Planning for Manipulation with Adaptive Motion Primitives

International Conference on Robotics and Automation, 2007

This paper presents the ResolveSpatialConstraints (RSC) algorithm for manipulation planning in a domain with movable obstacles. Empirically we show that our algorithm quickly generates plans for simulated articulated robots in a highly nonlinear search space of exponential dimension. RSC is a reverse-time search that samples future robot actions and constrains the space of prior object displacements. To optimize the efficiency

Journal of Experimental & Theoretical Artificial Intelligence, 2016

This paper proposes an interactive search approach, termed INTERACT, which couples sampling-based motion planning with action planning in order to effectively solve the combined task and motion planning problem. INTERACT is geared towards scenarios involving a mobile robot operating in a fully known environment consisting of static and movable objects. INTERACT makes it possible to specify a task in the planning domain definition language (PDDL) and automatically computes a collision-free and dynamically feasible trajectory that enables the robot to accomplish the task. The coupling of sampling-based motion planning with action planning is made possible by expanding a tree of feasible motions and partitioning it into equivalence classes based on the task predicates. Action plans provide guidance as to which a equivalence class should be further expanded. Information gathered during the motion tree expansion is used to adjust the action costs in order to effectively guide the expansion towards the goal. This interactive process of selecting an equivalence class, expanding the motion tree to implement its action plan and updating the action costs and plans to reflect the progress made is repeated until a solution is found. Experimental validation is provided in simulation using a robotic vehicle to accomplish sophisticated pick-and-place tasks. Comparisons to previous work show significant improvements.

Algorithmic Foundations of …, 2004

This paper addresses the manipulation planning problem which deals with motion planning for robots manipulating movable objects among static obstacles. We propose a general manipulation planning approach capable to deal with continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the existing planners. The algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. This property leads to reduce the problem by structuring the search-space. It allows us to devise a manipulation planner that directly captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner demonstrate its efficacy to solve complex manipulation problems.

1995

This paper deals with the manipulation planning problem, where the goal is to plan the motion of a robot so that it can move a given object from an initial con guration to a nal con guration while avoiding collisions with the static obstacles in the environment. Our speci c approach adapts Adraine's Clew Algorithm that has been shown effective for classical motion planning problem 1, 12]. In our approach, landmarks are placed in lower dimensional submanifolds of the composite con guration space. These landmarks represent stable grasps that are reachable from the initial con guration. From each new landmark, the planner attempts to reach the goal con guration by executing a local planner, again in a lower (but di erent) dimensional submanifold of the composite con guration space. We have implemented this approach and present initial experiments with a simple 2-dof planar arm among polygonal obstacles.

2010 IEEE International Conference on Robotics and Automation, 2010

In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRTbased algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque. We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

1995

Abstract An emerging paradigm in solving the classical motion planning problem (among static obstacles) is to capture the connectivity of the configuration space using a finite (but possibly large) set of landmarks (or nodes) in it. In this paper, the authors extend this paradigm to manipulation planning problem, where the goal is to plan the motion of a robot so that it can move a given object from an initial configuration to a final configuration while avoiding collisions with the static obstacles in the environment.

IEEE Robotics &amp amp amp Automation Magazine

In this work, we present an integrated approach for planning collision-free grasping motions. The proposed Grasp–RRT planner combines the three main tasks needed for grasping an object: building a feasible grasp, solving the inverse kinematics problem and determining a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRT-based algorithms are used to build a tree of reachable and collision-free configurations. During the tree generation, both grasp hypotheses and approach movements toward them are computed. The quality of reachable grasping poses is evaluated using grasp wrench space analysis. We present an extension to a dual arm planner which generates bimanual grasps together with collision-free dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

2010

Abstract To efficiently solve challenges related to motion-planning problems with dynamics, this paper proposes treating motion planning not just as a search problem in a continuous space but as a search problem in a hybrid space consisting of discrete and continuous components. A multilayered framework is presented which combines discrete search and sampling-based motion planning. This framework is called synergistic combination of layers of planning (SyCLoP) hereafter.

Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics, 2018

The work implements a new real-time flexible motion planning method used for reactive object manipulation in pick and place tasks typical of in-store logistics scenarios such as shelf replenishment of retail stores. This method uses a new hybrid pipeline to recognize and localize an object observed through a depth camera, by integrating and optimizing state of the art techniques. The proposed algorithm guarantees recognition robustness and localization accuracy. The desired object is then manipulated. The motion planner, based on the obstacles detected in the scene, plans a collision-free path towards the target pose. The planned trajectory optimizes a cost function that reflects the best solution among those available and produces natural and smooth path through a smart IK constrained solution which avoids robot unnecessary reconfigurations. A reactive control based on distributed proximity sensors is finally adopted to locally modify the planned trajectory in real time to avoid collisions with uncertain or dynamic obstacles. Experimental results in a supermarket scenario populated with cluttered obstacles demonstrate smoothness of the robot motions and reactive capabilities in a typical fetch and carry task.

… International Journal of …, 2004

This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems. KEY WORDS-manipulation task planning, path planning, probabilistic roadmaps

Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)

This paper proposes an adaptive framework for single shot motion planning, i.e., planning without preprocessing. This framework can be used in any situation, and in particular, is suitable for crowded environments in which the robot's free C-space has narrow corridors such as maintainability studies in complex 3D CAD models. Our strategy is to adaptively select a planner whose strengths match the current situation, and then, on-line, switch to a di erent planner when circumstances change. This requires techniques to evaluate the characteristics of the current query, and a set of planners which are characterized so that we can match the query with the best planner for it. Our experimental results in complex 3D environments show that our strategy solves queries that none of the planners could solve on their own.

IEEE Journal on Robotics and Automation, 1987

A simple and efficient algorithm is presented, using configuration space, to plan collision-free motions for general manipulators. An implementation of the algorithm for manipulators made up of revolute joints is also presented. The configuration-space obstacles for an n degree-of-freedom manipulator are approximated by sets of n-1dimensional slices, recursively built up from one-dimensional slices. This obstacle representation leads to an efficient approximation of the free space outside of the configuration-space obstacles.

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

In this work we tackle the path planning problem for a 21-dimensional snake robot-like manipulator, navigating a cluttered gas turbine for the purposes of inspection. Heuristic search based approaches are effective planning strategies for common manipulation domains. However, their performance on high dimensional systems is heavily reliant on the effectiveness of the action space and the heuristics chosen. The complex nature of our system, reachability constraints, and highly cluttered turbine environment renders naive choices of action spaces and heuristics ineffective. To this extent we have developed i) a methodology for dynamically generating actions based on online optimization that help the robot navigate narrow spaces, ii) a technique for lazily generating these computationally expensive optimization actions to effectively utilize resources, and iii) heuristics that reason about the homotopy classes induced by the blades of the turbine in the robot workspace and a Multi-Heuristic framework which guides the search along the relevant classes. The impact of our contributions is presented through an experimental study in simulation, where the 21 DOF manipulator navigates towards regions of inspection within a turbine.

2021 IEEE International Conference on Robotics and Automation (ICRA), 2021

In many applications, including logistics and manufacturing, robot manipulators operate in semi-structured environments alongside humans or other robots. These environments are largely static, but they may contain some movable obstacles that the robot must avoid. Manipulation tasks in these applications are often highly repetitive, but require fast and reliable motion planning capabilities, often under strict time constraints. Existing preprocessing-based approaches are beneficial when the environments are highly-structured, but their performance degrades in the presence of movable obstacles, since these are not modelled a priori. We propose a novel preprocessing-based method called Alternative Paths Planner (APP) that provides provably fixed-time planning guarantees in semi-structured environments. APP plans a set of alternative paths offline such that, for any configuration of the movable obstacles, at least one of the paths from this set is collisionfree. During online execution, a collision-free path can be looked up efficiently within a few microseconds. We evaluate APP on a 7 DoF robot arm in semi-structured domains of varying complexity and demonstrate that APP is several orders of magnitude faster than state-of-the-art motion planners for each domain. We further validate this approach with real-time experiments on a robotic manipulator.

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