Multirobot heterogeneous control considering secondary objectives

Cooperative robotics has considered tasks that are executed frequently, maintaining the shape and orientation of robotic systems when they fulfill a common objective, without taking advantage of the redundancy that the robotic group could present. This paper presents a proposal for controlling a group of terrestrial robots with heterogeneous characteristics, considering primary and secondary tasks thus that the group complies with the following of a path while modifying its shape and orientation at any time. The development of the proposal is achieved through the use of controllers based on linear algebra, propounding a low computational cost and high scalability algorithm. Likewise, the stability of the controller is analyzed to know the required features that have to be met by the control constants, that is, the correct values. Finally, experimental results are shown with di erent configurations and heterogeneous robots, where the graphics corroborate the expected operation of the proposalThis research was funded by Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia–CEDI

Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach

In this paper, we propose a topology of Recurrent Neural Network (RNN) based on a metaheuristic optimization algorithm for the tracking control of mobile-manipulator while enforcing nonholonomic constraints. Traditional approaches for tracking control of mobile robots usually require the computation of Jacobian-inverse or linearization of its mathematical model. The proposed algorithm uses a nature-inspired optimization approach to directly solve the nonlinear optimization problem without any further transformation. First, we formulate the tracking control as a constrained optimization problem. The optimization problem is formulated on position-level to avoid the computationally expensive Jacobian-inversion. The nonholonomic limitation is ensured by adding equality constraints to the formulated optimization problem. We then present the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm to solve the optimization problem efficiently using very few mathematical operations. We present a theoretical analysis of the proposed algorithm and show that its computational cost is linear with respect to the degree of freedoms (DOFs), i.e., O(m). Additionally, we also prove its stability and convergence. Extensive simulation results are prepared using a simulated model of IIWA14, a 7-DOF industrial-manipulator, mounted on a differentially driven cart. Comparison results with particle swarm optimization (PSO) algorithm are also presented to prove the accuracy and numerical efficiency of the proposed controller. The results demonstrate that the proposed algorithm is several times (around 75 in the worst case) faster in execution as compared to PSO, and suitable for real-time implementation. The tracking results for three different trajectories; circular, rectangular, and rhodonea paths are presented

Reuleaux: Robot Base Placement by Reachability Analysis

Before beginning any robot task, users must position the robot's base, a task that now depends entirely on user intuition. While slight perturbation is tolerable for robots with moveable bases, correcting the problem is imperative for fixed-base robots if some essential task sections are out of reach. For mobile manipulation robots, it is necessary to decide on a specific base position before beginning manipulation tasks. This paper presents Reuleaux, an open source library for robot reachability analyses and base placement. It reduces the amount of extra repositioning and removes the manual work of identifying potential base locations. Based on the reachability map, base placement locations of a whole robot or only the arm can be efficiently determined. This can be applied to both statically mounted robots, where position of the robot and work piece ensure the maximum amount of work performed, and to mobile robots, where the maximum amount of workable area can be reached. Solutions are not limited only to vertically constrained placement, since complicated robotics tasks require the base to be placed at unique poses based on task demand. All Reuleaux library methods were tested on different robots of different specifications and evaluated for tasks in simulation and real world environment. Evaluation results indicate that Reuleaux had significantly improved performance than prior existing methods in terms of time-efficiency and range of applicability.Comment: Submitted to International Conference of Robotic Computing 201

Design and walking analysis of proposed four-legged glass cleaning robot

In this study, a legged and wheeled robot model was proposed for cleaning the glass of greenhouses. The robot has four wheels and four legs, each with three degrees of freedom (DOF). The design, kinematic analysis and simulation of the robot was carried out. Glass greenhouses are created by placing glass sheets on T-shaped iron bars arranged in parallel at certain intervals. The robot performs the glass cleaning task by performing two different movements on greenhouse roof. As a first movement, the robot moves like a train moving on the rail on iron bars with wheels, cleaning the glass as it travels. After cleaning the glasses placed between two iron bars along a column, as second movement, the robot passes the next column using legs. These two movements continue until the entire roof of the greenhouse is cleaned. Kinematic analysis of this robot, which is designed with mechanical properties that can make these movements, has been made. Walking simulation of the robot was carried out according to the kinematic analysis. The simulation results showed that this proposed robot can be used to clean glass on the greenhouse roof

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

The power dissipation method and kinematic reducibility of multiple-model robotic systems

This paper develops a formal connection between the power dissipation method (PDM) and Lagrangian mechanics, with specific application to robotic systems. Such a connection is necessary for understanding how some of the successes in motion planning and stabilization for smooth kinematic robotic systems can be extended to systems with frictional interactions and overconstrained systems. We establish this connection using the idea of a multiple-model system, and then show that multiple-model systems arise naturally in a number of instances, including those arising in cases traditionally addressed using the PDM. We then give necessary and sufficient conditions for a dynamic multiple-model system to be reducible to a kinematic multiple-model system. We use this result to show that solutions to the PDM are actually kinematic reductions of solutions to the Euler-Lagrange equations. We are particularly motivated by mechanical systems undergoing multiple intermittent frictional contacts, such as distributed manipulators, overconstrained wheeled vehicles, and objects that are manipulated by grasping or pushing. Examples illustrate how these results can provide insight into the analysis and control of physical systems