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Positioning Improvement of Sensors in Wireless Sensor Networks
Published in Huynh Thi Thanh Binh, Nilanjan Dey, Soft Computing in Wireless Sensor Networks, 2018
N. Pushpalatha, K. Ramani, B. Anuradha
Recently, there has been some research on the error characteristics of sensor localization [33,34] and computation complexity [33]. Chintalapudi et al. [35,36] analyzed factors that impacted the performance of the system and then proposed ad hoc localization systems with ranging information [37]. Eren applied graph rigidity theory to locate sensors. Range constraints [36] and area constraints are used to locate sensors in coarse granularity as well.
Swarm fixed-time reference tracking: a discrete model
Published in International Journal of Control, 2023
Giuseppe Fedele, Luigi D'Alfonso, Antonio Bono
To this aim, we have chosen the method proposed in Gazi and Passino (2011) since it is designed for pure discrete-time systems and solves the shape control using the graph rigidity theory which is a very common tool in this context (Mesbahi & Egerstedt, 2010). Such a method is iterative: at each step agents utilise the new position information of other agents to compute ad-hoc potential functions that drive the swarm to form the desired geometrical pattern while avoiding collisions. The ability to assume any desired shape defined by agents inter-distances, however, comes with practical drawbacks that can be decisive in the robotics context. After a transient phase, whose duration cannot be fixed a priori, unlike the proposed strategy, the agents form the desired shape with an attitude that depends on their initial positions. In a realistic scenario where robots/vehicles move in a non-free space (roads, hallways, etc.) maintaining a desired predefined attitude is actually crucial. Thanks to the coordinates-coupling matrix M, our model, instead, allows the user define this important property of the flock/swarm. The following simulations give an example of such a difference.