Flocking for multi-robot systems via the Null-Space-based Behavioral control

Abstract

In this paper the flocking problem for a multi-robot system, consisting in making the robots of a team grouping together, is addressed. The flocking is achieved resorting to the Null-Space-based Behavioral (NSB) control by defining very simple behaviors for each robot of the team and by properly arranging these behaviors in priority. The NSB algorithm, making the robots using only local information, successfully achieves the flocking with or without a rendezvous point and in eventual presence of obstacles. Extensive simulations and experiments using differential-drive mobile robots prove the effectiveness of the proposed algorithm.

Collision avoidance for multiple Lagrangian dynamical systems with gyroscopic forces

“…In fact, considering the definition of B ð; _ Þ given in equation (15), and considering the definition of bð _ x i Þ given in equation (17), it is possible to obtain the following equality…”

“…Considering equations (17) and (19), the amount of energy E d ðq i Þ that can be dissipated by the braking force can be computed as the absolute value of the integral of the dissipated power, namely Considering the fact that, as stated before, when the j th obstacle is active, v i; j ¼ _ s i; j ! 0, we can conclude that…”

“…Several works on the use of gyroscopic forces for obstacle avoidance can be found in the literature, [12][13][14][15][16][17] typically for mobile robots moving on a two-dimensional environment (i.e. the ground floor).…”

Exploring the Split and Join Capabilities of a Robotic Collective Movement Framework

“…The main disadvantage of this type of architecture is that it does not scale well with an increasing number of robots since the position error propagates cumulatively from leader to follower. In other solutions no leaders are designated and robots react to the positions of their neighbouring robots, while moving in an emergent direction (Antonelli et al, 2008;Spears et al, 2004). These last solutions usually need global information according to the desired direction of movement, which makes them not scalable.…”

“…PF does not allow splitting into sub-groups, and potential joining is much harder than in our proposed solution thanks to the way neighbours are chosen. Antonelli et al (2008) propose a method that also creates collective movement placing robots in a hexagonal lattice but it also needs a virtual goal which limits the scalability and application on real robots. The control of real robots if performed off-board, using a camera that tracks the positions of the robots, and sending commands to the motors.…”