Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks.
Robots manipulating in domestic environments generally need to interact with articulated objects, such as doors, drawers, laptops and swivel chairs. The rigid bodies that make up these objects are connected by a revolute pair or a prismatic pair. Robots are expected to learn and understand the objects' articulated constraints with a simple interaction method. In this way, the autonomy of robot manipulation will be greatly improved in an environment with unstructured constraints. In this paper, a method is proposed to obtain the articulated objects' constraint model by learning from a one-shot continuous visual demonstration which contains multistep movements, and this enables human teacher to continuously demonstrate several tasks at once without manual segmentation. At the end of this paper, a six-degree-of-freedom robot uses the constraint model obtained by demonstration learning to achieve manipulation planning of various tasks based on the AG-CBiRRT algorithm.
The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples’ rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency.
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