Metin Ozkan

Adaptive control of free-floating space manipulators using dynamically equivalent manipulator model

Abstract In this paper, adaptive control of free-floating space manipulators is considered. The dynamics based on the momentum conservation law for the free-floating space manipulator has non-linear parameterization properties. Therefore, the adaptive control based on a linear parameterization model cannot be used in this dynamics. In this paper, the dynamics of the free-floating space manipulator system are derived using the Dynamically Equivalent Model (DEM) approach. The DEM is a fixed-base manipulator system and allows us to linearly parameterize the dynamic equations. Using this linearly parameterized dynamic equation, an adaptive control method is developed to control the system in joint space. Parameter identification and torque calculations are done using the DEM dynamics. Simulations show that the tracking errors of the manipulator joints to a given desired trajectory become zero when the calculated torques act on the joints of the space manipulator system.

A pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time

Sensor-based multi-robot coverage path planning problem is one of the challenging problems in managing flexible, computer-integrated, intelligent manufacturing systems. A novel pattern-based genetic algorithm is proposed for this problem. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a sequence of the disks for each robot to minimize the coverage completion time determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Robot turns cause the robot to slow down, turn and accelerate inevitably. Therefore, the actual travel time of a mobile robot is calculated based on the traveled distance and the number of turns. The algorithm is designed to handle routing and partitioning concurrently. Experiments are conducted using P3-DX mobile robots in the laboratory and simulation environment to validate the results.

Adaptive control of free-floating space robots in Cartesian coordinates

An adaptive control scheme is proposed for the end-effector trajectory tracking control of free-floating space robots. In order to cope with the nonlinear parameterization problem of the dynamic model of the free-floating space robot system, the system is modeled as an extended robot which is composed of a pseudo-arm representing the base motions and a real robot arm. An on-line estimation of the unknown parameters along with a computed-torque controller is used to track the desired trajectory. The proposed control scheme does not require measurement of the accelerations of the base and the real robot arm. A two-link planar space robot system is simulated to illustrate the validity and effectiveness of the proposed control scheme.

Formation-based cooperative transportation by a group of non-holonomic mobile robots

In this study, motion planning and control scheme for a cooperative transportation system, which consists of a single object and multiple autonomous non-holonomic mobile robots, is proposed. Virtual leader-follower formation control strategy is used for the cooperative transportation system. The object is assumed as the virtual leader of the system and the robots carrying the object are considered as follower robots. A smooth path is generated by considering the constraints of the virtual robot. The origin of the coordinate system attached to the center of gravity of the object tracks the generated path. A path for each follower robot is generated to keep the formation structure. The follower robots track their paths. A communication framework is used for the messaging between robots, and asymptotically stable tracking control is used for trajectory tracking. The proposed method is verified with real applications and simulations using Pioneer P3-DX mobile robots and a single object.

Pattern-Based Genetic Algorithm Approach to Coverage Path Planning for Mobile Robots

Sensor-based mobile robot coverage path planning (MRCPP) problem is a challenging problem in robotic management. We here develop a genetic algorithm (GA) for MRCPP problems. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a path which runs through the center of each disk at least once with minimal cost of full coverage. The proposed GA utilizes prioritized neighborhood-disk information to generate practical and high-quality paths for the mobile robot. Prioritized movement patterns are designed to generate efficient rectilinear coverage paths with no narrow-angle turn; they enable GA to find optimal or near-optimal solutions. The results of GA are compared with a well-known approach called backtracking spiral algorithm (BSA). Experiments are also carried out using P3-DX mobile robots in the laboratory environment.

Project-oriented task scheduling for mobile robot team

This paper presents a project-oriented framework for multi-robot task scheduling. An agent-based architecture is designed to schedule tasks where robots are considered as resources. The study focused on the problems when the number of available robots is less than the required number. In this case, the problem becomes a resource-constrained scheduling problem. Initially, tasks are scheduled by using Critical path method (CPM), resource leveling method is used to smooth the deviation between the resource requirements and available resource levels, and tasks are allocated to robots. As robots perform their tasks, a monitoring agent observes them and tasks are rescheduled if the difference between the planned and actual completion time of tasks exceeds a predefined threshold. Effectiveness of the proposed approach is shown by using a nine-task two-robot project simulation.

Market-based task allocation by using assignment problem

In this study, a market-based task allocation method is proposed. In the trading process, the energy model of a robot platform is used to calculate the price and cost for a task. In order to determine the winner robot(s), two auction clearing algorithms, which are named as Iterative and Highest-Bid Task for Robots (HBTR), are proposed. Additionally, assignment problem is used to determine instantaneous optimal task-robot matching. The Hungarian algorithm is implemented to solve optimal assignment problem. In the implementation of the algorithms, three types of tasks are used: cleaning, carrying and monitoring. The tasks consist of three important features: delicacy, priority, and the task completion time. These tasks are assigned to the members of a heterogeneous robot team, according to the proposed task allocation method.

A Multi-Robot Control Architecture for Fault-Tolerant Sensor-Based Coverage

Sensor-based coverage problems have many applications such as patrolling, search-rescue, and surveillance. Using multi-robot team increases efficiency by reducing completion time of a sensor-based coverage task. Robustness to robot failures is another advantage of using multiple robots for coverage. Although there are many works to increase the efficiency of coverage methods, there are few works related to robot failures in the literature. In this paper, fault-tolerant control architecture is proposed for sensor-based coverage. Robot failures are detected using the heartbeat strategy. To show the effectiveness of the proposed approach, experiments are conducted using P3-DX mobile robots both in laboratory and simulation environment.

A genetic algorithm for task completion time minimization for multi-robot sensor-based coverage

Minimizing the coverage task time is important for many sensor-based coverage applications. The completion time of a sensor-based coverage task is determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Most of the coverage algorithms results in sharp turns which require the robot to slow down, turn and accelerate. So the actual travel time of a mobile robot is depending on the traveled distance and number of turns both. In this study, previously proposed hierarchical oriented genetic algorithm (HOGA) is extended to consider the travel time rather than just the traveled distances. The HOGA consists of two phases. In the first phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. Then, in the second phase, a directed genetic algorithm is used to partition the route among robots considering actual travel time costs. The algorithms are coded in C++ and simulations are conducted using P3-DX mobile robots in the MobileSim environment.

Hierarchical oriented genetic algorithms for coverage path planning of multi-robot teams with load balancing

Multi-robot coverage path planning problems require every point in a given area to be covered by at least one member of the robot team using their sensors. For a time-efficient coverage, the environment needs to be partitioned among robots in a balanced manner. So the problem can be modeled as task assignment problem with load balancing. In this study, we propose two oriented genetic algorithms working in a hierarchical manner to deal with this problem. In the first phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. In the following phase, a directed genetic algorithm is used to partition the route among robots considering load balancing. The algorithm is coded in C++, simulations and experiments are conducted using P3-DX mobile robots in the MobileSim environment. The hierarchical oriented genetic algorithm (HOGA) is also compared to the multi-robot spanning tree coverage (STC) approach in terms of load balancing. The comparison indicates competitive results over multi-robot STC.