Delay compensation for nonlinear teleoperators using predictor observers

This paper presents a delay compensation technique for nonlinear teleoperators by developing a predictor type sliding mode observer (SMO) that estimates future states of the slave operator. Predicted states are then used in control formulation. In the proposed scheme, disturbance observers (DOB) are also utilized to linearize nonlinear dynamics of the master and slave operators. It is shown that utilization of disturbance observers and predictor observer allow simple PD controllers to be used to provide stable position tracking for bilateral teleoperation. Proposed approach is verified with simulations where it is compared with two state-of-the-art methods. Successful experimental results with a bilateral teleoperation system consisting of a pair of pantograph robots also validates the proposed method

Predictive input delay compensation for motion control systems

This paper presents an analytical approach for the prediction of future motion to be used in input delay compensation of time-delayed motion control systems. The method makes use of the current and previous input values given to a nominally behaving system in order to realize the prediction of the future motion of that system. The generation of the future input is made through an integration which is realized in discrete time setting. Once the future input signal is created, it is used as the reference input of the remote system to enforce an input time delayed system, conduct a delay-free motion. Following the theoretical formulation, the proposed method is tested in experiments and the validity of the approach is verified

Commande Vision/Force de robots parallèles.

National audienceIn this paper, force and position control of parallel kinematic machines is discussed. Cartesian space computed torque control is applied to achieve force and position servoing directly in the task space within a sensor based control architecture. The originality of the approach resides in the use of a vision system as an exteroceptive pose measurement of a parallel machine tool for force control purposes

Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators

When robots operate in unknown environments small errors in postions can lead to large variations in the contact forces, especially with typical high-impedance designs. This can potentially damage the surroundings and/or the robot. Series elastic actuators (SEAs) are a popular way to reduce the output impedance of a robotic arm to improve control authority over the force exerted on the environment. However this increased control over forces with lower impedance comes at the cost of lower positioning precision and bandwidth. This article examines the use of an iteratively-learned feedforward command to improve position tracking when using SEAs. Over each iteration, the output responses of the system to the quantized inputs are used to estimate a linearized local system models. These estimated models are obtained using a complex-valued Gaussian Process Regression (cGPR) technique and then, used to generate a new feedforward input command based on the previous iteration's error. This article illustrates this iterative machine learning (IML) technique for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful convergence of the IML approach to reduce the tracking error.Comment: 9 pages, 16 figure. Submitted to AMC Worksho

Hybrid Control of Formations of Robots

We describe a framework for controlling a group of nonholonomic mobile robots equipped with range sensors. The vehicles are required to follow a prescribed trajectory while maintaining a desired formation. By using the leader-following approach, we formulate the formation control problem as a hybrid (mode switching) control system. We then develop a decision module that allows the robots to automatically switch between continuous-state control laws to achieve a desired formation shape. The stability properties of the closed-loop hybrid system are studied using Lyapunov theory. We do not use explicit communication between robots; instead we integrate optimal estimation techniques with nonlinear controllers. Simulation and experimental results verify the validity of our approach

Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning

This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is processed by a bio-inspired Spiking Neural Network (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning, as observed in the brain. In contrast to most learning-based SLAM systems%, which a) require the acquisition of a representative dataset of the environment in which navigation must be performed and b) require an off-line training phase, our method does not require any offline training phase, but rather the SNN continuously learns features from the input data on the fly via STDP. At the same time, the SNN outputs are used as feature descriptors for loop closure detection and map correction. We conduct numerous experiments to benchmark our system against state-of-the-art RGB methods and we demonstrate the robustness of our DVS-Radar SLAM approach under strong lighting variations

Obstacle Avoidance and Proscriptive Bayesian Programming

Unexpected events and not modeled properties of the robot environment are some of the challenges presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a probabilistic approach called Bayesian Programming, which aims to deal with the uncertainty, imprecision and incompleteness of the information handled to solve the obstacle avoidance problem. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. A video illustration of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac

A climbing autonomous robot for inspection application in 3D complex environment

Often inspection and maintenance work involve a large number of highly dangerous manual operations, especially within industrial fields such as shipbuilding and construction. This paper deals with the autonomous climbing robot which uses the “caterpillar” concept to climb in complex 3D metallic-based structures. During its motion the robot generates in real-time the path and grasp planning in order to ensure stable self-support to avoid the environment obstacles, and to optimise the robot consumption during the inspection. The control and monitoring of the robot is achieved through an advanced Graphical User Interface to allow an effective and user friendly operation of the robot. The experiments confirm its advantages in executing the inspection operations.This work has been partially funded by the Spanish government agency CICYT under project TAP95-0088. The authors would like to acknowledge the technical support of A. Jardón, E. Jiménez, C. Palazuelos, J.A. Campo and F. Manera and also the company of APTECA for its help in the mechanical development.Publicad

Statistical multi-moment bifurcations in random delay coupled swarms

We study the effects of discrete, randomly distributed time delays on the dynamics of a coupled system of self-propelling particles. Bifurcation analysis on a mean field approximation of the system reveals that the system possesses patterns with certain universal characteristics that depend on distinguished moments of the time delay distribution. Specifically, we show both theoretically and numerically that although bifurcations of simple patterns, such as translations, change stability only as a function of the first moment of the time delay distribution, more complex patterns arising from Hopf bifurcations depend on all of the moments

Sampling-based Multi-robot Motion Planning

International audienceThis paper describes a sampling-based approach to multi-robot motion planning. The proposed approach is centralized, which aims to reduce interference between mobile robots such as collision, congestion and deadlock, by increasing the number of waypoints. The implementation based on occupancy grid map is decomposed into three steps: the first step is to identify primary waypoints by using the Voronoi diagram, the second step is to generate additional waypoints by sampling the Voronoi diagram, and the last step is to assign the waypoints to robots by using the Hungarian method. The approach has been implemented and tested in simulation and the experimental results show a good system performance for multi-robot motion planning