Priority-based intersection management with kinodynamic constraints

We consider the problem of coordinating a collection of robots at an intersection area taking into account dynamical constraints due to actuator limitations. We adopt the coordination space approach, which is standard in multiple robot motion planning. Assuming the priorities between robots are assigned in advance and the existence of a collision-free trajectory respecting those priorities, we propose a provably safe trajectory planner satisfying kinodynamic constraints. The algorithm is shown to run in real time and to return safe (collision-free) trajectories. Simulation results on synthetic data illustrate the benefits of the approach.Comment: to be presented at ECC2014; 6 page

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Cooperative Control of the Dual Gantry-Tau Robot

Utilization of multiple parallel robots operating in the same work place and cooperating on the same job have opened up new challenges in coordination control strategies. Multiple robot control is a natural progression for Parallel Kinematic Machines (PKM) as it offers many of the desirable qualities especially in cooperative arrangements where multiple robots can be associated with an easily reconfigurable parallel machine. These special characteristics allow much faster and precise manipulations especially in manufacturing industries. With the possibility of cooperative control architecture, PKMs will be able to perform many of the tasks currently requiring dual serial robots such as complex assemblies, heavy load sharing and large machining jobs

A Decentralized Strategy for Swarm Robots to Manage Spatially Distributed Tasks

Large-scale scenarios such as search-and-rescue operations, agriculture, warehouse, surveillance, and construction consist of multiple tasks to be performed at the same time. These tasks have non-trivial spatial distributions. Robot swarms are envisioned to be efficient, robust, and flexible for such applications. We model this system such that each robot can service a single task at a time; each task requires a specific number of robots, which we refer to as \u27quota\u27; task allocation is instantaneous; and tasks do not have inter- dependencies. This work focuses on distributing robots to spatially distributed tasks of known quotas in an efficient manner. Centralized solutions which guarantee optimality in terms of distance travelled by the swarm exist. Although potentially scalable, they require non-trivial coordination; could be computationally expensive; and may have poor response time when the number of robots, tasks and task quotas increase. For a swarm to efficiently complete tasks with a short response time, a decentralized approach provides better parallelism and scalability than a centralized one. In this work, we study the performance of a weight-based approach which is enhanced to include spatial aspects. In our approach, the robots share a common table that reports the task locations and quotas. Each robot, according to its relative position with respect to task locations, modifies weights for each task and randomly chooses a task to serve. Weights increase for tasks that are closer and have high quota as opposed to tasks which are far away and have low quota. Tasks with higher weights have a higher probability of being selected. This results in each robot having its own set of weights for all tasks. We introduce a distance- bias parameter, which determines how sensitive the system is to relative robot-task locations over task quotas. We focus on evaluating the distance covered by the swarm, number of inter- task switches, and time required to completely allocate all tasks and study the performance of our approach in several sets of simulated experiments

Modelling, Control and Optimization of Modular Reconfigurable Robots

Modular reconfigurable robots are robotic systems offering new opportunities to rapidly create fit-to-task flexible automation lines. The recent trends of increasingly varying market needs in low-volume high-mix manufacturing demands for highly adaptable robotic systems like this. In this context, methods for quickly and automatically generating a modular robot model and controller should be developed. Moreover, modularity and reconfigurabilty open up new opportunities for on-demand robot morphology optimization for varying tasks. Therefore a method to optimize the robot design for a certain criterion should be provided in order to exploit the full potential of reconfigurable robots. In this thesis, a complete hard- and software architecture for a modular reconfigurable EtherCAT-based robot is presented. This novel approach allows to automatically reconstruct the topology of different robot structures, composed of a set of body modules, each of which represents an EtherCAT slave. This approach enables to obtain in an automatic way the kinematic and dynamic model of the robot and store it in URDF format as soon as the physical robot is assembled or reconfigured. The method also automatically reshapes a generic optimization-based controller to be instantly used after reconfiguration. Finally, a study and analysis on how to find the best suited reconfigurable robot morphology for a given task are presented, starting from a fixed set of joint and link modules. In particular, is shown how exploiting multi-arm robotic systems and modifying the relative and absolute positions of their bases, can expand the solution space for a given task. Results obtained in simulations for different tasks, are verified with real-world experiments using a in-house developed reconfigurable robot prototype

A Pseudospectral Optimal Motion Planner for Autonomous Unmanned Vehicles

2010 American Control Conference, Marriott Waterfront, Baltimore, MD, USA, June 30-July 02, 2010This paper presents a pseudospectral (PS) optimal control algorithm for the autonomous motion planning of a fleet of unmanned ground vehicles (UGVs). The UGVs must traverse an obstacle-cluttered environment while maintaining robustness against possible collisions. The generality of the algorithm comes from a binary logic that modifies the cost function for various motion planning modes. Typical scenarios including path following and multi-vehicle pursuit are demonstrated. The proposed framework enables the availability of real-time information to be exploited by real-time reformulation of the optimal control problem combined with real-time computation. This allows the each vehicle to accommodate potential changes in the mission/environment and uncertain conditions. Experimental results are presented to substantiate the utility of the approach on a typical planning scenario