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This paper deals with control of the brachiation robot. The brachiation is a type of mobile robot that moves from branch to branch like a long-armed ape. Here, as a new innovation, Pontryagin’s minimum principle is used to obtain the optimal trajectories for two different problems. The first problem is “Brachiation between fixed branches with diffe...
Citations
... [4][5][6] The control problem becomes even more complicated when one considers that brachiation robots are often underactuated. [7][8][9] On the one side, underactuation may be pursued in the design of these robotic systems in an aim to reduce their weight and energy consumption after removing speci¯c actuators from them. On the other side, functioning of these robotic systems in an underactuation mode signi¯es that these robots are fault tolerant in the case of actuators' failures and that they continue to perform reliably their tasks even if speci¯c actuators are subjected to a fault. ...
... experiments. The obtained results are depicted in Figs.[3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Indicative values for the simulation experiments have been m 1 ¼ 1 kg, l 1 ¼ 1 m, m 2 ¼ 1 kg, l 2 ¼ 1 m and m 3 ¼ 0:5 kg, l 3 ¼ 0:5 m. ...
This paper proposes a nonlinear optimal control approach for mulitple degrees of freedom (DOF) brachiation robots, which are often used in inspection and maintenance tasks of the electric power grid. Because of the nonlinear and multivariable structure of the related state-space model, as well as because of underactuation, the control problem of these robots is nontrivial. The dynamic model of the brachiation robots undergoes first approximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method. For the approximately linearized model, an H-infinity feedback controller is designed. The linearization procedure relies on the Jacobian matrices of the brachiation robots’ state-space model. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the brachiation robots, under model uncertainties and external perturbations. For the computation of the controller’s feedback gains an algebraic Riccati equation is solved at each time-step of the control method. The global stability properties of the control scheme are proven through Lyapunov analysis. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the brachiation robots, under moderate variations of the control inputs.
Endowing a robot with skills to perform manipulative tasks has an important role in developing an intelligent robot. To manipulate objects, a robot needs perception and action skills. However, designing the programming framework to integrate a variety of skills in a robot system is a challenging task and significantly influences the robot performance. In this paper, we present a behavior-based manipulator programming framework which is based on Extensible Agent Behavior Specification Language (XABSL) to manage behaviors in a robot system. To achieve the flexibility and reusability of robot behaviors required for practice applications, the proposed concept is to implement a programming framework for robot manipulation into two steps: first, perception and action behaviors are created to endow a robot with fundamental skills to perform manipulative tasks; second, using the XABSL framework, the created behaviors are simply planned by an option graph. Because behaviors are planned to be activated by certain stimuli and respond accordingly, programming robot manipulative tasks becomes simpler. Moreover, by the programming framework for robot manipulative tasks, the programming effort is reduced considerably. In our experiments, we provide an extensive validation of the proposed behavior-based programming framework on the manipulative tasks such as stacking cubes and solving rubik's cube.
Temporal projection is the computational problem of predicting what will happen when a robot executes its plan. Temporal projection for everyday manipulation tasks such as table setting and cleaning is a challenging task. Symbolic projection methods developed in Artificial Intelligence are too abstract to reason about how to place objects such that they do not hinder future actions. Simulation-based projection is fine-grained enough but computationally too expensive as it is not able to abstract away from the execution of uninteresting actions (such as navigation). In this paper we propose a novel temporal projection mechanism that combines the strengths of both approaches: it is able to abstract away from the execution of continuous but uninteresting actions and provides the realism and fine grainedness needed to reason about critical situations.
Mobile manipulation robots are becoming more and more common and begin to extend their task spectrum towards more general housework activities. The sequence of actions needed to accomplish such tasks can be obtained from instructions on the Internet originally written for humans. While giving valuable information about the types of actions and some of their parameters, these instructions usually lack information that humans consider to be obvious. In this paper, we investigate how we can equip robots with sufficient knowledge and inference mechanisms to competently detect and fill such knowledge gaps in descriptions of everyday activities. We present methods for projecting the effects of actions and processes, for inferring action parameters like the objects and locations to be used, and introduce representations for reasoning about object transformations resulting from the effects of actions.
Recently, there has been growing interest in enabling robots to use task instructions from the Internet and to share tasks they have learned with each other. To competently use, select and combine such instructions, robots need to be able to find out if different instructions describe the same task, which parts of them are similar and which ones differ. In this paper, we investigate techniques for automatically aligning symbolic task descriptions. We propose to adapt and extend established algorithms for sequence alignment that are commonly used in bioinformatics in order to make them applicable to robot action specifications. The extensions include methods for the comparison of complex sequence elements, for taking the semantic similarity of actions into account, and for aligning descriptions at different levels of granularity. We evaluate the algorithm on two large datasets of observations of human everyday tasks and show that they are able to align action sequences performed by different subjects in very different ways.
This article gives an overview of cognition-enabled robot control, a computational model for controlling autonomous service robots to achieve home chore task intelligence. For the realization of task intelligence, this computational model puts forth three core principles, which essentially involve the combination of reactive behavior specifications represented as semantically interpretable plans with inference mechanisms that enable flexible decision making. The representation of behavior specifications as plans enables the robot to not only execute the behavior specifications but also to reason about them and alter them during execution. We provide a description of a complete system for cognition-enabled robot control that implements the three core principles, demonstrating the feasibility of our approach.
