Optimal whole body motion planning of humanoid with articulated spine for object manipulation in double support phase
TL;DR: The proposed approach deals with the stability constraints by rejective sampling approach which divides random configurations generated into valid and invalid configurations, and the loop-closure constraints are tackled by separating the humanoid into two open-kinematic sub-chains, and then generating random configurations in one sub- chain whereas the remaining sub-chain uses inverse kinematics for closure.
Abstract: In this paper we consider the motion planning problem of humanoid with articulated-spine for object manipulation in double support phase. Complexity of motion planning increases due to higher degrees-of-freedom (DOF), redundancy arising due to articulated spine, inherent underactuation and stability constraint. Additionally, loop-closure constraints arise during double-support phase, and self-collision constraints exist independent of the task to be performed. These problems make motion planning of humanoids a challenging task. In this work, we address the above issues by proposing a sampling based approach for planning the motion of the humanoid. Our approach to the problem is based on RRT* which can generate asymptotically optimal paths. The proposed approach deals with the stability constraints by rejective sampling approach which divides random configurations generated into valid and invalid configurations. Additionally, the loop-closure constraints are tackled by separating the humanoid into two open-kinematic sub-chains, and then generating random configurations in one sub-chain whereas the remaining sub-chain uses inverse kinematics for closure. Efficacy of the proposed approach is demonstrated for whole-body motion planning of a 25-DOF humanoid in generating asymptotically optimal end-effector paths.
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TL;DR: An asymptotically optimal sampling based approach for generating motion plans is presented and a novel constraint solver extension to the bidirectional Fast Marching Trees algorithm in the form of a way-point generator is proposed such that it can be applied for whole-body motion planning of humanoids.
5 citations
TL;DR: This work treats motion planning as a graph search problem, and employs Shared Multi-heuristic A* (SMHA*) to generate efficient, stable and collision-free motion plans given only the starting state of the robot and the desired end-effector pose.
2 citations
01 Nov 2018
TL;DR: In this paper, a Center of Mass (CoM) based manipulation and regrasp planner is proposed to preserve the robot balance and ensure that assembly motions are stable and prevent the robot from falling while performing dexterous tasks.
Abstract: This paper presents a Center of Mass (CoM) based manipulation and regrasp planner that implements stability constraints to preserve the robot balance. The planner provides a graph of IK-feasible, collision-free and stable motion sequences, constructed using an energy based motion planning algorithm. It assures that the assembly motions are stable and prevent the robot from falling while performing dexterous tasks in different situations. Furthermore, the constraints are also used to perform an RRT-inspired task-related stability estimation in several simulations. The estimation can be used to select between single-arm and dual-arm regrasping configurations to achieve more stability and robustness for a given manipulation task. To validate the planner and the task-related stability estimations, several tests are performed in simulations and real-world experiments involving the HRP5P humanoid robot, the 5th generation of the HRP robot family. The experiment results suggest that the planner and the task-related stability estimation provide robust behavior for the humanoid robot while performing regrasp tasks.
1 citations
References
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TL;DR: In this paper, the authors studied the asymptotic behavior of the cost of the solution returned by stochastic sampling-based path planning algorithms as the number of samples increases.
Abstract: 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 number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g. showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.
3,438 citations
24 Apr 2000
TL;DR: A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces by incrementally building two rapidly-exploring random trees rooted at the start and the goal configurations.
Abstract: 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.
3,102 citations
Book•
01 Jan 2006TL;DR: In this paper, the Jacobian is used to describe the relationship between rigid motions and homogeneous transformations, and a linear algebraic approach is proposed for vision-based control of dynamical systems.
Abstract: Preface. 1. Introduction. 2. Rigid Motions and Homogeneous Transformations. 3. Forward and Inverse Kinematics. 4. Velocity Kinematics-The Jacobian. 5. Path and Trajectory Planning. 6. Independent Joint Control. 7. Dynamics. 8. Multivariable Control. 9. Force Control. 10. Geometric Nonlinear Control. 11. Computer Vision. 12. Vision-Based Control. Appendix A: Trigonometry. Appendix B: Linear Algebra. Appendix C: Dynamical Systems. Appendix D: Lyapunov Stability. Index.
3,100 citations
"Optimal whole body motion planning ..." refers background in this paper
...Note that such inverse and forward kinematics mapping are well studied in robotics literature [17], and hence, not reported here for brevity....
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Posted Content•
TL;DR: The main contribution of the paper is the introduction of new algorithms, namely, PRM and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum.
Abstract: 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 number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.
2,210 citations
"Optimal whole body motion planning ..." refers background or methods in this paper
...RRT* [8] algorithm is another variant of RRT that is proven to lead to asymptotically optimal paths as number of iterations increases....
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...In this section the generic RRT* algorithm as proposed in [8] is presented in our notation for better understanding of the proposed implementation, then it is extended for motion planning of humanoid....
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...As the number of iterations are increased, it can be seen that the average path cost is reduced, which is the expected result as RRT* is proven to be asymptotically optimal in [8]....
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...The use of RRT* for whole-body motion planning of a humanoid robot with articulated spine in double support phase is not a trivial extension of [8], and poses some obvious challenges as mentioned earlier....
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10 May 1999
TL;DR: A state-space perspective on the kinodynamic planning problem is presented, and a randomized path planning technique that computes collision-free kinodynamic trajectories for high degree-of-freedom problems is introduced.
Abstract: The paper presents a state-space perspective on the kinodynamic planning problem, and introduces a randomized path planning technique that computes collision-free kinodynamic trajectories for high degree-of-freedom problems. By using a state space formulation, the kinodynamic planning problem is treated as a 2n-dimensional nonholonomic planning problem, derived from an n-dimensional configuration space. The state space serves the same role as the configuration space for basic path planning. The bases for the approach is the construction of a tree that attempts to rapidly and uniformly explore the state space, offering benefits that are similar to those obtained by successful randomized planning methods, but applies to a much broader class of problems. Some preliminary results are discussed for an implementation that determines the kinodynamic trajectories for hovercrafts and satellites in cluttered environments resulting in state spaces of up to twelve dimensions.
1,414 citations