Bakir Lacevic

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster-Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster-Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robots trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.

Kinetostatic danger field - a novel safety assessment for human-robot interaction

This paper presents a novel method for evaluating the danger within the environment of a robot manipulator. It is based on the introduced concept of kinetostatic danger field, a quantity that captures the complete state of the robot - its configuration and velocity. The field itself is invariant with respect to objects around the robot and can be computed in any given point of the workspace using measurements from the proprioceptive sensors. Moreover, all the computation can be performed in closed form, yielding compact algebraic expressions that allow for real time applications. The danger field is not only a meaningful indicator about the risk in the vicinity of the robot, but can also be fed back within control skills that implement some well known safety strategies like collision avoidance and virtual impedance control, provided that some environment perception is available in order to determine the points where the field should be computed. Kinematic redundancy for simultaneous task performance and danger minimization can be exploited. The methodology described in the paper is supported with simulation results.

Safety Assessment and Control of Robotic Manipulators Using Danger Field

This paper presents a synergistic approach to danger assessment and safety-oriented control of articulated robots that are based on a quantity called danger field. This quantity captures the state of the robot as a whole and indicates how dangerous the current posture and velocity of the robot are to the objects in the environment. The field itself is invariant with respect to objects around the robot and can be computed in any given point of the robots workspace using measurements from the proprioceptive sensors. Furthermore, the danger field can be expressed in the closed form, which enables its fast computation. Apart from being a pure safety assessment, the danger field provides a natural prelude to safety-oriented control strategy. Namely, the information about the danger field can easily be fed back to shape standard control schemes in order to make the motion of the robot safer to the environment. The proposed method is validated through simulations and experiments.

Efficient Path Planning Algorithm for Mobile Robot Navigation with a Local Minima Problem Solving

This paper proposes a new reactive planning algorithm for mobile robot navigation in unknown environments. The overall navigation system consists of three navigation subsystems. The lower level subsystem deals with the control of the linear and angular velocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is in the medium level, and it is a nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. The high level subsystem uses the Fuzzy logic and Dempster-Shafer evidence theory to design the fusion of sensor data, map building and path planning tasks. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. A particular attention is paid to detection of the robots trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.

Towards a complete safe path planning for robotic manipulators

We propose a novel method of path planning for robotic manipulators that is based on the tree expansion via bubbles of free configuration space. The algorithm is designed to yield collision-free paths that also tend to minimize a certain danger criterion. This is achieved by embedding a suitably tailored heuristics within the algorithm. For that purpose we use a recently proposed safety assessment based on the concept of the danger field - an easily computable quantity that captures the complete kinematic behavior of the manipulator. Under the assumption that a systematic graph search technique dictates the tree growth, we prove the algorithms completeness.

A novel passivity-based control law for safe human-robot coexistence

This paper presents a new control law for robotic manipulators in unstructured environments which guarantees the achievement of the goal position without incurring in local minima. The passivity of the closed-loop system renders this control scheme well-suited for human-robot coexistence, especially when the robot is supposed to share its workspace with humans. The given control law has been implemented and experimentally tested in a realistic scenario, demonstrating the effectiveness in driving the robot to a given configuration in a cluttered environment without any offline planning phase.

Nonlinear Motion Control of Mobile Robot Dynamic Model

The problem of motion planning and control of mobile robots has attracted the interest of researchers in view of its theoretical challenges because of their obvious relevance in applications. From a control viewpoint, the peculiar nature of nonholonomic kinematics and dynamic complexity of the mobile robot makes that feedback stabilization at a given posture cannot be achieved via smooth time-invariant control (Oriolo et al., 2002). This indicates that the problem is truly nonlinear; linear control is ineffective, and innovative design techniques are needed. In recent years, a lot of interest has been devoted to the stabilization and tracking of mobile robots. In the field of mobile robotics, it is an accepted practice to work with dynamical models to obtain stable motion control laws for trajectory following or goal reaching (Fierro & Lewis, 1997). In the case of control of a dynamic model of mobile robots authors usually used linear and angular velocities of the robot (Fierro & Lewis, 1997; Fukao et al., 2000) or torques (Rajagopalan & Barakat , 1997; Topalov et al., 1998) as an input control vector. The central problem in this paper is reduction of control torques during the reference position tracking. In the case of dynamic mobile robot model, the position control law ought to be nonlinear in order to ensure the stability of the error that is its convergence to zero (Oriollo et al., 2002). The most authors solved the problem of mobile robot stability using nonlinear backstepping algorithm (Tanner & Kyriakopoulos, 2003) with constant parameters (Fierro & Lewis, 1997), or with the known functions (Oriollo et al., 2002). In (Tanner & Kyriakopoulos, 2003) a combined kinematic/torque controller law is developed using backstepping algorithm and stability is guaranteed by Lyapunov theory. In (Oriollo et al., 2002) method for solving trajectory tracking as well as posture stabilization problems, based on the unifying framework of dynamic feedback linearization was presented. The objective of this chapter is to present advanced nonlinear control methods for solving trajectory tracking as well as convergence of stability conditions. For these purposes we developed a backstepping (Velagic et al., 2006) and fuzzy logic position controllers (Lacevic, et al., 2007). It is important to note that optimal parameters of both controllers are adjusted using genetic algorithms. The novelty of this evolutionary approach lies in automatic obtaining of suboptimal set of control parameters which differs from standard manual adjustment presented in (Hu & Yang, 2001; Oriolo et al., 2002). The considered motion control system of the mobile robot has two levels. The lower level subsystem deals with the

Evolutionary Design of Fuzzy Logic Based Position Controller for Mobile Robot

This paper develops a fuzzy logic based position controller whose membership functions are tuned by genetic algorithm. The main goal is to ensure successful velocity and position trajectories tracking between the mobile robot and the virtual reference cart. The proposed fuzzy controller has two inputs and two outputs. The first input represents the distance between the mobile robot and the reference cart. The second input is the angle formed by the straight line defined with the orientation of the robot, and the straight line that connects the robot with the reference cart. The outputs represent linear and angular velocity commands, respectively. The performance of the fuzzy controller is validated through comparison with previously developed mobile robot position controller based on control Lyapunov functions (CLF). Simulation results indicate good performance of position tracking while at the same time a substantial reduction of the control torques is achieved.

Safety-Oriented Control of Robotic Manipulators – a Kinematic Approach

Abstract In this paper, a control strategy for improving safety in human-robot interaction is described. The approach is based on the concept of kinetostatic danger field—a safety assessment recently proposed in the literature. A method for mapping the danger field information directly into position/velocity commands, thus bypassing the dynamics of the manipulator, is presented. Such an approach is suitable for industrial manipulators that usually require decentralized position control. Moreover, decoupling of task and posture behavior enables safety enhancement without compromising the task. The proposed control strategy is validated within a simulation study.

Ectropy of diversity measures for populations in Euclidean space

Measures to evaluate the diversity of a set of points (population) in Euclidean space play an important role in a variety of areas of science and engineering. Well-known measures are often used without a clear insight into their quality and many of them do not appropriately penalize populations with a few distant groups of collocated or closely located points. To the best of our knowledge, there is a lack of rigorous criteria to compare diversity measures and help select an appropriate one. In this work we define a mathematical notion of ectropy for classifying diversity measures in terms of the extent to which they tend to penalize point collocation, we investigate the advantages and disadvantages of several known measures and we propose some novel ones that exhibit a good ectropic behavior. In particular, we introduce a quasi-entropy measure based on a geometric covering problem, three measures based on discrepancy from uniform distribution and one based on Euclidean minimum spanning trees. All considered measures are tested and compared on a large set of random and structured populations. Special attention is also devoted to the complexity of computing the measures. Most of the novel measures compare favorably with the classical ones in terms of ectropy. The measure based on Euclidean minimum spanning trees turns out to be the most promising one in terms of the tradeoff between the ectropic behavior and the computational complexity.