Pattern formation for a fleet of AUVs based on optical sensor

Abstract

Abstract-During the past decades, multi-robot systems have been studied deeply and have demonstrated their advantages in conducting autonomous missions, particularly in underwater environment. When using multiple robots, high cost single sensor can be replaced by many coordinated low cost sensors, such as optical sensors used in our work for acquiring relevant data and coordinating the fleet of robots. In this paper, we propose a coordination algorithm for pattern formation (the shape of the fleet), which includes a new predefined pyramid pattern independent on the number of robots and an associated formation control strategy for building and maintaining this pyramid without collision among robots and without external assistant in non-obstacle situation. The formation control strategy consists of three steps: (1) a common frame is built after the exchange of local information extracted from visual servoing (VS) and compass of each robot; (2) a pyramid frame is established from the distribution of robots, and whose orientation is defined by one of the two following methods: one based on principal component analysis (PCA) and the other one based on an intermediate circle pattern (CP); (3) the kernel part is the collision avoidance strategy (CA) which is realized by optimally matching up positions in the distribution and the pyramid, and planning the straight nonintercrossed trajectories. At last, robots build the pyramid along the straight trajectory. During the whole process, except the information exchange for establishing the common frame, robots do not need communication as they have the same algorithms. Until now the performance of the formation control strategy is demonstrated with 4 to 6 robots in Blender based on dynamic equations of real underwater robots.

Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication

“…The pattern to be built is a pyramid pattern shown in Figure 2, which is a leader-follower model, designed in [19] based on the second smallest eigenvalue of Laplacian matrix [20] and other literals [21,22]. Except the leader, each of the other NAOs (noted as a child) followers one NAO located in their front layer (noted as a parent) with the expected distance d e and angle ϕ e , respectively.…”

“…Then a pyramid frame o p x p y p will be established, as the origin point o p (x op , y op ) is the average value of all the positions of NAOs in the o c x c y c , the y axis is the principal axis (the pyramid orientation), and x axis is perpendicular to y axis, as shown in Figure 13. Subsequently, the pyramid distribution in the pyramid frame o p x p y p can be identified according to its definition after giving the leader position first [19].…”

“…The edges of the match-up positions are non-intercrossing and straight, which are the planned trajectories including two elements: angle and distance, noted as (η i , d i ) in the pyramid frame, shown in Figure 14(c). The details and the performance proof have been given in [19].…”

“…Considering that the forehead camera looks forward, a leader-follower pattern, the planar pyramid pattern (pyramid pattern for short in the rest part), is given to put all the NAOs together [19], because the internal robots can consolidate the connections among the robots and reduce the communication length, which meets the short working range of the video cameras. To achieve the pyramid pattern, a local position-based control method is proposed to plan trajectories and build the pattern with minimum WiFi communication by refering to the position-based method and giving an O(n log n) complexity collision avoidance strategy.…”

Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning

“…Four seed agents acted as a static reference point for all other agents. Other methods that use seed/leader agents can be found in the works of Khaledyan and de Queiroz (2017), Cicerone et al (2016), Hasan et al (2018), and Wang et al (2017). Furthermore, Derakhshandeh et al (2016), Di Luna et al (2017), and Yamauchi and Yamashita (2014) explored autonomous leader election algorithms to avoid manually defining leaders.…”