This paper proposes a novel strategy for Leader-Follower formation control based on NMPC using visual information for wheeled mobile robots. The formation task is to follow a visual path whose control problem is solved in the image plane for the leader robot. With the proposed strategy it is possible to increase the range of real applications through the use of arbitrary visual paths in conjunction with already consolidated Leader-Follower approach. Time-varying formations are dealt with a classical method to stability guarantee and a new approach to feasibility guarantee. Experimental results with a nonholonomic platform show the performance of the proposed control scheme.

In this paper, a Propositional Temporal Logic (PTL) inference engine is implemented in the cognitive level of a Concurrent Autonomous Agent (CAA). The hardware architecture of the system is an embedded microcontrollers network designed to fit the concurrency requirements of the cognitive model of the aforementioned agent. The architecture of the agent comprises three levels, namely, the reactive level, instinctive level and the cognitive level, which runs concurrently. The reactive level is embedded in a PSoC 5LP, consisting of behaviours created over a embedded kinematic controller. The instinctive level runs in an ARM mbed, which receives perceptions from and sends the active behaviour to the reactive level through a CAN bus. This level uses a Knowledge Based System (KBS) with First Order Logic (FOL) as automatic reasoning mechanism. The cognitive level runs an implementation of the METATEM algorithm on a DNP/2486 which, in turn, receives symbolic information from the instinctive level to update its logical world model. It also sends new local goals to instinctive level through an Ethernet network. Finally, the architecture used in the experiments is presented, followed by the results.

Maximizing the performance of cooperative perception of a tracked target by a team of mobile robots while maintaining the team's formation is the core problem addressed in this work. We propose a solution by integrating the controller and the estimator modules in a formation control loop. The controller module is a distributed non-linear model predictive controller and the estimator module is based on a particle filter for cooperative target tracking. A formal description of the integration followed by simulation and real robot results on two different teams of homogeneous robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target's cooperative estimate while complying with the performance criteria such as keeping a pre-set distance between the team-mates and/or the target and obstacle avoidance.