Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This paper takes a different approach and proposes to address this problem by using a randomized physics-based motion planner that permits robot-object and object-object interactions. The main idea is to avoid an explicit high-level reasoning of the task by providing the motion planner with a physics engine to evaluate possible complex multi-body dynamical interactions. The approach is able to solve the problem in complex scenarios, also considering uncertainty in the objects pose and in the contact dynamics. The work enhances the state validity checker, the control sampler and the tree exploration strategy of a kinodynamic motion planner called KPIECE. The enhanced algorithm, called p-KPIECE, has been validated in simulation and with real experiments. The results have been compared with an ontological physics-based motion planner and with task and motion planning approaches, resulting in a significant improvement in terms of planning time, success rate and quality of the solution path.
Physics-based motion planning is a step further towards physical realism in the planning process, and therefore useful in manipulation tasks. It extends kinodynamic motion planners (i.e., those planners that consider kinematic and dynamics constraints) by incorporating the physics-based constraints, and by allowing the robot-object and object-object interactions during planning. These interactions are modeled based on rigid body dynamics. Typically, sampling-based kindoynamic motion planners (particularly tree-based planners) are used to sample the states and to construct the solution path. The propagation step is performed using a physics engine such as ODE. Within this framework, approaches are proposed for problems like motion planning among collisionable obstacles @cite_4 , rearrangement planning @cite_13 @cite_7 , object placement @cite_14 , object sorting @cite_23 or bin picking @cite_24 . This paper will tackle the clutter grasping problem using a physics-based motion planner able to cope with uncertainty in the initial state of the system and in the poses of the objects as a result of robot-object or object-object interactions.
@cite_14 @cite_4 @cite_7 @cite_24 @cite_23 @cite_13
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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. Physics-based motion planning is a challenging task, since it requires the computation of the robot motions while allowing possible interactions with (some of) the obstacles in the environment. Kinodynamic motion planners equipped with a dynamic engine acting as state propagator are usually used for that purpose. The difficulties arise in the setting of the adequate forces for the interactions and because these interactions may change the pose of the manipulatable obstacles, thus either facilitating or preventing the finding of a solution path. The use of knowledge can alleviate the stated difficulties. This paper proposes the use of an enhanced state propagator composed of a dynamic engine and a low-level geometric reasoning process that is used to determine how to interact with the objects, i.e. from where and with which forces. The proposal, called κ-PMP can be used with any kinodynamic planner, thus giving rise to e.g. κ-RRT. The approach also includes a preprocessing step that infers from a semantic abstract knowledge described in terms of an ontology the manipulation knowledge required by the reasoning process. The proposed approach has been validated with several examples involving an holonomic mobile robot, a robot with differential constraints and a serial manipulator, and benchmarked using several state-of-the art kinodynamic planners. The results showed a significant difference in the power consumption with respect to simple physics-based planning, an improvement in the success rate and in the quality of the solution paths. This paper addresses the problem of rearrangement planning, i.e. to find a feasible trajectory for a robot that must interact with multiple objects in order to achieve a goal. We propose a planner to solve the rearrangement planning problem by considering two different types of actions: robot-centric and object-centric. Object-centric actions guide the planner to perform specific actions on specific objects. Robot-centric actions move the robot without object relevant intent, easily allowing simultaneous object contact and whole arm interaction. We formulate a hybrid planner that uses both action types. We evaluate the planner on tasks for a mobile robot and a household manipulator. This paper explores the idea of manipulation-aided perception and grasping in the context of sorting small objects on a cluttered tabletop. We present a robust pipeline that combines perception and manipulation to accurately sort objects by some property (e.g., color, size, shape, etc.). The pipeline uses two motion primitives to manipulate the scene in ways that help the robot to improve its perception and grasps. This results in the ability to sort cluttered object piles accurately. We also present an implementation on the PR2 robot that applies our algorithm to sort Duplo bricks by color and size, and compare our method to brick sorting without the aid of manipulation. The experimental results demonstrate the benefits of our approach, particularly in environments with a high degree of clutter. In this work we present a fast kinodynamic RRT-planner that uses dynamic nonprehensile actions to rearrange cluttered environments. In contrast to many previous works, the presented planner is not restricted to quasi-static interactions and monotonicity. Instead the results of dynamic robot actions are predicted using a black box physics model. Given a general set of primitive actions and a physics model, the planner randomly explores the configuration space of the environment to find a sequence of actions that transform the environment into some goal configuration.