Decentralized multi-robot coordination in crowded workspaces

The coordination of multi-robot systems is becoming one of the most important areas of research in robotics, mostly because it is required by numerous complex applications. These applications range from intelligent transportation systems, search and rescue robots, and medical robots, to cosmology and astrophysics. The coordination of multi-robot systems is based upon cooperation. The actions performed by each robot take into account the actions executed by the others in such a way that the whole system can operate coherently and efficiently. Regardless of the application, coordination is the key to the successful design and implementation of multi-robot systems. The number of robots involved in the aforementioned applications is increasing along with advances in miniaturization and automation. Consequently, a large number of robots need to share a workspace. This crowded workspace introduces new challenges into the coordination problem by increasing the risk of collision. To take into account communication constraints and sensor ranges, robots rely on local information. Therefore, efficient but simple coordination algorithms are required. This thesis investigates decentralized approaches based on navigation functions for the coordination of multi-robot systems in crowded workspaces. Decentralization allows robots to rely on local information, guarantees scalability and enables real-time deployment. Navigation functions are a special category of potential functions. Their negated gradient vector-field is attractive towards the goal and repulsive with respect to fixed or moving obstacles to avoid collision. In the first part of the thesis, we present the multi-robot coordination problem using navigation functions in a game-theory based framework. We propose a motion model along with a control law that leads the robots to a Nash equilibrium. The existence of the Nash equilibrium enables navigation functions to be exploited for studying, building, and running coordination frameworks for multi-robot systems. In the second part, we address the coordination of autonomous vehicles at intersections. A novel decentralized navigation function is proposed. It guarantees collision-free crossing of autonomous vehicles modeled as first order dynamic systems. The inertia of the vehicles is also introduced in the navigation functions to ensure deadlock-free coordination. The proposed approach does not require adaptation of the road infrastructure and relies upon onboard vehicles sensor data. Compared with traffic lights and roundabouts, the proposed method significantly reduces the travel time and the number of stops, thus decreasing energy consumption and pollution emission. This provides a strong motivation to pursue efforts towards the deployment of autonomous vehicles on roads. In the third part of the thesis, we investigate a coordination framework for a large number of miniaturized fiber positioner robots. The fiber positioner robots are designed and built as parts of the next generation of telescopes enabling large spectroscopic surveys. The proposed decentralized framework ensures the collision-free coordination of the fiber positioners sharing a crowed workspace at the focal plate of the telescope. The dynamical (max speed) and the mechanical (limited actuation range) constraints of the positioners are taken into account in the proposed coordination approach, which significantly reduces the time to reach a new robot configuration.

Collision avoidance in next-generation fiber positioner robotic systems for large survey spectrographs

Hannes Bleuler, Mohamed Bouri, Denis Gillet, Laurent Frédéric Jenni, Jean-Paul Richard Kneib, Laleh Makarem

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 in parallel the fiber-ends from the previous to the next target points. The most direct trajectories are prone to collision that could damage the robots and impact 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 for the proof of convergence and collision avoidance. Decentralization results in linear complexity for the motion planning as well as dependency of motion duration with respect to 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.

2014

Toward Online Probabilistic Path Replanning

Björn Jensen, Roland Philippsen, Roland Siegwart

In this talk we present work on sensor-based motion planning in initially unknown dynamic environments. Motion detection and probabilistic motion modeling are combined with a smooth navigation function to perform on-line path planning and replanning in cluttered dynamic environments such as public exhibitions. Human behavior is unforeseeable in most situations that include human-robot interaction, e.g. service robots or robotic companions. This makes motion prediction problematic (they rarely move e.g. with constant velocity along straight lines), especially in settings which include large numbers of humans. Additionally, the robot is usually required to react swiftly rather than optimally, in other words the time required to calculate the plan becomes part of the optimality criterion. The "Probabilistic Navigation Function" (PNF) is an approach for planning in these cluttered dynamic environments. It relies on probabilistic worst-case computations of the collision risk and weighs regions based on that estimate. The PNF is intended to be used for gradient-descent control of a vehicle, where the gradient indicates the best trade-off between risk and detour. An underlying reactive collision avoidance provides the tight perception-action loop to cope with the remaining collision probability. As this is work in progress, we present the approach and describe finished components and give an outlook on remaining implementation issues. Two algorithmic building blocks have been developed and tested: On-line motion detection from a mobile platform is performed by the SLIP scan alignment method to separate static from dynamic objects (it also helps with pose estimation). The interface between motion detection and path planning is a probabilistic co-occurrence estimation measuring the risk of future collisions given environment constraints and worst-case scenarios, which unifies dynamic and static elements. The risk is translated into traversal costs for an E* path planner, which produces smooth navigation functions that can incorporate new environmental information in near real-time.

2006

Coordination schemes for distributed boundary coverage with a swarm of miniature robots

Nicolaus Correll

We provide a comparison of a series of original coordination mechanisms for the distributed boundary coverage problem with a swarm of miniature robots. Our analysis is based on real robot experimentation and models at different levels of abstraction. Distributed boundary coverage is an instance of the distributed coverage problem and has applications such as inspection of structures, de-mining, cleaning, and painting. Coverage is a particularly good example for the benefits of a multi-robot approach due to the potential for parallel task execution and additional robustness out of redundancy. The constraints imposed by a potential application, the autonomous inspection of a jet turbine engine, were our motivation for the algorithms considered in this thesis. Thus, there is particular emphasis on how algorithms perform under the influence of sensor and actuator noise, limited computational and communication capabilities, as well as on the policies about how to cope with such problems. The algorithms developed in this dissertation can be classified into reactive and deliberative algorithms, as well as non-collaborative and collaborative algorithms. The performance of these algorithms ranges from very low to very high, corresponding to highly redundant coverage to near-optimal partitioning of the environments, respectively. At the same time, requirements and assumptions on the robotic platform and the environment (from no communication to global communication, and from no localization to global localization) are incrementally raised. All the algorithms are robust to sensor and actuator noise and gracefully decay to the performance of a randomized algorithm as a function of an increased noise level and/or additional hardware constraints. Although the deliberative algorithms are fully deterministic, the actual performance is probabilistic due to inevitable sensor and actuator noise. For this reason, probabilistic models are used for predicting time to complete coverage and take into account sensor and actuator noise calibrated by using real hardware. For reactive systems with limited memory, the performance is captured using a compact representation based on rate equations that track the expected number of robots in a certain state. As the number of states explode for the deliberative algorithms that require a substantial use of memory, this approach becomes less tractable with the amount of deliberation performed, and we use Discrete Event System (DES) simulation in these cases. Our contribution to the domain of multi-robot systems is three-fold. First, we provide a methodology for system identification and optimal control of a robot swarm using probabilistic models. Second, we develop a series of algorithms for distributed coverage by a team of miniature robots that gracefully decay from a near-optimal performance to the performance of a randomized approach under the influence of sensor and actuator noise. Third, we design an implement a miniature inspection platform based on the miniature robot Alice with ZigBee ready communication capabilities and color vision on a foot-print smaller than 2 × 2 × 3 cm3.