Projects such as "The Dark Energy Spectroscopic Instrument” (DESI)  or ”The Multi Object Optical and Near-infrared Spectrograph” (MOONS )  are developing spectrographs, composed of more than thousand of optical fibers in a confined hexagonal focal plane, to study the evolution of the universe. Such systems allow fast reconfiguration of the fibers as they are moved simultaneously to their assigned target by a 2-arm positioner within an short interval of time. Moreover, astronomers prioritize the observation of some objects over those that hold less information, creating a hierarchy of importances or priorities. In a scenario where not all the positioners can reach their targets, It is important to ensure the observation of the high-priority targets. In previous works, a decentralized navigation function from the family of potential fields was used for collisionfree coordination. While it guarantees convergence of all the positioners to their targets for DESI [1,2], it fails at planning motion for positioners in MOONS . The reason is that the second arm of the positioners in MOONS is two times the length of the first arm. Covering a larger working space, they are prone to deadlocks, a situation where two or more positioners are blocked by each other and so unable to reach their targets. In this paper and in the framework of MOONS project, we present our new approach to integrate assigned priorities with the decentralized navigation functions to reduce the deadlocks situations. For this purpose, we regulate the movements of the positioners using a finite-state machine combined with distance-based heuristics. Each positioner’s state dictates its behaviors with respect to other positioners. Distance-based heuristics limit the states transition when a positioner is interacting with its adjacent positioners to localize possible deadlock situations. The advantage of this method is its simplicity as it relies on local interaction of positioners, keeping the complexity of the algorithm quasilinear. In addition, since it does not depend on the positioner’s geometry, it is also scalable to other positioner kinematics. We developed a motion planning simulator with a graphic interface in python to validate the coordination of the positioners with assigned priorities. As a result, the number of positioners converging to their targets improve from 60-70% to 80-95%. The computation time of the trajectories increases slightly due to the new layer of algorithm added for deadlocks prevention.
Many fiber-fed spectroscopic survey projects, such as DESI, PFS and MOONS, will use thousands of fiber positioners packed at a focal plane. To maximize observation time, the positioners need to move simultaneously and reach their targets swiftly. We have previously presented a motion planning method based on a decentralized navigation function for the collision-free coordination of the fiber positioners in DESI. In MOONS, the end effector of each positioner handling the fiber can reach the centre of its neighbours. There is therefore a risk of collision with up to 18 surrounding positioners in the chosen dense hexagonal configuration. Moreover, the length of the second arm of the positioner is almost twice the length of the first one. As a result, the geometry of the potential collision zone between two positioners is not limited to the extremity of their end-effector, but surrounds the second arm. In this paper, we modify the navigation function to take into account the larger collision zone resulting from the extended geometrical shape of the positioners. The proposed navigation function takes into account the configuration of the positioners as well as the constraints on the actuators, such as their maximal velocity and their mechanical clearance. Considering the fact that all the positioners' bases are fixed to the focal plane, collisions can occur locally and the risk of collision is limited to the 18 surrounding positioners. The decentralizing motion planning and trajectory generation takes advantage of this limited number of positioners and the locality of collisions, hence significantly reduces the complexity of the algorithm to a linear order. The linear complexity ensures short computation time. In addition, the time needed to move all the positioners to their targets is independent of the number of positioners. These two key advantages of the chosen decentralization approach turn this method to a promising solution for the collision-free motion-planning problem in the next- generation spectroscopic survey projects. A motion planning simulator, exploited as a software prototype, has been developed in Python. The pre-computed collision-free trajectories of the actuators of all the positioners are fed directly from the simulator to the electronics controlling the motors. A successful demonstration of the effectiveness of these trajectories on the real positioners as well as their simulated counterparts are put side by side in the following online video sequence (https://goo.gl/YuwwsE).
Next generation massive spectroscopic survey projects have to process a massive amount of targets. The preparation of subsequent observations should be feasible in a reasonable amount of time. We present a fast algorithm for target assignment that scales as O(<i>log</i>(<i>n</i>)). Our proposed algorithm follow a target based approach, which enables to assign large number of targets to their positioners quickly and with a very high assignment efficiency. We also discuss additional optimization of the fiber positioning problem to take into account the positioner collision problems and how to use the algorithm for an optimal survey strategy. We apply our target-based algorithm in the context of the MOONS project.
The next generation of large-scale spectroscopic survey experiments such as DESI, will use thousands of fiber positioner robots packed on a focal plate. In order to maximize the observing time with this robotic system we need to move in parallel the fiber-ends of all positioners from the previous to the next target coordinates. Direct trajectories are not feasible due to collision risks that could undeniably damage the robots and impact the survey operation and performance. We have previously developed a motion planning method based on a novel decentralized navigation function for collision-free coordination of fiber positioners. The navigation function takes into account the configuration of positioners as well as their envelope constraints. The motion planning scheme has linear complexity and short motion duration (2.5 seconds with the maximum speed of 30 rpm for the positioner), which is independent of the number of positioners. These two key advantages of the decentralization designate the method as a promising solution for the collision-free motion-planning problem in the next-generation of fiber-fed spectrographs. In a framework where a centralized computer communicates with the positioner robots, communication overhead can be reduced significantly by using velocity profiles consisting of a few bits only. We present here the discretization of velocity profiles to ensure the feasibility of a real-time coordination for a large number of positioners. The modified motion planning method that generates piecewise linearized position profiles guarantees collision-free trajectories for all the robots. The velocity profiles fit few bits at the expense of higher computational costs.
In the large-scale, Dark Energy Spectroscopic Instrument (DESI), thousands of fiber positioners will be used. Those are
robotic positioners, with two axis, and having the size of a pen. They are tightly packed on the focal plane of the
telescope. Dedicated micro-robots have been developed and they use 4mm brushless DC motors. To simplify the
implementation and reduce the space occupancy, each actuator will integrate its own electronic control board. This board
will be used to communicate with the central trajectory generator, manage low level control tasks and motor current
feeding. In this context, we present a solution for a highly compact electronic. This electronic is composed of two layers.
The first is the power stage that can drive simultaneously two brushless motors. The second one consists of a fast
microcontroller and deals with different control tasks: communication, acquisition of the hall sensor signals,
commutation of the motors phases, and performing position and current regulation. A set of diagnostic functions are also
implemented to detect failure in the motors or the sensors, and to sense abnormal load change that may be the result of
two robots colliding.