Industries are on the brink of widely accepting a new paradigm for organizing production by having autonomous robots manage in-factory processes. This transition from static process chains towards more automation and autonomy poses new challenges in terms of, e.g., efficiency of production processes. The RoboCup Logistics League (RCLL) has been proposed as a realistic testbed to study the above mentioned problem at a manageable scale. In RCLL, teams of robots manage and optimize the material flow according to dynamic orders in a simplified factory environment. In particular, robots have to transport workpieces among several machines scattered around the factory shop floor. Each machine performs a specific processing step, orders that denote the products which must be assembled with these operations are posted at run-time and require quick planning and scheduling. Orders also come with a delivery time window, therefore introducing a temporal component into the problem. Though there exist successful heuristic approaches to solve the underlying planning and scheduling problems, a disadvantage of these methods is that they provide no guarantees about the quality of the solution. A promising solution to this problem is offered by the recently emerging field of Optimization Modulo Theories (OMT), where Satisfiability Modulo Theories (SMT) solving is extended with optimization functionalities. In this paper, we present an approach that combines bounded model checking and optimization to generate optimal controllers for multi-robot systems. In particular, using the RoboCup Logistics League as a testbed, we build formal models for robot motions, production processes, and for order schedules, deadlines and rewards. We then encode the synthesis problem as a linear mixed-integer problem and employ Optimization Modulo Theories to synthesize controllers with optimality guarantees.
Single auction-based methods are known to be efficient for multi-robot problem solving. In this work, we investigate performance of our general multi robot coordination framework for multi robot multi target exploration problem under uncertainties in dynamic environments. Our framework offers a real time single item allocation method featuring different mechanisms for failure recovery. In multi robot exploration problem, a different version of well known NPhard MTSP (Multiple Traveling Salesman Problem), each target is visited by at least one robot in its open tour. Overall objective function for cost optimization while visiting targets varies by different exploration domains.
Akin, H. Levent (Bogazici University) | Ito, Nobuhiro (Aichi Institute of Technology) | Jacoff, Adam (National Institute of Standards and Technology) | Kleiner, Alexander (Linköping University) | Pellenz, Johannes (V&R Vision &) | Visser, Arnoud (Robotics GmbH)
The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (for example, Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.
Making a one-legged robot that moves is very hard. Two-legged robots are a little bit more straightforward in some ways, and four-legged robots are statically stable much of the time. You can see where this is going--there's a general trend towards more legs being more stable and potentially easier to control, especially as terrain complexity increases. So what happens if you take that logic to an extreme? As it turns out, you end up with a spherical robot made of 32 individually actuated telescoping legs, named Mochibot.