Development of Modular Compliant Anthropomorphic Robot Hand

The chapter presents the development of a modular compliant robotic hand characterized by the anthropomorphic structure and functionality. The prototype is made based on experience in development of contemporary advanced artificial hands and taking into account the complementary aspects of human bio-mechanics. The robot hand developed in the Institute Mihailo Pupin is called “Pupin hand”. The Pupin hand is developed for research purposes as well as for implementation with service and medical robot devices as an advance robot end-effector. Mechanical design, system identification, modeling and simulation and acquisition of the biological skill of grasping adopted from humans are considered in the chapter. Mechanical structure of the tendon-driven, multi-finger, 23 degrees of freedom compliant robot hand is presented in the chapter. Model of the hand is represented by corresponding multi-body rigid system with the complementary structural elasticity inserted between the particular finger modules. Some characteristic simulation results are given in the chapter in order to validate the chosen design concept. For the purpose of motion capture of human grasping skill, an appropriate experimental setup is prepared. It includes an infrared Kinect camera that combines visual and depth information about objects from the environment. The aim of using the Kinect sensor is to acquire human grasping skill and to map this natural motion to the robotic device. The novelties of the robot hand prototyping beyond to the state-of-the-art are stressed out in the conclusion

Upregulation of Heme Oxygenase-1 in Response to Wild Thyme Treatment Protects against Hypertension and Oxidative Stress

High blood pressure is the most powerful contributor to the cardiovascular morbidity and mortality, and inverse correlation between consumption of polyphenol-rich foods or beverages and incidence of cardiovascular diseases gains more importance. Reactive oxygen species plays an important role in the development of hypertension. We found that wild thyme (a spice plant, rich in polyphenolic compounds) induced a significant decrease of blood pressure and vascular resistance in hypertensive rats. The inverse correlation between vascular resistance and plasma heme oxygenase-1 suggests that endogenous vasodilator carbon monoxide generated by heme oxidation could account for this normalization of blood pressure. Next product of heme oxidation, bilirubin (a chain-breaking antioxidant that acts as a lipid peroxyl radical scavenger), becomes significantly increased after wild thyme treatment and induces the reduction of plasma lipid peroxidation in hypertensive, but not in normotensive rats. The obtained results promote wild thyme as useful supplement for cardiovascular interventions

Multi-Agent Mission Planning

Multi-Agent Systems (MASs) have been utilized in various settings and frameworks, and have thus been successfully applied in many applications to achieve different goals. It has been shown that MASs are more cost-effective as compared to building a single agent with all the capabilities a mission may require. Moreover, the cost is not the only driving factor for the adoption of MASs, e.g., safety is another important aspect: Deploying a group of agents, in a harsh or extreme environment, instead of a human team decreases the safety risks. Furthermore, MASs offer more flexibility and robustness when compared to a single-agent solution. The flexibility comes from dividing resources into separate groups, while robustness comes from the fact that a critical error in one agent does not necessarily endanger the success of a mission. Note that a mission may have many different constraints and aspects, however, the most trivial case has a single agent and a single task.  These kinds of missions can be planned by a human operator, overseeing a mission, without the need for an automated planner. On the other hand, more complex missions, that are utilizing a large number of heterogeneous agents and tasks, as well as constraints (precedence, synchronization, etc.) are not that trivial to plan for a human operator. These complex problems pose a great challenge to making a feasible plan, let alone the best possible one. Moreover, the increase in the power of available computing platforms in robotic systems has allowed the utilization of parallel task execution. More specifically, it allowed for possible parallelism in sensing, computation, motion, and manipulation tasks. This in turn had the benefit of allowing the creation of more complex robotic missions. However, it came at the cost of increased complexity for the optimization of the task allocation problem. To circumvent these issues, an automated planner is necessary. These types of problems are notoriously difficult to solve, and it may take too long for an optimal plan to be found. Therefore, a balance between optimality and computation time taken to produce a plan become very important. This thesis deals with the formal definition of two particular Multi-Robot Task Allocation (MRTA) problem configurations used to represent multi-agent mission planning problems. More specifically, the contribution of this thesis can be grouped into three categories.  Firstly, this work proposes a model to represent different problem configurations, also referred to as missions, in a structured way. This model is called TAMER, and it also allows the addition of new dimensions in a more systematic way, expanding the number of problems that can be described compared to previously proposed MRTA taxonomies. Secondly, this thesis defines and provides two different problem formulations, in a form of Mixed-Integer Linear Problem formulation, of the Extended Colored Travelling Salesman Problem (ECTSP). These models are implemented and verified in the CPLEX optimization tool on the selected problem instances. In addition, a sub-optimal approach to solving these complex problems is devised. Proposed solutions are based on the Genetic Algorithm (GA) approach, and they are compared to the solutions obtained by state-of-the-art (and state-of-practice) solvers, i.e., CPLEX. The advantage of using GA for planning over classical approaches is that it has better scalability that enables it to find solutions for large-scale problems. Although those solutions are, in the majority of cases, sub-optimal they are obtained much faster than with other exact methods. Another advantage is represented in a form of "anytime stop" option. In time-critical operations, it is important to have the option to stop the planning process and use the sub-optimal solution when it is required.  Lastly, this work addresses the one dimension of the MRTA problem that has not caught much of the research attention in the past. In particular, problem configurations including Multi-Task (MT) robots have been neglected. To overcome the aforementioned problem, first, the cases in which task parallelism may be achieved have been defined. In addition, the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution has been introduced. Two models have been proposed and compared. The first one is expressed as ILP and implemented in the CPLEX optimization tool. The other one is defined as a Constraint Programming (CP) model and implemented in CP optimization tools. Both solvers have been evaluated on a series of problem instances

Contribution to the development of intelligent behavior of mobile robot in predefined technological environment using decision making methods based on ANN

U ovom radu prikazan je jedan od mogućih načina zamene konvencionalnog načina za obavljanje unutrašnjeg transporta u tehnološkim okruženjima. Novo, poboljšano rešenje uključuje korišćenje inteligentnog mobilnog robota za transport materijala. Ovo je ostvareno implementacijom modela kretanja i primenom algoritmom za planiranje i odabir optimalne putanje. Epitet 'inteligentni' zaslužen je korišćenjem veštačkih neuronskih mreža (VNM) u domenu prepoznavanja postojanja prepreka i donošenju odluka o načinu njihovog uspešnog izbegavanja. Eksperimentalni rezultati, izvedeni na Khepera II mobilnom robotu, opravdali su očekivanja i pokazali da ovakvo rešenje ima praktičnu upotrebu u današnjim tehnološkim okruženjima. .This paper shows one of possible approaches in replacing conventional way of inner transport in technological environments. New, improved solution includes the use of intelligent mobile robot for transport of materials. This is achieved by implementing movement model and using algorithms for planning and selection of optimal trajectory. Epithet 'intelligent' is deserved for using artificial neural networks (ANN) for recognition of existing obstacles and making a decision about their successful avoidance. Experimental results, conducted on Khepera II mobile robot, have fulfilled expectations and shown that this kind of solution has practical use in today's modern technological environments.

Contribution to the development of intelligent behavior of mobile robot in predefined technological environment using decision making methods based on ANN

U ovom radu prikazan je jedan od mogućih načina zamene konvencionalnog načina za obavljanje unutrašnjeg transporta u tehnološkim okruženjima. Novo, poboljšano rešenje uključuje korišćenje inteligentnog mobilnog robota za transport materijala. Ovo je ostvareno implementacijom modela kretanja i primenom algoritmom za planiranje i odabir optimalne putanje. Epitet 'inteligentni' zaslužen je korišćenjem veštačkih neuronskih mreža (VNM) u domenu prepoznavanja postojanja prepreka i donošenju odluka o načinu njihovog uspešnog izbegavanja. Eksperimentalni rezultati, izvedeni na Khepera II mobilnom robotu, opravdali su očekivanja i pokazali da ovakvo rešenje ima praktičnu upotrebu u današnjim tehnološkim okruženjima. .This paper shows one of possible approaches in replacing conventional way of inner transport in technological environments. New, improved solution includes the use of intelligent mobile robot for transport of materials. This is achieved by implementing movement model and using algorithms for planning and selection of optimal trajectory. Epithet 'intelligent' is deserved for using artificial neural networks (ANN) for recognition of existing obstacles and making a decision about their successful avoidance. Experimental results, conducted on Khepera II mobile robot, have fulfilled expectations and shown that this kind of solution has practical use in today's modern technological environments.

Optimizing Parallel Task Execution for Multi-Agent Mission Planning

Multi-Agent Systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates numerous problem dimensions, and it aims at providing formulations and solutions to various problem configurations, i.e., complex multi-robot missions. One dimension of the MRTA problem has not caught much of the research attention. In particular, problem configurations including Multi-Task (MT) robots have been neglected. However, the increase in computational power, in robotic systems, has allowed the utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex robotic missions; however, it came at the cost of increased problem complexity.  To overcome the aforementioned problem, we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution. To fill in the gap in the literature related to MT robot problem configurations, we provide a formalization of the mission planning problem, using MT robots, in the form of Integer Linear Programming and Constraint Programming (CP), to minimize the mission makespan. The models are validated in CPLEX and CP Optimizer on the set of benchmarks. Moreover, we provide a comprehensive performance analysis of both solvers, exploring their scalability and solution quality

Optimizing Parallel Task Execution for Multi-Agent Mission Planning

Multi-Agent Systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates numerous problem dimensions, and it aims at providing formulations and solutions to various problem configurations, i.e., complex multi-robot missions. One dimension of the MRTA problem has not caught much of the research attention. In particular, problem configurations including Multi-Task (MT) robots have been neglected. However, the increase in computational power, in robotic systems, has allowed the utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex robotic missions; however, it came at the cost of increased problem complexity.  To overcome the aforementioned problem, we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution. To fill in the gap in the literature related to MT robot problem configurations, we provide a formalization of the mission planning problem, using MT robots, in the form of Integer Linear Programming and Constraint Programming (CP), to minimize the mission makespan. The models are validated in CPLEX and CP Optimizer on the set of benchmarks. Moreover, we provide a comprehensive performance analysis of both solvers, exploring their scalability and solution quality

GLocal : A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem

Multi-robot systems can be prone to failures during plan execution, depending on the harshness of the environment they are deployed in. As a consequence, initially devised plans may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. Two main approaches emerge as possible solutions, a global re-planning technique using a centralized planner that will redo the task allocation with the updated world state information, or a decentralized approach that will focus on the local plan reparation, i.e., the re-allocation of those tasks initially assigned to the failed robots.The former approach produces an overall better solution, while the latter is less computationally expensive.The goal of this paper is to exploit the benefits of both approaches, while minimizing their drawbacks. To this end, we propose a hybrid approach {that combines a centralized planner with decentralized multi-agent planning}. In case of an agent failure, the local plan reparation algorithm tries to repair the plan through agent negotiation. If it fails to re-allocate all of the pending tasks, the global re-planning algorithm is invoked, which re-allocates all unfinished tasks from all agents.The hybrid approach was compared to planner approach, and it was shown that it improves on the makespan of a mission in presence of different numbers of failures,as a consequence of the local plan reparation algorithm