Conflict-Free Routing of Mobile Robots

The recent advances in perception have enabled the development of more autonomous mobile robots in the sense that they can operate in a more dynamic environment where obstacles surrounding the robot emerge, disappear, and move. The increased perception of Autonomous Mobile Robots (AMRs) allows them to plan detailed on-line trajectories in order to avoid previously unforeseen obstacles, making AMRs useful in dynamic environments where humans, traditional fork-lifts, and also other mobile robots operate. These abilities contributed to increase automation in logistic applications. This thesis discusses how to efficiently operate a fleet of AMRs and make sure that all tasks are successfully completed.Assigning robots to specific delivery tasks and deciding the routes they have to travel can be modelled as a variant of the classical Vehicle Routing Problem (VRP), the combinatorial optimization problem of designing routes for vehicles. In related research it has been extended to scheduling routes for vehicles to serve customers according to predetermined specifications, such as arrival time at a customer, amount of goods to deliver, etc.In this thesis we consider to schedule a fleet of robots such that areas avoid being congested, delivery time-windows are met, the need for robots to recharge is considered, while at the same time the robots have freedom to use alternative paths to handle changes in the environment. This particular version of the VRP, called CF-EVRP (Conflict-free Electrical Vehicle Routing Problem) is motivated by an industrial need. In this work we consider using optimizing general purpose solvers, in particular, MILP and SMT solvers are investigated. We run extensive computational analysis over well-known combinatorial optimization problems, such as job shop scheduling and bin-packing problems, to evaluate modeling techniques and the relative performance of state-of-the-art MILP and SMT solvers.We propose a monolithic model for the CF-EVRP as well as a compositional approach that decomposes the problem into sub-problems and formulate them as either MILP or SMT problems depending on what fits each particular problem best. The performance of the two approaches is evaluated on a set of CF-EVRP benchmark problems, showing the feasibility of using a compositional approach for solving practical fleet scheduling problems

Conflict-free routing of multi-stop warehouse trucks

The recent interest in greater vehicular autonomy for factory and warehouse automation has stimulated research in conflict-free routing: a challenging network routing problem in which vehicles may not pass each other. Motivated by a real-world case study, we consider one such application: truck movements in a tightly constrained warehouse. We propose an extension of an existing conflict-free routing algorithm to consider multiple stopping points per route. A high level metaheuristic is applied to determine the route construction and assignment of vehicles to routes

Leveraging Conflicting Constraints in Solving Vehicle Routing Problems

The Conflict-Free Electric Vehicle Routing Problem (CF-EVRP) is a combinatorial optimization problem of designing routes for vehicles to visit customers such that a cost function, typically the number of vehicles or the total travelled distance, is minimized. The CF-EVRP involves constraints such as time windows on the delivery to the customers, limited operating range of the vehicles, and limited capacity on the number of vehicles that a road segment can simultaneously accommodate.In previous work, the compositional algorithm ComSat was introduced and that solves the CF-EVRP by breaking it down into sub-problems and iteratively solve them to build an overall solution.Though ComSat showed good performance in general, some problems took significant time to solve due to the high number of iterations required to find solutions that satisfy the road segments\u27 capacity constraints. The bottleneck is the Paths Changing Problem, i.e., the sub-problem of finding a new set of shortest paths to connect a subset of the customers, disregarding previously found shortest paths. This paper presents an improved version of the PathsChanger function to solve the Paths Changing Problem that exploits the unsatisfiable core, i.e., information on which constraints conflict, to guide the search for feasible solutions. Experiments show faster convergence to feasible solutions compared to the previous version of PathsChanger

Route planning of automated guided vehicles for container logistics

Automated guided vehicles (AGVs) are widely used in container terminals for the movement of material from shipping to the yard area and vice versa. Research in this area is directed toward the development of a path layout design and routing algorithms for container movement. The problem is to design a path layout and a routing algorithm that will route the AGVs along the bi-directional path so that the distance traveled will be minimized. This thesis presents a bi-directional path flow layout and a routing algorithm that guarantee conflict-free, shortest time routes for AGVs. Based on the path layout, a routing algorithm and sufficient, but necessary conditions, mathematical relationships are developed among certain key parameters of vehicle and path. A high degree of concurrency is achieved in the vehicle movement. The routing efficiency is analyzed in terms of the distance traveled and the time required for AGVs to complete all pickup and drop-off jobs. Numerical results are presented to compare performance of the proposed model. The research provides the foundation for a bi-directional path layout design and routing algorithms that will aid the designer to develop complicated path layouts

DISPATCHING AND CONFLICT-FREE ROUTING OF VEHICLES IN NEW CONCEPTUAL AUTOMATED CONTAINER TERMINALS

Ph.DDOCTOR OF PHILOSOPH

Deployment of an Distributed Strategic Material Flow Control for Automated Material Flow Systems Consisting of Autonomous Modules

The modularisation of hardware and software is one approach to handle the demand for increasing flexibility and changeability of automated material flow systems that are, for example, utilised in flexible production systems. In such automated material flow systems, autonomous modules communicate with each other to coordinate and execute transport tasks. In this paper a strategic material flow control is introduced, which is distributed on several modules realised with a multi-agent system. The strategic material flow control agent coordinates transport tasks with advanced logistical requirements, such as sequencing. A transport task states for a transport unit the system source and sink together with arrival criteria at the sink, e.g. sequence In order to fulfil the arrival criteria the strategic material flow agent selects additional destinations within the automated material flow system to buffer a transport unit. For the selection of suitable buffer modules, several strategies are proposed and evaluated in a simulation study

Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster

Task allocation and motion coordination are the main factors that should be consi-dered in the coordination of multiple autonomous vehicles in material handling systems. Presently, these factors are handled in different stages, leading to a reduction in optimality and efficiency of the overall coordination. However, if these issues are solved simultaneously we can gain near optimal results. But, the simultaneous approach contains additional algorithmic complexities which increase computation time in the simulation environment. This work aims to reduce the computation time by adopting a parallel and distributed computation strategy for Simultaneous Task Allocation and Motion Coordination (STAMC). In the simulation experiments, each cluster node executes the motion coordination algorithm for each autonomous vehicle. This arrangement enables parallel computation of the expensive STAMC algorithm. Parallel and distributed computation is performed directly within the interpretive MATLAB environment. Results show the parallel and distributed approach provides sub-linear speedup compared to a single centralised computing node. © 2007 Springer-Verlag Berlin Heidelberg

Automatic Routing System for Intelligent Warehouses

Automation of logistic processes is essential to improve productivity and reduce costs. In this context, intelligent warehouses are becoming a key to logistic systems thanks to their ability of optimizing transportation tasks and, consequently, reducing costs. This paper initially presents briefly routing systems applied on intelligent warehouses. Then, we present the approach used to develop our router system. This router system is able to solve traffic jams and collisions, generate conflict-free and optimized paths before sending the final paths to the robotic forklifts. It also verifies the progress of all tasks. When a problem occurs, the router system can change the task priorities, routes, etc. in order to avoid new conflicts. In the routing simulations, each vehicle executes its tasks starting from a predefined initial pose, moving to the desired position. Our algorithm is based on Dijkstra's shortest path and the time window approaches and it was implemented in C language. Computer simulation tests were used to validate the algorithm efficiency under different working conditions. Several simulations were carried out using the Player/Stage Simulator to test the algorithms. Thanks to the simulations, we could solve many faults and refine the algorithms before embedding them in real robots.Comment: 2010 IEEE International Conference on Robotics and Automation, International workshop on Robotics and Intelligent Transportation System, Full Day Workshop, May 7th 2010, Anchorage, Alaska. Organizers,Christian Laugier (INRIA, France), Ming Lin (University of North Carolina, USA), Philippe Martinet IFMA and LASMEA, France),Urbano Nunes (ISR, Portugal

Hybrid multiobjective genetic algorithm for integrated dynamic scheduling and routing of jobs and automated guided vehicles in flexible manufacturing systems

The dynamic continues trend of adoption and improvement inventive automated technologies is one of the main competing strategies of many manufacturing industries. Effective integrated operations management of Automated Guided Vehicle (AGV) system in Flexible Manufacturing System (FMS) environment results in the overall system performance. Routing AGVs was proved to be NP-Complete and scheduling of jobs was also proved to be NP hard problems. The running time of any deterministic algorithms solving these types of problems increases very rapidly with the size of the problem, which can be many years with any computational resources available presently. Solving AGVs conflict free routing, dispatching and simultaneous scheduling of the jobs and AGVs in FMS in an integrated manner is identified as the only means of safeguarding the feasibility of the solution to each sub-problem. Genetic algorithm has recorded of huge success in solving NP-Complete optimization problems with similar nature to this problem. The objectives of this research are to develop an algorithm for integrated scheduling and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm, ensure the algorithm validity and improvement on the performance of the developed algorithm. The algorithm generates an integrated scheduling and detail paths route while optimizing makespan, AGV travel time, mean flow time and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The integrated algorithms use two genetic representations for the individual solution entire sub-chromosomes. The first three sub-chromosomes use random keys to represent jobs sequencing, operations allocation on machines and AGV dispatching, while the remaining sub-chromosomes are representing particular routing paths to be used by each dispatched AGV. The multiobjective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance history. The algorithm used weight mapping crossover (WMX) and Insertion Mutation (IM) as genetic operators for sub-chromosomes represented with priority-based representation. Parameterized uniform crossover (PUX) and migration are used as genetic operators for sub-chromosomes represented using random-key based encoding. Computational experiments were conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. First scenario uses static consideration, the second scenario uses dynamic consideration with machine failure recovery. Sensitivity analysis and convergence analysis was also conducted. The results show the effectiveness of the proposed algorithm in generating the integrated scheduling, AGVs dispatching and conflict-free routing. The comparison of the result of the developed integrated algorithm using two benchmark FMS scheduling algorithms datasets is conducted. The comparison shows the improvement of 1.1% and 16% in makespan of the first and the second benchmark production dataset respectively. The major novelty of the algorithm is an integrated approach to the individual sub-problems which ensures the legality, and feasibility of all solutions generated for various sub-problems which in the literature are considered separately