295 research outputs found
Energy-saver mobile manipulator based on numerical methods
The work presents the kinematic and dynamic control of a mobile robotic manipulator
system based on numerical methods. The proposal also presents the curvature analysis of a path
not parameterized in time, for the optimization of energy consumption. The energy optimization
considers two aspects: the velocity of execution in curves and the amount of movements generated
by the robotic system. When a curve occurs on the predefined path, the execution velocity is
analyzed throughout the system in a unified method to prevent skid e ects from a ecting the mobile
manipulator, while the number of movements is limited by the redundancy presented by the robotic
system to optimize energy use. The experimental results are shown to validate the mechanical and
electronic construction of the system, the proposed controllers, and the saving of energy consumptionThis research was funded by Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia–CEDI
Type-2 Fuzzy Hybrid Controller Network for Robotic Systems
Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control
Modulation of Robot Orientation via Leg-Obstacle Contact Positions
We study a quadrupedal robot traversing a structured (i.e., periodically spaced) obstacle field driven by an open-loop quasi-static trotting walk. Despite complex, repeated collisions and slippage between robot legs and obstacles, the robot’s horizontal plane body orientation (yaw) trajectory can converge in the absence of any body level feedback to stable steady state patterns. We classify these patterns into a series of “types” ranging from stable locked equilibria, to stable periodic oscillations, to unstable or mixed period oscillations. We observe that the stable equilibria can bifurcate to stable periodic oscillations and then to mixed period oscillations as the obstacle spacing is gradually increased. Using a 3D-reconstruction method, we experimentally characterize the robot leg-obstacle contact configurations at each step to show that the different steady patterns in robot orientation trajectories result from a self-stabilizing periodic pattern of leg-obstacle contact positions. We present a highly-simplified coupled oscillator model that predicts robot orientation pattern as a function of the leg-obstacle contact mechanism. We demonstrate that the model successfully captures the robot steady state for different obstacle spacing and robot initial conditions. We suggest in simulation that using the simplified coupled oscillator model we can create novel control strategies that allow multi-legged robots to exploit obstacle disturbances to negotiate randomly cluttered environments. For more information: Kod*lab (link to kodlab.seas.upenn.edu
Multi-objective Anti-swing Trajectory Planning of Double-pendulum Tower Crane Operations using Opposition-based Evolutionary Algorithm
Underactuated tower crane lifting requires time-energy optimal trajectories
for the trolley/slew operations and reduction of the unactuated swings
resulting from the trolley/jib motion. In scenarios involving non-negligible
hook mass or long rig-cable, the hook-payload unit exhibits double-pendulum
behaviour, making the problem highly challenging. This article introduces an
offline multi-objective anti-swing trajectory planning module for a
Computer-Aided Lift Planning (CALP) system of autonomous double-pendulum tower
cranes, addressing all the transient state constraints. A set of auxiliary
outputs are selected by methodically analyzing the payload swing dynamics and
are used to prove the differential flatness property of the crane operations.
The flat outputs are parameterized via suitable B\'{e}zier curves to formulate
the multi-objective trajectory optimization problems in the flat output space.
A novel multi-objective evolutionary algorithm called Collective Oppositional
Generalized Differential Evolution 3 (CO-GDE3) is employed as the optimizer. To
obtain faster convergence and better consistency in getting a wide range of
good solutions, a new population initialization strategy is integrated into the
conventional GDE3. The computationally efficient initialization method
incorporates various concepts of computational opposition. Statistical
comparisons based on trolley and slew operations verify the superiority of
convergence and reliability of CO-GDE3 over the standard GDE3. Trolley and slew
operations of a collision-free lifting path computed via the path planner of
the CALP system are selected for a simulation study. The simulated trajectories
demonstrate that the proposed planner can produce time-energy optimal
solutions, keeping all the state variables within their respective limits and
restricting the hook and payload swings.Comment: 14 pages, 14 figures, 6 table
Advanced Mobile Robotics: Volume 3
Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective
Bio-Inspired Robotics
Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
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