Sarmad Riazi

Energy optimization of multi-robot systems

This paper presents an optimization algorithm that has been able to save up to 45% of the energy consumption of an industrial multi-robot system. The tool Sequence Planner now includes these algorithms, focusing on minimizing energy consumption. The goal has been to reduce the energy consumption of individual robots and robots interacting in a work station, without changing the original paths or the total cycle time. The presented algorithms are based on an efficient and rapid nonlinear model for optimizing the sequences of operations and the motion trajectories. Smart simplification of the optimization problem together with innovative tools for logging data and executing the optimized trajectories on real robots has resulted in 18% to 45% saving of the energy consumption in the presented test scenarios.

Energy and Peak Power Optimization of Time-Bounded Robot Trajectories

This paper, as an outcome of the EU project AREUS, heralds an optimization procedure that reduces up to 30% of energy consumption and up to 60% in peak-power for the trajectories that were tested on a real industrial robot. We have evaluated a number of cost functions and tested our algorithm for a variety of scenarios such as varying cycle times, payloads, and single/two-robot cases. The significance of our work is not only in the impressive savings, simplicity of implementation and preserving path and cycle time, but also in the effort made to carry out the optimization and experiments in as realistic conditions as possible, and the guidelines we provide to achieve this.

Energy and peak-power optimization of existing time-optimal robot trajectories

This paper, as an outcome of the EU project AREUS, heralds an optimization procedure that reduces up to 30% of energy consumption and up to 60% of peak-power for the trajectories that were tested on a real industrial robot. We have evaluated a number of cost functions and examined our algorithm for a variety of scenarios such as varying cycle times and single/two-robot cases. The significance of our work is not only in the impressive savings, simplicity of implementation and preserving path and cycle time, but also in the effort made to carry out the optimization and experiments in as realistic conditions as possible.

Modeling and Optimization of Hybrid Systems for the Tweeting Factory

In this paper, a predicate transition model for discrete-event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model, a hybrid Petri net including explicit differential equations and shared variables is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30% reduction in energy consumption. Moreover, a flexible online and event-based information architecture called the Tweeting Factory is proposed. Simple messages (tweets) from all kinds of equipment are combined into high-level knowledge, and it is demonstrated how this information architecture can be used to support optimization of robot stations.

A Gossip Algorithm for Home Healthcare Scheduling and Routing Problems

Many people in need of care still live in their homes, requiring the caretakers to travel to them. Assigning tasks to nurses (or caretakers) and scheduling their work plans is an NP-hard problem, which can be expressed as a vehicle routing problem with time windows (VRPTW) that includes additional problem-specific constraints. In this paper, we propose to solve the Home Healthcare Scheduling and Routing Problem (HHCRSP) by a distributed Gossip algorithm. We also apply an extended version called n-Gossip, which provides the required flexibility to balance optimality versus computational speed. We also introduce a relaxation on the optimality of the underlying solver in the Gossip, which speeds up the iterations of the Gossip algorithm, and helps to quickly obtain solutions with good quality.

Benders/gossip methods for heterogeneous multi-vehicle routing problems

In this paper, we propose a logic-based Benders decomposition (LBBD), as well as an LBBD/gossip method to solve the heterogeneous multi-vehicle routing problem (HMVRP). HMVRP is a newly formalized extension of the NP-hard multi-traveling salesman problem (mTSP). First, a hybrid algorithm based on LBBD is formulated that decomposes the HMVRP into an assignment problem and a cluster of sequencing problems. The former is solved by a mixed integer linear programming (MILP) solver, and the latter by a dedicated TSP solver. Then, a gossip algorithm is constructed which utilizes the mentioned LBBD for local optimization to achieve better computational efficiency. The use of LBBD remarkably reduces the CPU time. Furthurmore, integrating the three layers of gossip algorithm, LBBD and the TSP solver, results in a very efficient solution method.

Computationally efficient energy optimization of multiple robots

This paper presents convex modeling techniques for the problem of optimal velocity control of multiple robots on given intersecting paths. The optimal control problem is formulated as a nonlinear program, which generates predictive state and control trajectories that avoid collisions among the robots and minimize a certain performance index, such as operation time, energy dissipation and actuator usage. Advantages and disadvantages of two modeling approaches are discussed, involving a variable change with square of velocity, or alternatively inverse of velocity. The performance of the two approaches is evaluated in a case study with an industrial robot.

Modeling and Optimization of Hybrid Systems

In this paper a predicate transition model for discrete event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model a hybrid Petri net, including explicit differential equations and shared variables, is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30\% reduction in energy consumption.