In manipulation planning, dynamic interactions between the objects and the robots play a significant role. In this scope, dynamic engines allow to consider them within motion planners, giving rise to physics-based motion planners that consider the purposeful manipulation of objects. In this context, the representation of knowledge regarding how the objects have to be manipulated
eases a semantic-based reasoning that reduces the computational cost of physics-based planners. In this work, an ontology framework is proposed to organize the knowledge needed for physics-based manipulation planning, allowing to derive
manipulation regions and behaviors. A semantic map is constructed to categorize and assign the manipulation constraints based on the robot, the objects and the type of actions. The ontology framework can be queried using Description Language to obtain the necessary knowledge for the robot to manipulate the objects in its environment.
Grasping an object in unstructured and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exits. High-level knowledge and reasoning processes, as well as the allowing of interaction between objects, can enhance the planning efficiency in such environments. In this direction, this study proposes a knowledge-oriented physics-based motion planning approach for a hand-arm system that uses a high-level knowledge-based reasoning to partition the workspace into regions to both guide the planner and reason about the result of the dynamical interactions between rigid bodies. The proposed planner is a kinodynamic RRT that uses a region-biased state sampling strategy and a smart validity checker that takes into account uncertainty in the pose of the objects. Complex dynamical interactions along with possible physics-based constraints such as friction and gravity are handled by a physics engine that is used as the RRT state propagator. The proposal is validated for different scenarios in simulation and in a real environment using a 7-degree-of-freedom KUKA Lightweight robot equipped with a two-finger gripper. The results show a significant improvement in the success rate of the execution of the computed plan in the presence of object pose uncertainty.
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 ¿-PMP can be used with any kinodynamic planner, thus giving rise to e.g. ¿-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.
One of the main foci of robotics is nowadays centered in providing a great degree of autonomy to robots. A fundamental step in this direction is to give them the ability to plan in discrete and continuous spaces to find the required motions to complete a complex task. In this line, some recent approaches describe tasks with Linear Temporal Logic (LTL) and reason on discrete actions to guide sampling-based motion planning, with the aim of finding dynamically-feasible motions that satisfy the temporal-logic task specifications. The present paper proposes an LTL planning approach enhanced with the use of ontologies to describe and reason about the task, on the one hand, and that includes physics-based motion planning to allow the purposeful manipulation of objects, on the other hand. The proposal has been implemented and is illustrated with didactic examples with a mobile robot in simple scenarios where some of the goals are occupied with objects that must be removed in order to fulfill the task.
Rosell, J.; Nuño, E.; Claret, J.; Zaplana, I.; Garcia, N.; Akbari, A.; Ud Din, M.; Palomo, L.; Pérez, A.; Mas, Orestes; Basañez, L. Jornadas Nacionales de Robótica p. 1-6 Data de presentació: 2017-06-08 Presentació treball a congrés
In work environments, the use of dexterous mobile manipulators as co-workers poses several challenges with respect to the human-robot collaboration. On the one hand, its focus is in the autonomy (i.e. the mobile manipulators are required to cooperate
with humans by performing autonomously complementary tasks while moving around in the human environment and in their presence). On the other hand, its focus is in the interaction (i.e. with a virtual interaction via teleoperation, or with a physical interaction through an object jointly handled). The project summarized in this paper deals with the development of planning, reasoning and control algorithms, and of the necessary software to provide mobile manipulators with the autonomy (mainly through the simultaneous planning of tasks and motions) and the capacity of interaction mainly through teleoperation) to allow the cooperation with humans.
Akbari, A.; Muhayyuddin; Rosell, J. IEEE International Conference on Emerging Technologies and Factory Automation p. 1-8 DOI: 10.1109/ETFA.2016.7733599 Data de presentació: 2016-09 Presentació treball a congrés
In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge.
The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.
—This work presents a knowledge-based task and motion planning framework based on a version of the FastForward task planner. A reasoning process on symbolic literals in terms of knowledge and geometric information about the workspace, together with the use of a physics-based motion planner, is proposed to evaluate the applicability and feasibility of manipulation actions and to compute the heuristic values that guide the search. The proposal results in low-cost physically-feasible plans
Motion planning has evolved from coping with simply geometric problems to physics-based ones that incorporate the kinodynamic and the physical constraints imposed by the robot and the physical world. Therefore, the criteria for evaluating physics-based motion planners goes beyond the computational complexity (e.g. in terms of planning time) usually used as a measure for evaluating geometrical planners, in order to consider also the quality of the solution in terms of dynamical parameters. This study proposes an evaluation criteria and analyzes the performance of several kinodynamic planners, which are at the core of physics-based motion planning, using different scenarios with fixed and manipulatable objects. RRT, EST, KPIECE and SyCLoP are used for the benchmarking. The results show that KPIECE computes the time-optimal solution with heighest success rate, whereas, SyCLoP compute the most power-optimal solution among the planners used.
To cope with the growing complexity of manipulation tasks, the way to combine and access information from high- and low-planning levels has recently emerged as an interesting challenge in robotics. To tackle this, the present paper first represents the manipulation problem, involving knowledge about the world and the planning phase, in the form of an ontology. It also addresses a high-level and a low-level reasoning processes, this latter related with physics-based issues, aiming to appraise manipulation actions and prune the task planning phase from dispensable actions. In addition, a procedure is contributed to run these two-level reasoning processes simultaneously in order to make task planning more efficient. Eventually, the proposed planning approach is implemented and simulated through an example.
Robotic manipulation involves actions where contacts occur between the robot and the objects. In this scope, the availability of physics-based engines allows motion planners to comprise dynamics between rigid bodies, which is necessary for planning this type of actions. However, physics-based motion planning is computationally intensive due to the high dimensionality of the state space and the need to work with a low integration step to find accurate solutions. On the other hand, manipulation actions change the environment and conditions further actions and motions. To cope with this issue, the representation of manipulation actions using ontologies enables a semantic-based inference processe that alleviates the computational cost of motion planning. This paper proposes a manipulation planning framework where physics-based motion planning is enhanced with ontological knowledge representation and reasoning. The proposal has been implemented and is illustrated and validated with a simple example. Its use in grasping tasks in cluttered environments is currently under development
For everyday manipulation tasks, the combination of task and motion planning is required regarding the need of providing the set of possible subtasks which have to be done and how to perform them. Since many alternative plans may exist, the determination of their feasibility and the identification of the best one is a great challenge in robotics. To address this, this paper proposes: a) a version of GraphPlan (one of the best current approaches to task planning) that has been modified to use ontological knowledge and to allow the retrieval of all possible plans; and b) a physics-based reasoning process that determines the feasibility of the resulting plans and an associated cost that allows to select the best one among them. The proposed framework has been implemented and is illustrated through an example.
Rosell, J.; Pérez, A.; Akbari, A.; Ud Din, M.; Palomo, L.; Garcia, N. IEEE International Conference on Emerging Technologies and Factory Automation p. 1-8 Data de presentació: 2014-09-18 Presentació treball a congrés
This paper presents the software tool used at the Institute of Industrial and Control Engineering (IOC-UPC) for teaching and research in robot motion planning. The tool allows to cope with problems with one or more robots, being a generic robot defined as a kinematic tree with a mobile base, i.e. the tool can plan and simulate from simple two degrees of freedom free-flying robots to multi-robot scenarios with mobile manipulators equipped with anthropomorphic hands. The main core of planners is provided by the Open Motion Planning Library (OMPL). Different basic planners can be flexibly used
and parameterized, allowing students to gain insight into the different planning algorithms. Among the advanced features the tool allows to easily define the coupling between degrees of freedom, the dynamic simulation and the integration with task planers. It is principally being used in the research of motion planning strategies for hand-arm robotic systems.