For planning rearrangements of objects in a clutter, it is required to know the goal configuration of the objects. However, in real life scenarios, this information is not available most of the time. We introduce a novel method that computes a collision-free placement of objects on a cluttered surface, while minimizing the total number and amount of displacements of the existing moveable objects. Our method applies nested local searches that perform multi-objective optimizations guided by heuristics. Experimental evaluations demonstrate high computational efficiency and success rate of our method, as well as good quality of solutions.
@cite_7 propose an algorithm that searches for a suitable placement for a single object on a cluttered surface by discretizing the possible orientations of the object, convolving object pixels with the ones on the table, and identifying candidate regions for the object placement that result in minimal penetration with other objects. A placement is then produced by sampling these regions; however, this placement may not be collision-free. Then, they plan for a sequence of linear push actions to rearrange the clutter and clear space for the new object such that this placement becomes collision-free. Note that there are several limitations in this approach; multiple new objects are not considered, the surface and possible object orientations are discretized, and the final configuration is not necessarily collision-free.
@cite_7
2087191078 2109447584 2146161009 2033998449
We introduce a novel computational method for geometric rearrangement of multiple movable objects on a cluttered surface, where objects can change locations more than once by pick and or push actions. This method consists of four stages: (i) finding tentative collision-free final configurations for all objects (all the new objects together with all other objects in the clutter) while also trying to minimize the number of object relocations, (ii) gridization of the continuous plane for a discrete placement of the initial configurations and the tentative final configurations of objects on the cluttered surface, (iii) finding a sequence of feasible pick and push actions to achieve the final discrete placement for the objects in the clutter from their initial discrete place, while simultaneously minimizing the number of object relocations, and (iv) finding feasible final configurations for all objects according to the optimal task plan calculated in stage (iii). For (i) and (iv), we introduce algorithms that utilize local search with random restarts; for (ii), we introduce a mathematical modeling of the discretization problem and use the state-of-the-art ASP reasoners to solve it; for (iii) we introduce a formal hybrid reasoning framework that allows embedding of geometric reasoning in task planning, and use the expressive formalisms and reasoners of ASP. We illustrate the usefulness of our integrated AI approach with several scenarios that cannot be solved by the existing approaches. We also provide a dynamic simulation for one of the scenarios, as supplementary material. We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation. We present an approach to recognition of complex objects in cluttered 3-D scenes that does not require feature extraction or segmentation. Our object representation comprises descriptive images associated with each oriented point on the surface of an object. Using a single point basis constructed from an oriented point, the position of other points on the surface of the object can be described by two parameters. The accumulation of these parameters for many points on the surface of the object results in an image at each oriented point. These images, localized descriptions of the global shape of the object, are invariant to rigid transformations. Through correlation of images, point correspondences between a model and scene data are established and then grouped using geometric consistency. The effectiveness of our algorithm is demonstrated with results showing recognition of complex objects in cluttered scenes with occlusion. We present a real-time algorithm that segments unstructured and highly cluttered scenes. The algorithm robustly separates objects of unknown shape in congested scenes of stacked and occluded objects. The model-free approach finds smooth surface patches, using a depth image from a Kinect camera, which are subsequently combined to form highly probable object hypotheses. Coplanarity and curvature matching is used to recombine surfaces separated by occlusion. The real-time capabilities are proven and the quality of the algorithm is evaluated on a benchmark database. Advantages compared to existing approaches as well as weaknesses are discussed.