Abstract-In this paper we introduce a new decentralized navigation function for coordination of autonomous vehicles at intersections. The main contribution is a navigation function designed for vehicles with predefined paths that uses expected time to intersection for collision avoidance. In such way, deadlock situations are avoided. Different inertias of the vehicles are taken into account to enable on-board energy optimization for crossing. Heavier vehicles that need more energy and time for acceleration or braking are given an indirect priority at intersections. The proposed decentralized coordination scheme shows a significant improvement in energy consumption and in motion smoothness compared to traditional crossing with human drivers.

In this paper, the decentralized coordination of point-mass autonomous vehicles at intersections using navigation functions is considered. As main contribution, the inertia of the vehicles is taken into account to enable on-board energy optimization for crossing. In such a way, heavier vehicles that need more energy and time for acceleration or breaking are given an indirect priority at intersections. The proposed decentralized coordination scheme of autonomous vehicles at intersection is compared with an optimal centralized coordination approach and traditional crossing of manned vehicles at intersection with traffic lights. The proposed decentralized coordination scheme shows a significant improvement in energy consumption and in motion smoothness compared to traditional crossing. It is also easier to deploy and more scalable than centralized approaches, with only a limited performance reduction.

This paper presents a decentralized model predictive control approach for the coordination of autonomous vehicles at intersections. A linear quadratic optimal controller is introduced for each vehicle with predefined path, in order to minimize energy as well as to pass intersection smoothly. We guarantee collision avoidance by adding linear constraints to the optimization problem. We apply the so-called soft constraints for the collision avoidance problem to reduce the computation time in order to be able to run the simulations in real-time. In addition, the method can take into account crossing of vehicles in platoons by extending the linear quadratic cost functions.

Some of the next-generation massive spectroscopic survey projects plan to use thousands of fiber positioner robots packed at a focal plane to quickly move the fiber ends in parallel from the previous to the next target points. The most direct trajectories are prone to collision that could damage the robots and have an impact on the survey operation. We thus present here 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 the actuator constraints. We provide details of the proof of convergence and collision avoidance. Decentralization results in linear complexity for the motion planning as well as no dependence of motion duration on the number of positioners. Therefore, the coordination method is scalable for large-scale spectrograph robots. The short in-motion duration of positioner robots will thus allow the time dedicated for observation to be maximized.

One of the challenges with autonomous vehicles is their performance at intersections. An alternative control method for the coordination of autonomous vehicles at intersections is shown. The proposed approach was grounded in multiple-robot coordination and took into account vehicle dynamics as well as realistic communications constraints. The existing concept of decentralized navigation functions was combined with a sensing model, and a crossing strategy was developed. The simulation results showed that because of the proposed approach, vehicles had smoother trajectories when crossing at a four-way intersection. The proposed method was compared with adaptive traffic lights and roundabouts in terms of throughput. Results showed that using a decentralized navigation function for the coordination of autonomous vehicles improved their performance by reducing energy consumption and pollution emissions.

Context. Massive spectroscopic survey are becoming trendy in astrophysics and cosmology, as they can address new fundamental knowledge such as Galactic Archaeology and probe the nature of the mysterious Dark Energy. To enable massive spectroscopic surveys, new technology are being developed to place thousands of optical fibers at a given position on a focal plane. These technology needs to be: 1) accurate, with micrometer positional accuracy; 2) fast to minimize overhead; 3) robust to minimize failure; and 4) low cost. In this paper we present the development of a new 8-mm in diameter fiber positionner robot using two 4mm DC-brushless gearmotors, developed in the context of the Dark Energy Spectroscopic Instrument. This development was conducted by a SpanishSwiss (ES-CH) team led by the Instituto de Física Teórica (UAM-CSIC) and the Laboratoire d'Astrophysique (EPFL), in collaboration with the AVS company in Spain and the Faulhaber group (MPS & FAULHABER-MINIMOTOR) in Switzerland. Aims. The meachanical concept, DC-brushless motor properties, and the final performance of a prototyped unit is presented. Methods. Performance and verification tests were conducted with a fiber view camera-based optical set-up and using an automatic algorithm. Results. The prototype build is mechanically robust and reliable, and its control electronics ensure a very firm system with an xy positional accuracy better than 5µm. Conclusions. In this paper, we validate the concept of our advanced 8-mm fiber robot positioner prototype, as well as demonstrate that it can meet the requirements of the DESI project. Such efficient gearmotor fiber positionner robotic system can be adapted to any future massive fiber-fed spectrograph instrument.

Abstract-This paper deals with the coordination of a group of mobile robots at an intersection. It focusses on decentralized navigation functions (DNFs) to achieve efficient traffic control. The main challenge is to define virtual potentials, which are used by decentralized navigation functions, such that traffic is both fluent and safe, while taking into account real-world limitations like acceleration, braking and speed limits. Our method consists in defining the navigation function with respect to the desired acceleration profile and is accompanied by a set of visibility conditions that increase the capacity of the intersection in terms of vehicle throughput. Priority conditions have been used to both avoid blockades of robots and to save energy by assigning higher priorities to robots with higher inertias.

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 nextgeneration 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).