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This paper presents a motion-planning and control scheme for a cooperative transportation system comprising a single rigid object and multiple autonomous non-holonomic mobile robots. A leader-follower formation control strategy is used for the transportation system in which the object is assumed to be the virtual leader; the robots carrying the object are considered to be followers. A smooth trajectory between the current and desired locations of the object is generated considering the constraints of the virtual leader. In the leader-follower approach, the origin of the coordinate system attached to the centre of gravity of the object, which is known as the virtual leader, moves along the generated trajectory while the real robots, which are known as followers, maintain a desired distance and orientation in relation to the leader. An asymptotically stable tracking controller is used for trajectory tracking. The proposed approach is verified by simulations and real applications using Pioneer P3-DX mobile robots.

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.

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In this work, a novel graphical and mathematical model is introduced for Timed-Arc Petri Nets. In this model, operation durations related to events are associated with arcs as firing durations. Time elements are introduced on the arcs of the model for monitoring tokens in operation durations (firing durations) of firing processes (events). In the model, the status of the system is represented as states consisting of remaining time vector for indicating the status of time elements and marking vector for indicating the status of places' state information. This feature of states allows to obtain reachability set enhanced with the time information and the timed reachability graph of the system. In this work, behavioral properties of the proposed model are also defined. Moreover, via considering real world systems, behavioral properties are analyzed and performance of the model is compared with Stretched Petri Nets which is a recently proposed type of Timed Petri Nets.

In this study, a fire-fighting scenario in an office environment wherein three different nonholonomic differential-drive mobile robots are used is considered as a case study. The 2D configuration space of the office environment is divided into grid cells by using the method of "Occupancy Grid Map" such that each grid cell is associated with each interrelated node. Each robot constructs a reachability three by using these nodes and Breadth-First Search (BFS) algorithm. The back-tracking algorithm is used to obtain the finite solution set of paths from the motion planning. The set of alternatives is constructed by randomly selecting routes from the finite solution set of paths. Each robot determines its own best route by applying Multi-Criteria Decision Making (MCDM) methods such that "Elimination et Choix Traduisant la Realite (ELECTRE I)" and "Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)". Criteria for the path selection is weighted by applying the method of "Analytic Hierarchy Process (AHP)". Then, each robot except the leader robot sends its best path-info to the leader so that the leader robot determines the most suitable robot that conforms to the fire-fighting task by using AHP. To analyze the effect of criteria's weights on the alternatives and perform sensitivity-graphs, Expert Choice 11 software is used. The robot determined by the leader executes the task by tracking its own best path.