This paper presents a motion planner for systems subject to kinematic and dynamic constraints. The former appear when kinematic loops are present in the system, such as in parallel manipulators, in robots that cooperate to achieve a given task, or in situations involving contacts with the environment. The latter are necessary to obtain realistic trajectories, taking into account the forces acting on the system. The kinematic constraints make the state space become an implicitly-defined manifold, which complicates the application of common motion planning techniques. To address this issue, the planner constructs an atlas of the state space manifold incrementally, and uses this atlas both to generate random states and to dynamically simulate the steering of the system towards such states. The resulting tools are then exploited to construct a rapidly-exploring random tree (RRT) over the state space. To the best of our knowledge, this is the first randomized kinodynamic planner for implicitly-defined state spaces. The test cases presented in this paper validate the approach in significantly-complex systems.
From constrained multibody dynamics it is well known that, when simulating a system's motion, it is advantageous to directly deal with the DAE of the system, rather than converting it into its ODE form @cite_18 . Several techniques have been used to this end @cite_53 @cite_48 . In the popular Baumgarte method the drift is alleviated with control techniques @cite_2 , but the control parameters are problem dependent and there is no general method to tune them. Another way to reduce the drift is to use violation suppression techniques @cite_33 @cite_7 , but they do not guarantee a drift-free integration. A better alternative are the methods relying on local parameterizations @cite_36 , since they cancel the drift to machine accuracy. To the best of our knowledge, this approach has never been applied in the context of kinodynamic planning. However, it nicely complements an existing planning method for implicitly-defined configuration spaces @cite_11 @cite_52 , which also relies on local parameterizations. The planner introduced in this paper can be seen as an extension of the latter method to also deal with dynamics, or an extension of @cite_37 to include kinematic constraints.
@cite_18 @cite_37 @cite_33 @cite_7 @cite_36 @cite_48 @cite_53 @cite_52 @cite_2 @cite_11
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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. By means of the Udwadia-Kalaba approach we propose an explicit equation of constrained motion developed to simulate constrained dynamical systems without error accumulation due to constraint drift. The basic idea is to embed a small virtual force and a small virtual impulse to the equation of motion, in order to avoid the drift typically experienced in constrained multibody simulations. The embedded correction terms are selected to minimally alter the dynamics in an acceleration and kinetic energy norm sense. The formulation allows one to use a standard ODE solver, avoiding the need for iterative constraint stabilization. The equation is based on the pseudoinverse of a constraint matrix such that it can be used under redundant constraints and kinematic singularities. The proposed method takes into account the finite word-length of the computational environment, and also accommodates possibly inconsistent initial conditions. A hallmark of multibody dynamics is that most formulations involve a number of constraints. Typically, when redundant generalized coordinates are used, equations of motion are simpler to derive but constraint equations are present. Approaches to dealing with high index differential algebraic equations, based on index reduction techniques, are reviewed and discussed. Constraint violation stabilization techniques that have been developed to control constraint drift are also reviewed. These techniques are used in conjunction with algorithms that do not exactly enforce the constraints. Control theory forms the basis for a number of these methods. Penalty based techniques have also been developed, but the augmented Lagrangian formulation presents a more solid theoretical foundation. In contrast to constraint violation stabilization techniques, constraint violation elimination techniques enforce exact satisfaction of the constraints, at least to machine accuracy. Finally, as the finite element method has gained popularity for the solution of multibody systems, new techniques for the enforcement of constraints have been developed in that framework. The goal of this paper is to review the features of these methods, assess their accuracy and efficiency, underline the relationship among the methods, and recommend approaches that seem to perform better than others. We consider motion planning problems for a vehicle with kinodynamic constraints, where there is partial knowledge about the environment and replanning is required. We present a new tree-based planner that explicitly deals with kinodynamic constraints and addresses the safety issues when planning under finite computation times, meaning that the vehicle avoids collisions in its evolving configuration space. In order to achieve good performance we incrementally update a tree data-structure by retaining information from previous steps and we bias the search of the planner with a greedy, yet probabilistically complete state space exploration strategy. Moreover, the number of collision checks required to guarantee safety is kept to a minimum. We compare our technique with alternative approaches as a standalone planner and show that it achieves favorable performance when planning with dynamics. We have applied the planner to solve a challenging replanning problem involving the mapping of an unknown workspace with a nonholonomic platform