Khelifa Baizid

Longitudinal and lateral slip control of autonomous wheeled mobile robot for trajectory tracking

This research formulates a path-following control problem subjected to wheel slippage and skid and solves it using a logic-based control scheme for a wheeled mobile robot (WMR). The novelty of the proposed scheme lies in its methodology that considers both longitudinal and lateral slip components. Based on the derived slip model, the controller for longitudinal motion slip has been synthesized. Various control parameters have been studied to investigate their effects on the performance of the controller resulting in selection of their optimum values. The designed controller for lateral slip or skid is based on the proposed side friction model and skid check condition. Considering a car-like WMR, simulation results demonstrate the effectiveness of the proposed control scheme. The robot successfully followed the desired circular trajectory in the presence of wheel slippage and skid. This research finds its potential in various applications involving WMR navigation and control.

Experiments on behavioral coordinated control of an Unmanned Aerial Vehicle manipulator system

This work tackles the problem of controlling an Unmanned Aerial Vehicle equipped with a robotic Manipulator and it has been developed within the framework of the EU-funded ARCAS (Aerial Robotics Cooperative Assembly System) project. A behavioral control, based on the Null Space-based Behavioral (NSB) paradigm, is proposed to tackle the coordination between the arm and vehicle motions. To this aim, a set of basic functionalities (called elementary behaviors) are designed and combined in a priority order to attain complex tasks (called compound behaviors). The proposed controller has been experimentally validated on a multirotor aircraft with an attached 6 Degree of Freedoms manipulator. Two experimental case studies, involving several compound behaviors, have been reported and the results show the effectiveness of the approach.

Behavioral control of unmanned aerial vehicle manipulator systems

In this paper a behavioral control framework is developed to control an unmanned aerial vehicle-manipulator (UAVM) system, composed by a multirotor aerial vehicle equipped with a robotic arm. The goal is to ensure vehicle-arm coordination and manage complex multi-task missions, where different behaviors must be encompassed in a clear and meaningful way. In detail, a control scheme, based on the null space-based behavioral paradigm, is proposed to handle the coordination between the arm and vehicle motion. To this aim, a set of basic functionalities (elementary behaviors) are designed and combined in a given priority order, in order to attain more complex tasks (compound behaviors). A supervisor is in charge of switching between the compound behaviors according to the mission needs and the sensory feedback. The method is validated on a real testbed, consisting of a multirotor aircraft with an attached 6 Degree of Freedoms manipulator, developed within the EU-funded project ARCAS (Aerial Robotics Cooperative Assembly System). At the the best of authors’ knowledge, this is the first time that an UAVM system is experimentally tested in the execution of complex multi-task missions. The results show that, by properly designing a set of compound behaviors and a supervisor, vehicle-arm coordination in complex missions can be effectively managed.

CAVIS: a Control software Architecture for cooperative multi-unmanned aerial VehIcle-manipulator Systems

Abstract In this paper a Control software Architecture for Cooperative multiple unmanned aerial VehIcle-manipulator Systems (CAVIS) is presented. The core of the architecture is a set of software components, communicating each other through a set of defined messages. To handle multiple control objectives simultaneously, a library of elementary behaviors is defined; then, multiple elementary behaviors are combined, in a given priority order, into tasks (compound behaviors); to this aim the Null-Space-based Behavioral (NSB) approach has been adopted. An application example, involving a cooperative transportation of a bar by two aerial vehicle-manipulator systems, is developed to assess the performance of the proposed architecture.

Safety in coordinated control of multiple unmanned aerial vehicle manipulator systems: Case of obstacle avoidance

This paper treats the safety issues in coordinated transportation of an object via multiple Unmanned Aerial Vehicles with Manipulator Systems (UAVMS); in particular, the case of obstacle avoidance is considered. The proposed paradigm is based on the so-called Null-Space-based Behavioral (NSB) control approach, that allows to achieve multiple tasks simultaneously. Two different scenarios of bar transportation using multi-UAVMSs have been developed, tested and compared in simulation: in the first scenario, the obstacle avoidance is achieved at the coordination level, where the controller allows the whole system (UAVMSs and bar) to circumnavigate the obstacle; while in the second scenario only the UAVMS closest to the obstacle modifies its trajectory to overcome the obstacle by keeping the desired trajectory of the bar unchanged.

Vector Maps: A Lightweight and Accurate Map Format for Multi-robot Systems

SLAM algorithms produce accurate maps that allow localization with typically centimetric precision. However, such a map is materialized as a large Occupancy Grid. Beside the high memory footprint, Occupancy Grid Maps lead to high CPU consumption for path planning. The situation is even worse in the context of multi-robot exploration. Indeed, to achieve coordination, robots have to share their local maps and merge ones provided by their teammates. These drawbacks of Occupancy Grid Maps can be mitigated by the use of topological maps. However, topological maps do not allow accurate obstacle delimitations needed for autonomous robots exploration. So, robots still have to handle with Occupancy Grid Maps. We argue that Vector-based Maps which materialize obstacles using collections of vectors is a more interesting alternative. Vector Maps both provide accurate metric information likewise Occupancy Grid Maps, and represent data as a graph that can be processed for path planning and maps merging as efficiently as with topological maps. Conclusions are backed by several metrics computed with several terrains that differ in size, form factor, and obstacle density.

RRS: Rapidly-Exploring Random Snakes a New Method for Mobile Robot Path Planning

Recently, sampling-based path planning algorithms have been implemented in many practical robotics tasks. However, little improvements have been dedicated to the returned solution (quality) and sampling process. The aim of this paper is to introduce a new technique that improves the classical rapidly-exploring random trees (RRT) algorithm. First, the sampling step is modified in order to increase the number of possible solutions in the free space. Second, within the possible solutions, we apply an optimization scheme that gives the best solution in term of safety and shortness. The proposed solution, namely, rapidly-exploring random snakes (RRS) is a combination of a modified deformable Active Contours Model (called Snakes) and the RRT. The RRS takes the advantage of both RRT and deformable Snakes contours, respectively, in: rapidly searching new candidate nodes in the free space and circumnavigating obstacles by calculating a safe sub-path in the free space toward the new node created by the RRT. In comparison to the classical RRT, the proposed algorithm increases the probability of completeness, accelerates the convergence and generates a much safer and shorter open-loop solution, hence, increasing considerably the efficiency of the classical RRT. The proposed approach has been validated via numerical simulations and experimental results with a mobile robot.

Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators

Industrial robot manipulators must work as fast as possible in order to increase the productivity. This goal could be achieved by increasing robots speed or/and optimizing the trajectories followed by robots while performing assembly, welding or similar tasks. In our contribution, we focus on the second aspect and we target the shortening of paths between task-points. In other words, the goal is to find the shorter traveled distance between different configurations in the coordinate space. In addition to the short distance goal, we aim as well to impose both IKM (Inverse Kinematic Model) and the relative position and orientation of the manipulator regarding the task-points. To this end, we propose an optimization method based on Genetics Algorithms. The method is validated via numerical and graphical simulation, where, results show that the total cycle time required to perform a spot-welding task of an industrial car-body by a 6-DOFs (Degree Of Freedoms) industrial manipulator was drastically reduced.

Multi-robots coverage approach

In this paper we present a full and effective system allowing the deployment of N semi-autonomous robots in order to cover a given area for video surveillance and search purposes. The coverage problem is solved through a new technique based on the exploitation of Voronoi tessellations. To supervise a given area, a set of viewpoints are extracted, then visited by a group of mobile rover. Robots paths are calculated by resorting a salesman problem through Multi-objective Genetic Algorithms. In the running phase, robots deal with both motion and sensors uncertainties while performing the pre-established paths. Results of indoor scenario are given.