by 18 (7 self)
 Add to MetaCart
(Show Context)
endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubspermissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
LQRRRT ∗ : Optimal SamplingBased Motion Planning with Automatically Derived Extension Heuristics
"... Abstract — The RRT ∗ algorithm has recently been proposed as an optimal extension to the standard RRT algorithm [1]. However, like RRT, RRT ∗ is difficult to apply in problems with complicated or underactuated dynamics because it requires the design of a two domainspecific extension heuristics: a d ..."
Abstract

Cited by 15 (1 self)
 Add to MetaCart
(Show Context)
Abstract — The RRT ∗ algorithm has recently been proposed as an optimal extension to the standard RRT algorithm [1]. However, like RRT, RRT ∗ is difficult to apply in problems with complicated or underactuated dynamics because it requires the design of a two domainspecific extension heuristics: a distance metric and node extension method. We propose automatically deriving these two heuristics for RRT ∗ by locally linearizing the domain dynamics and applying linear quadratic regulation (LQR). The resulting algorithm, LQRRRT ∗ , finds optimal plans in domains with complex or underactuated dynamics without requiring domainspecific design choices. We demonstrate its application in domains that are successively torquelimited, underactuated, and in belief space. I.
A Quadratic RegulatorBased Heuristic for Rapidly Exploring State Space
, 2010
"... Kinodynamic planning algorithms like RapidlyExploring Randomized Trees (RRTs) hold the promise of finding feasible trajectories for rich dynamical systems with complex, nonconvex constraints. In practice, these algorithms perform very well on configuration space planning, but struggle to grow effi ..."
Abstract

Cited by 14 (2 self)
 Add to MetaCart
Kinodynamic planning algorithms like RapidlyExploring Randomized Trees (RRTs) hold the promise of finding feasible trajectories for rich dynamical systems with complex, nonconvex constraints. In practice, these algorithms perform very well on configuration space planning, but struggle to grow efficiently in systems with dynamics or differential constraints. This is due in part to the fact that the conventional proximity metric, Euclidean distance, does not take into account system dynamics and constraints when identifying which node in the existing tree is capable of producing children closest to a given point in state space. Here we argue that the RRTs ’ coverage of state space is maximized by using a proximity psuedometric proportional to the length, in time, of the quickest possible trajectory
A SamplingBased Tree Planner for Systems with Complex Dynamics
, 2012
"... This paper presents a kinodynamic motion planner, ..."
Abstract

Cited by 14 (10 self)
 Add to MetaCart
This paper presents a kinodynamic motion planner,
Disassembly Path Planning for Complex Articulated Objects
"... Samplingbased path planning algorithms are powerful tools for computing constrained disassembly motions. This paper presents a variant of the RRT algorithm particularly devised for the disassembly of objects with articulated parts. Configuration parameters generally play two different roles in this ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
(Show Context)
Samplingbased path planning algorithms are powerful tools for computing constrained disassembly motions. This paper presents a variant of the RRT algorithm particularly devised for the disassembly of objects with articulated parts. Configuration parameters generally play two different roles in this type of problems: some of them are essential for the disassembly task, while others only need to move if they hinder the progress of the disassembly process. The proposed method is based on such a partition of the configuration parameters. Results show a remarkable performance improvement compared to standard path planning techniques. The paper also shows practical applications of the presented algorithm in robotics and structural bioinformatics. Keywords: