Improving AGV Systems: Integration of Advanced Sensing and Control Technologies

Improving AGV Systems: Integration of Advanced Sensing and Control Technologies Fabio Oleari, Massimiliano Magnani and Davide Ronzoni Elettric80 s.p.a. via G. Marconi 23, 42030 Viano Italy {oleari.f, magnani.m, ronzoni.d}@elettric80.it Abstract This paper focuses on the integration of advanced sensing and control technologies into advanced systems of multiple Automated Guided Vehicles (AGVs). In particular, we focus on the key technologies developed within the Plug-and-Navigate Robots (PAN-Robots) project, highlighting how those results contribute to improving the performance of AGV systems. Lorenzo Sabattini, Elena Cardarelli, Valerio Digani, Cristian Secchi and Cesare Fantuzzi Department of Sciences and Methods for Engineering (DISMI) University of Modena and Reggio Emilia via G. Amendola 2, 42122 Reggio Emilia Italy {lorenzo.sabattini, elena.cardarelli, valerio.digani, cristian.secchi, cesare.fantuzzi}@unimore.it I. INTRODUCTION This paper deals with the challenge of increasing the degree of automation in factory logistics. In particular, we focus on the use of Automated Guided Vehicles (AGVs), exploited for movement of goods and raw material inside industrial plants. In modern manufacturing plants, automation is widely adopted in the production phases, which leads to achieving a high level of productivity and efficiency. However, the same level of automation is generally not achieved in logistics, that is typically performed by human operators, and manually driven vehicles. However, it is worth noting that the movement of goods in a manufacturing plant is a very frequent operation: its manual implementation is then a relevant source of inefficiency for the overall system. In fact, if logistics is not well integrated with the overall production flow, it becomes a bottleneck for the entire system. Moreover, logistics is one of the main sources of safety issues for workers [1]. Massive deployment of AGVs would be of great help in increasing the level of automation of factory logistics. In fact, AGV systems lead to obtaining a completely integrated and optimized factory (Fig. 1). Nowadays these autonomous systems have a market share of about few thousands vehicles sold every year and they are not yet ready to be widespread in manufacturing plants. In fact safety, efficiency and plant installation costs are still open issues: state-of-the-art technological solutions are not mature enough to fully support a pervasive diffusion of AGVs. Therefore, innovations to address weaknesses of AGVs and automated warehouse systems will boost capabilities of these logistic solutions bringing them toward a pervasive diffusion in modern factories. The paper is organized as follows. Section II describes the scenario and related works about the automation of factory warehouses. The state of the art of AGV technology is then presented in Section III. The integration of new technologies 978-1-4673-8200-7/15/$31.00 c 2015 IEEE Fig. 1. AGVs moving goods in a modern factory warehouse with high production volumes developed within the PAN-Robot project is then described in Section IV. Finally, concluding remarks are given in Section V. II. SCENARIO AND RELATED WORKS In this paper we consider technological issues related to AGV systems used for factory logistics [2], [3]. Several research groups have been working of AGV systems, in the last few decades: a comprehensive survey of the relevant literature is presented in [4], where authors describe the main technologies adopted for localization and guidance of AGVs in industrial environments. The work in [5] describes the use of multiple AGVs for cooperative transportation of huge and heavy loads. Generally speaking, AGV systems are used for performing movements of goods among different positions of the industrial environment [6], [7]. Each movement operation is generally referred to as a mission. Different kinds of missions can be performed: pallets of goods can be transported from the end of an automated production line to the warehouse, from the warehouse to the shipment, or between two locations of the warehouse. Typically, goods prepared in automated production lines needs to be picked from a wrapper (Fig. 2(a)) of from a palletizer (Fig. 2(b)). AGVs are exploited for accomplishing missions in an automated manner. For this purpose, the AGV system is handled by a centralized controller, usually referred to as Warehouse Management System (WMS), that is in charge of assigning each mission to be completed to a specific AGV. Once each mission has been assigned to a specific AGV, then 257

(a) Wrapper Fig. 3. Complete and detailed portion of an AGV routemap for a medium size plant with several production lines (b) Palletizer Fig. 2. End of an automated production line the centralized controller needs to coordinate the motion of the AGVs themselves for mission completion. When dealing with a single AGV, several strategies can be exploited for singlerobot path planning (see e.g. [8]). Conversely, when multiple AGVs share the same environment, coordination strategies need to be adopted in order to optimize the traffic. Typically, the central controller is in charge of coordinating the motion of the AGVs [9] [13]. In order to simplify the coordination, and to enhance safety of operations, AGVs are often constrained to move along a predefined set of roads, referred to as route map (Fig. 3). III. S TATE OF THE ART In this section we briefly summarize the main technological solutions that are currently implemented in state-of-the-art AGV systems used for factory logistics. We will also highlight the main issues related to these systems. A. System installation Automated motion of the AGVs requires a precise and constantly updated knowledge of the current position. This is typically obtained exploiting laser-based technologies [14], that provide very reliable results. In particular, laser-based localization is obtained by each vehicle computing its relative position with respect to opportunely placed artificial landmarks (i.e. reflectors). A precise knowledge of the map of landmarks is mandatory for obtaining a highly precise localization. Moreover, the position of the landmarks themselves has a great influence on the localization accuracy: optimal landmark placement is addressed, for instance, in [15]. 258 For large plants, hundreds to thousands of reflectors are necessary for obtaining accurate localization. Hence, one of the first phases of plant installation consists in installing hundreds of reflectors, focusing on avoiding unwanted symmetries and in covering the entire working space. This is a time consuming operation, that is generally performed in a manual manner by a team of specialized technicians. Moreover, it is often necessary to achieve the final result step-by-step by modifying and verifying the new reflector layout design. Subsequently, the plant can be prepared to enable AGVs to complete missions. As mentioned before, missions consist in moving goods among different locations of the environment. In particular, locations where loading and unloading operations are performed are generally referred to as operation points. The position and the characteristics of each operation point need then to be mapped with high precision, in order to ensure that loading and unloading operations can be performed in an effective manner. Finally, a set of roads need to be define that connect each pair of operation points. This set of roads is typically referred to as route map. The design of the route map is generally performed manually with a CAD software and requires a highly specialized operator. In fact the route map design impacts on AGV traffic and then defines the system efficiency. It is worth noting that AGV systems are installed both in new plants and in already operating warehouses. When designing a plant from scratch, fewer problems appear, because of the possibility of easily adapting the environment to the automated system needs. Conversely, the adaptation of an already operating warehouse is a complex task, that takes months of works. Subsequently, reducing the time needed for system installation would heavily reduce the overall system costs.

B. Motion coordination The motion of the AGVs is coordinated inside the warehouse in order to ensure completion of all missions in an efficient manner. This entails reducing the overall completion time, while avoiding collisions and deadlocks. Centralized path planning and traffic coordination is a very complex problem, whose complexity grows exponentially with the number of vehicles. In order to reduce the complexity of the coordination algorithm, a commonly adopted methodology consists in constraining the motion of the vehicles along route maps. It is worth noting that, in general, the design of the route map and the design of the coordination algorithm both contribute to the overall efficiency of the system. Typically, specific features of the route map are handled by adding exceptions to the coordination algorithm, in the form of traffic rules [16]. Moreover, while route maps are a very effective manner of reducing the computational resources needed for traffic management, constraining the motion of the AGVs on a finite set of roads severely reduces the flexibility of the system. For instance, if an obstacle appears on an AGV s road, it is necessary to re-plan the path to circumvent the obstacle. When an alternative path is not available, traffic jams might appear, that can be solved only with the intervention of an operator, to manually remove the obstacle. C. Sensing and perception Human workers and autonomous machines usually share the environment in warehouses (Fig. 4), so safety is the main issue that must be fully addressed. Safety systems always need scanners. While these sensors are effective from a safety point of view, they do not provide advanced sensing capabilities. In fact, they only provide local information related to predefined areas around the AGV. Moreover, they do not provide classification of the acquired objects: the AGV is then not able to distinguish between static and dynamic objects, or between pedestrians and other entities. Therefore, it is not possible to take high-level decisions based on these sensors. This causes the fact that AGVs often need to highly reduce their speed in critical zones (i.e. black spots) in order to guarantee a safe behavior in response to unpredictable situations. IV. I NTEGRATION OF NEW TECHNOLOGIES This section describes how the integration of novel sensing and control technologies into AGV systems leads to improving the overall performance, reducing the weaknesses highlighted in Section III. A. System installation Several technological solutions were developed within PAN-Robots to make the system installation phase more efficient. As stated in Section III, the system installation phase is the set of operations that lead to the definition of the route map. Since the route map is a set of paths that connect every pair of operation points of the plant, for its definition it is necessary to have a precise map of the plant itself, including the position of all the points of interest, and of all the infrastructure elements. The mapping of the environment can be carried out, in a semi-automated manner, exploiting the procedure introduced in [17], [18]. In particular, a system composed of multiple advanced laser scanners is utilized for providing a threedimensional map of the environment, where the position and the characteristics of each element are measured with high precision. The three-dimensional map of the environment is then exploited for the definition of the route map. In particular, an algorithm was introduced in [19] for automatically defining a set of roads that cover all the environment, guaranteeing that AGVs have the possibility to reach every point of interest. Redundancy is also maximized, to guarantee multiple choices in the path to complete missions. An example of an automatically created route map is shown in Fig. 5. Fig. 4. Example of an environment shared by AGVs and human workers to be reliable and robust and commonly rely on certified laser A time consuming operation to be carried out during plant setup is the installation of laser reflectors to be used for localization. This phase can be completely avoided utilizing an alternative system for AGV localization. In particular, the strategy introduced in [20] utilizes laser scanners for performing localization based on natural landmarks. In details, natural landmarks are features of the environment that can be recognized by sensing systems. Performing localization based on natural landmarks removes the need for laser reflectors, thus heavily reducing plant installation time and cost. The contour based localization system based on laser scanners proposed in [20] provides robust and precise results, that can then be exploited for AGV navigation. 259

Fig. 5. Example of automatic route map definition B. Motion coordination The coordination of the motion of the AGVs is performed by the Global Navigation module of the PAN-Robots system. In particular, the global navigation module is in charge of defining the path to be traveled by each AGV along the route map, which is assumed to be defined according to the automatic creation algorithm introduced in the previous section. The route map is then considered as a set of routes, composed by distinguished elements called segments. The AGVs are constrained to follow the route map and its segments. In particular, each segment can be allocated only to one AGV at a time and it is characterized by a unique direction of movement. These properties are useful for coordination purposes, since it is not possible to have pathological situations, such as two (or more) AGVs moving along the same segment with opposite directions or conflicts on the same segment. The global navigation problem consists then in defining a coordination strategy such that each AGV is able to move from its initial position to its assigned final position, minimizing the total crossing time and by avoiding conflicts. Therefore, the problem consists in planning a path for a fleet of AGVs in a 2D static environment, so that conflicts and deadlocks are avoided. Each AGV starts its path in an initial position, and has to reach its own final position. The coordination strategy presented in [21], [22] proposed a multi-layer architecture for the coordination of a fleet of AGVs. In particular, two layers are used: 1) The higher layer, or Topological Layer, is a topological map representing the global map, with different macro-cells called sectors. 2) The lower layer is the geometric map of each sector of the first layer, and is then referred to as Route map Layer. Therefore the global navigation is performed as a planning on two levels. Topology planning searches for the best path to the final goal (actually to the final sector where the real goal is) from the current sector. Route map planning computes the path on the route map and handles the coordination inside the sector. Global navigation is performed once a mission has been assigned to each AGV. Clearly, in order to optimize the overall performance of the system, it is necessary perform the mission assignment in an optimized manner, that means minimizing the overall completion time. Mainly utilized assignment methods consider the mission assignment problem in a static manner: roughly speaking, each mission is assigned to the closest AGV. This assignment method does not consider the dynamic effects due to the motion of the AGVs themselves: namely, traffic jams might decelerate the AGVs, thus making the assignment very far from the optimal solution. In the PAN-Robots project, a novel mission assignment methodology was developed that explicitly considers the traffic model within the assignment problem: specifically, information on the state of the system is utilized to obtain a quantitative measure of the traffic, that is then exploited for an optimized mission assignment. Once the mission assignment has been performed, the multi-layer coordination method proposed in [21], [22] is applied. In particular, the coordination on the topological layer consist in defining the list of sectors to be traveled by each AGV to reach its destination. This problem is solved in a centralized manner, considering the traffic status, in a model predictive fashion: the optimal list of sectors is re-computed when the traffic status changes. Subsequently, inside each sector, the coordination on the route map layer is performed, for locally avoiding conflicts and deadlocks. An advanced coordination method, within sectors, was proposed in [23], with the purpose of reducing the number of necessary negotiations among the AGVs. Motion coordination and route map creation were combined in [24], where a holistic approach was proposed for simultaneously optimizing the coordination and route map creation, in order to maximize the overall efficiency of the system. A local path planning module has been implemented for computing local deviations from the route map in the presence of obstacles. Once obstacles have been perceived, the system automatically computes local deviations from the route map that prevent the AGV from stopping, if possible, thus avoiding the creation of traffic jams. Further details can be found in [25]. C. Sensing and perception An advanced sensing system is proposed in PAN-Robots to enhance the performance of the AGV system. The sensing system is composed of two main elements: on board sensing system, and infrastructure sensing system. In details, on board sensing system consists of laser scanners and cameras. Specifically, in order to complement safety laser scanners, each AGV is equipped with a reliable environment perception system, capable of monitoring the entire 360 region around the vehicle. The on board perception system is then composed by multiple laser scanners, positioned around the AGV, together with an omnidirectional stereo vision system consisting of two omnidirectional lenses and two cameras mounted on the top of the AGV. Implementation details can be found in [26]. 260

On board sensing system provides a view of the surroundings of the AGV, but has no possibility of providing a global view of the environment. To solve this issue, the PAN-Robots system includes additional sensing systems installed on the infrastructure. The idea is similar to the use of hemispherical mirrors mounted above the intersections, that are used by the workers to look around the corners (Fig. 6). Therefore, an solutions for logistics in factories of the future will significantly improve safety of work environments and efficiency of the overall production flow, with huge benefits for workers and manufacturing economy. ACKNOWLEDGMENT This paper is written within PAN-Robots project. PAN- Robots is funded by the European Commission, under the 7th Framework Programme Grant Agreement n. 314193. The partners of the consortium thank the European Commission for supporting the work of this project. REFERENCES Fig. 6. Examples of mirrors mounted above intersections: hemispherical (left) and flat-wide-fov (right) effective solution for monitoring of the black spots consists in the installation of laser scanners on specific locations in the environment, as detailed in [27]. Data acquired by the sensing systems need to be made available to the AGV motion control system, that can then take into account the presence of obstacles while planning the paths. A centralized system is then introduced, that is in charge of receiving data from different sources, opportunely merging them, and making them available for the AGV control system. This centralized system defines a Global Live View of the environment, that contains constantly updated information regarding all the entities that populate the industrial environment. This is obtained exploiting the methodology introduced in [28], that merges information acquired by different sources. In particular, a static three-dimensional map of the environment is built during the system installation phase, that describes all the static infrastructural elements (e.g. racks, walls, doors, etc.), while on board and infrastructure perception systems acquire dynamic objects. The centralized data fusion system is then in charge of collecting all data acquired by the sensors, and of combining them in a unique and complete representation of the overall system, including the static and dynamic entities that act inside it. Hence, the Global Live View contains dense information, that defines a global updated map representing the static entities (the 3D map of the plant, the route map), the dynamic entities (the current position and velocity of the AGVs, the position and velocity of currently identified objects), the congestion zones and the status of the monitored intersections. V. 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