This paper presents an analysis under a formal methods framework for multi-agent systems applications in manufacturing logistics optimization. More specifically, a case study for workflow abstraction for manufacturing logistics optimization is presented utilizing the SPECTER task planner framework for a food service company. A workflow abstraction is constructed considering the workflow stages, the temporal costs (i.e. a machine operation, products time construction, worker transitions between work-cells) and the agents involved in the production line (such as robots, machines, humans, materials, products etc.). Based on the derived abstraction, different case studies are investigated and sub-optimal solutions are provided. The paper leverages the modeling power of the SPECTER framework, while demonstrating its potential applications in providing solutions for the food service industry.

This paper presents a solution to the automatic task planning problem for multi-agent systems. A formal frame-work is developed based on Nondeterministic Finite Automata with ∊-transitions, where given the capabilities, constraints and failure modes of the agents involved, any initial state of the system and a task specification, an optimal solution is generated that satisfies the system constraints and the task specification. The resulting solution is guaranteed to be complete and optimal; moreover a heuristic solution that offers significant reduction of the computational requirements while relaxing the completeness and optimality requirements is proposed. The constructed system model is independent from the initial conditions and the task specifications, eliminating the need to repeat the costly pre-processing cycle, while allowing the incorporation of failure modes on-the-fly. A case study is provided to demonstrate the effectiveness and validity of the methodology.

This paper presents a novel system for automatic early detection of wild forest-fire using optical and thermal cameras at ground station and mounted on Unmanned Aerial Systems (UAS). The proposed system can detect and identify forest fires threats in real-time, and at the same time it is capable to notify the interested parties and authorities by providing alerts and important information (e.g. specific location, environmental conditions, etc.). Early recognition and detection of forest fires is a very challenging problem. Numerous potential sources of error leads to an increased rate of false positives (false alarms). The proposed system addresses them by exploiting the complementarities between thermal and optical cameras located at a panoramic static ground location together with the same type of cameras mounted onboard a (or potentially more) UAS. Also, the system is equipped with sensors to monitor and take into account the environmental conditions for the on the fly threat assessment. All data are fused together for an automated risk assessment. The proposed system has been integrated and it is ready for experimental testing and validation in close-to-operational conditions in field fire experiments with controlled safety conditions carry out in Pano Platres forest in Cyprus.

This work introduces a novel approach to the solution of the navigation problem by mapping an obstacle-cluttered environment to a trivial domain called the point world, where the navigation task is reduced to connecting the images of the initial and destination configurations by a straight line. Due to this effect, the underlying transformation is termed the “navigation transformation.” The properties of the navigation transformation are studied in this work as well as its capability to provide-through the proposed feedback controller designs-solutions to the motion- and path-planning problems. Notably, the proposed approach enables the construction of temporal stabilization controllers as detailed herein, which provide a time abstraction to the navigation problem. The proposed solutions are correct by construction and, given a diffeomorphism from the workspace to a sphere world, tuning free. A candidate construction for the navigation transformation on sphere worlds is proposed. The provided theoretical results are backed by analytical proofs. The efficiency, robustness, and applicability of the proposed solutions are supported by a series of experimental case studies.

In this paper we described the development of both the hardware and the algorithms for a novel laser vision system suitable for measuring distances from both solid and mesh-like targets in underwater environments. The system was developed as a part of the AQUABOT project that developed an underwater robotic system for autonomous inspection of offshore aquaculture installation. The system takes into account the hemispherical optics typical in underwater vehicle designs and implements an array of line-lasers to ensure that mesh-like targets provide reflections in a consistent manner. The developed algorithms for the laser vision system are capable of providing either raw pointcloud data sets from each laser or with additional processing high level information like distance and relative orientation of the target with respect to the ROV can be recovered. An automatic calibration procedure along with the accompanying hardware that was developed, is described in this paper, to reduce the calibration overhead required by regular maintenance operations as is typical for underwater vehicles operating in sea-water. A set of experimental results in controlled laboratory environment as well as at offshore aquaculture installations demonstrate the performance of the system.

This paper presents a visual servoing control scheme that is applied to an underwater robotic vehicle. The objective of the proposed control methodology is to provide a human operator the capability to move the vehicle without loosing the target from the vision system's field of view. On-line estimation of the vehicle states is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU) using an asynchronous Unscented Kalman Filter (UKF). A controller designed at the kinematic level, is backstepped into the dynamics of the system, maintaining its analytical stability guarantees. It is shown that the under-actuated degree of freedom is input-to-state stable and an energy based shaping of the user input with stability guarantees is implemented. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using a small Remotely Operated Vehicle (ROV) in a test tank.

This paper presents a novel modification of the Dual Unscented Kalman Filter (DUKF) for the on-line concurrent state and parameter estimation. The developed algorithm is successfully applied to an under-actuated underwater vehicle. Like in the case of conventional DUKF the proposed algorithm demonstrates quick convergence of the parameter vector. In addition, experimental results indicate an increased performance when the proposed methodology is utilized. The applicability and performance of the proposed algorithm is experimentally verified by combining the proposed DUKF with a non-linear controller on a modified Videoray ROV in a test tank. The on-line estimation of the vehicle states and dynamic parameters is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU).

In this paper we present a multi-agent coordination algorithm suitable for systems with aircraft-like kinematic constraints. A model of a system of input-constrained non-holonomic agents is constructed, suitable for use with formal verification tools. The agents considered are uniform and have bounded velocities and limited turning capabilities. We demonstrate how a model checker can be used to generate a counterexample trace for such a system, usable as a trajectory that satisfies our safety and liveness requirements.

In this paper, we introduce the concept of a novel robotic module, the ldquoR-Cellrdquo. R-Cell can be utilized in constructing distributed, homogeneous robotic systems. Each R-Cell is a rectangle endowed with: (a) motion capabilities provided by four revolutionary joints, each equipped with a clamping mechanism, and (b) deformation capabilities realized by four prismatic joints. The proposed module provides the resulting modular robotic structures with dexterous deformation and force creating capabilities. The resulting robotic structures are not defacto rigid, but can change shape & form even without any cell-reconfiguration. Our concept could be useful for a great variety of applications encompassing modular robotics like self-assembly, self-repair and reactive shape optimization just to mention a few, that conventional robots cannot accommodate.

In this paper, we propose a methodology for implementing multirobot navigation-function-based controllers to mixed teams of holonomic and nonholonomic agents. A new nonsmooth backstepping controller is introduced for translating kinematic controllers to equivalent dynamic ones, while maintaining bounded velocity specifications. The derived backstepping controller is applied to a dynamic model of the mobile robots, yielding a globally asymptotically stable dynamic controller. The effectiveness of the methodology is verified through nontrivial computer simulations.

We develop decentralized controllers for a team of disk-shaped robots to converge to and circulate along the boundary of a desired two-dimensional geometric pattern specified by a smooth function with collision avoidance. The proposed feedback controllers rely solely on each robot's range and bearing sensors which allow them to obtain information about positions of neighbors within a given range. This is relevant for applications such as perimeter surveillance or containing hazardous regions where limited bandwidth must be preserved for situational awareness. The computational complexity of the decentralized controller for each agent is linear in the number of neighboring agents, making it scalable to robot swarms. We establish stability and convergence properties of the controllers and verify the feasibility of the method through computer simulations.

While there is extensive work on motion planning and control for navigation tasks with guarantees, there is no systematic way for human operators to modify the resulting plans without losing the guarantees. In this paper we propose a systematic way of composing behaviors resulting from human inputs with behaviors derived from navigation functions. The proposed controller is based on a new class of navigation function based controllers that possess weak input-to-state stability properties. The resulting system has analytically guaranteed safety and convergence properties. The feasibility of the proposed methodology is demonstrated through simulation examples and hardware experiments

In this paper we present a methodology to drive the end effector of a robotic manipulator across the surface of an object in the workspace, and at the same time the manipulator can apply a force to the object, through its end-effector. Three typical tasks are considered, namely stabilization of the end effector over the object's surface and applying a specific force on it, motion planning and eventually trajectory tracking of the end effector across the object's surface. The proposed controllers utilize navigation functions and are based on the belt zone vector fields concept. The derived dynamic controllers are realized using an integrator backstepping methodology. The derived feedback based controllers guarantee global convergence and collision avoidance. The closed form solution provides fast feedback rendering the methodology particularly suitable for implementation on real time systems. The properties of the proposed methodology are verified through non-trivial computer simulations.

In this paper we present a methodology that drives and stabilizes a robotic agent moving in a three dimensional environment, to a 2-dimensional manifold embedded in the workspace. Once the agent reaches the manifold, depending on the application, it performs a motion planning or a trajectory tracking task. Appropriately constructed belt-zone vector fields guarantee that the agent will not depart the 2-D manifold proximity area, while carrying out the motion planning or trajectory tracking task. The derived closed form feedback control law guarantees global convergence and collision avoidance. The properties of the proposed algorithm are verified through non-trivial computer simulations.

In this paper we consider the problem of navigating a team of input constraint robotic agents. The main motivation comes from the field of micro-robotics, where several input sets are more favorable than others, in the sense of incurring a lower relative actuation error. A switching controller that actuates the system in those favored input regions is derived based on Multirobot Navigation Functions. The system is shown to possess theoretically guaranteed global convergence and collision avoidance properties. Non-trivial computer simulations show the effectiveness of the methodology.

This paper represents an extension of our previous work [D. Dimarogonas, et al. (2003), S. Loizou, et al. (2003)] on multiagent navigation to the case of decentralized control of multiple nonholonomic vehicles. Our main motivation comes from the field of air traffic management systems and from the field of micro robotic multiagent systems. A discontinuous feedback control scheme, based on dipolar navigation fields, is implemented and integrator backstepping is applied to suppress chattering behavior. The methodology has guaranteed global convergence and collision avoidance properties, which are verified by nontrivial computer simulations.

In this paper we incorporate dipolar potential fields used for nonholonomic navigation into a novel potential function designed for multi-robot navigation. The derived navigation function is suitable for navigation of multiple nonholonomic vehicles. A properly designed discontinuous feedback control law is applied to steer the nonholonomic vehicles. The derived closed form control scheme provides robust navigation with guaranteed collision avoidance and global convergence properties, as well as fast feedback, rendering the methodology particularly suitable for real time implementation. Collision avoidance and global convergence properties are verified through non-trivial computer simulations.

We apply a novel motion planning and control methodology, which is based on a non-smooth navigation function, to a point mobile robot moving amongst moving obstacles. The chattering introduced by the discontinuous potential field is suppressed using non-smooth backstepping. The combined controller guarantees global asymptotic convergence and collision avoidance. This controller is particularly suitable for real time implementation on systems with limited computational resources. The effectiveness of the proposed scheme is verified through computer simulations.

This paper presents the first motion planning methodology applicable to articulated, nonpoint nonholonomic robots with guaranteed collision avoidance and convergence properties. It is based on a new class of nonsmooth Lyapunov functions and a novel extension of the navigation function method to account for nonpoint articulated robots. The dipolar inverse Lyapunov functions introduced are appropriate for nonholonomic control and offer superior performance characteristics compared to existing tools. The new potential field technique uses diffeomorphic transformations and exploits the resulting point-world topology. The combined approach is applied to the problem of handling deformable material by multiple nonholonomic mobile manipulators in an obstacle environment to yield a centralized coordinating control law. Simulation results verify asymptotic convergence of the robots, obstacle avoidance, boundedness of object deformations, and singularity avoidance for the manipulators.

We extend the navigation function methodology, established for single robot navigation, to the case of multiple robots. Appropriate expressions for the robot potential functions guarantee global convergence. The derived closed form navigation function provides a robust navigation scheme, suitable for real time implementation. The collision avoidance and global convergence properties are verified through simulations.

This paper presents a motion planner and nonholonomic controller for a mobile robot, with global collision avoidance and convergence properties. An appropriately designed (dipolar) potential field is combined with discontinuous state feedback. A new class of Lyapunov functions is introduced and used for nonholonomic navigation. The obstacle avoidance and global asymptotic stability properties are verified through simulations.