000151045 001__ 151045
000151045 005__ 20251017144630.0
000151045 0247_ $$2doi$$a10.28991/ESJ-2022-06-03-01
000151045 0248_ $$2sideral$$a128738
000151045 037__ $$aART-2022-128738
000151045 041__ $$aeng
000151045 100__ $$aAndaluz, Gabriela M.
000151045 245__ $$aHybrid Controller based on Null Space and Consensus Algorithms for Mobile Robot Formation
000151045 260__ $$c2022
000151045 5060_ $$aAccess copy available to the general public$$fUnrestricted
000151045 5203_ $$aThis work presents a novel hybrid control approach based on null space and consensus algorithms to solve the scalability problems of mobile robot formation and improve leader control through multiple control objectives. In previous works, the training of robots based on the null space requires a rigid training structure based on a geometric shape, which increases the number of agents in the formation. The scheme of the control algorithm, which does not make formation scalability possible, must be changed; therefore, seeking the scalability of training based on null space is a challenge that could be solved with the inclusion of consensus algorithms, which allow the control structure to be maintained despite increasing or decreasing the number of robot followers. Another advantage of this proposal is that the formation of the followers does not depend on any geometric figure compared to previous works based on the null space; this new proposal does not present singularities as if the structure is based on geometric shape, the latter one is crucial since the formation of agents can take forms that cannot be achieved with a geometric structure, such as collinear locations, that can occur in many environments. The proposed hybrid control approach presents three tasks: i) leader position task, ii) leader shape task, and iii) follower formation task. The proposed algorithm is validated through simulations, performing tests that use the kinematic model of non-holonomic mobile robots. In addition, linear algebra and Lyapunov theory are used to analyze the stability of the method.
000151045 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000151045 592__ $$a0.486$$b2022
000151045 593__ $$aMultidisciplinary$$c2022$$dQ1
000151045 594__ $$a4.9$$b2022
000151045 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000151045 700__ $$aLeica, Paulo
000151045 700__ $$aHerrera, Marco
000151045 700__ $$aMorales, Luis
000151045 700__ $$aCamacho, Oscar
000151045 773__ $$g6, 3 (2022), 429-447$$pEmerg. sci. j.$$tEmerging science journal$$x2610-9182
000151045 8564_ $$s1625528$$uhttps://zaguan.unizar.es/record/151045/files/texto_completo.pdf$$yVersión publicada
000151045 8564_ $$s2067793$$uhttps://zaguan.unizar.es/record/151045/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000151045 909CO $$ooai:zaguan.unizar.es:151045$$particulos$$pdriver
000151045 951__ $$a2025-10-17-14:26:06
000151045 980__ $$aARTICLE