Individual item handling system capable of handling multiple grips, and associated method

Abstract

A method for individually handling articles capable of managing the risk of simultaneous picking of more than one article, comprising the steps of transporting articles on a first collective movement unit (CONV); acquiring an image of a picking zone (PickZ) of the articles using a first image sensor (CAM1); analyzing the image and locating an article (IT); commanding an individual handling unit (MA) equipped with a second image sensor (CAM2) and positioning the second sensor above the located article; acquiring an image (IMG2) of this article; analyzing this image (IMG2) and assessing the risk of multiple picking if the article is picked up by the individual handling unit (MA); then commanding the individual handling unit (MA) to pick up the article and move it to a destination (INS, RET, REJ) chosen from at least one reject bin (REJ) and a second collective movement unit (INS). Figure to be published with the abbreviation: Fig. 2

Description

Title of the invention: INDIVIDUAL ITEM HANDLING SYSTEM CAPABLE OF HANDLING MULTIPLE GRIPS, AND ASSOCIATED METHOD. TECHNICAL FIELD OF THE INVENTION

[0001] The invention relates to a system for the individual handling of articles between two units for the collective movement of these articles, for example for the individual placement of these articles on a conveyor belt during a depalletizing operation in a sorting facility. TECHNOLOGICAL BACKGROUND

[0002] Sorting systems for items such as luggage, parcels, and postal items implement conveyors, such as conveyor belts, capable of transporting a plurality of items simultaneously and / or gripper robots capable of individually handling each of these items.

[0003] Conveyors and gripping robots can be used in combination, for example for a depalletizing operation: the items are initially loose and possibly stacked on a first conveyor belt, then are handled individually by a gripping robot for individual placement, for example onto an inserter responsible for introducing each item into a machine dedicated to sorting these items. The inserter can be formed from a second conveyor belt.

[0004] The gripper robot is equipped with a gripper head, which is responsible for grasping the articles to be moved individually from the first conveyor belt to the inserter. However, it sometimes happens that the head accidentally grasps more than one article; this is called a "multiple grasp." This occurs, for example, when a gripper head uses multiple suction cups to grasp articles, and some suction cups attach to one article while others attach to a second. Such an event is accidental and preferably avoided. However, when it occurs, it is necessary to detect it and then handle the multiple grasped articles in a specific way, generally by sending them onto an alternative sorting path or by preventing all but one of the articles on the second conveyor belt from continuing their movement on that belt.In this regard, reference can be made to US patent documents 4,733,226 and FR 2,546,083. Regardless of the specific handling applied to the multiple items entered, the objective is to prevent the simultaneous insertion of several objects into the sorting machine. The risks associated with such insertion range from sorting or misorientation errors to jamming and damage to the sorting machine.

[0005] Some systems anticipate the risk of multiple capture by sending a set of insufficiently sorted articles from the first conveyor belt, which are very close to each other or even stacked, and therefore risk generating multiple capture, onto an alternative sorting path.

[0006] However, the implementation of such methods requires optimization of the detection of multiple taking, limiting false positives in order to reduce the undue differential treatment of articles perceived as posing a risk of multiple taking, without significantly reducing the processing rate of articles. Description of the invention

[0007] An object of the invention is an individual item handling system capable of managing the risk of simultaneous handling of more than one item by anticipating the occurrence of multiple handling.

[0008] To achieve this goal, a first aspect of the invention is a method for individually handling articles capable of managing the risk of simultaneously picking up more than one article, the method comprising the steps of transporting articles on a first collective article handling unit and bringing them to a picking area; acquiring an image of the picking area by means of a first image sensor; analyzing the image of the picking area by means of a computer system so as to locate an article in the picking area; in response to the analysis of the image of the picking area, the computer system commands an individual article handling unit equipped with a second image sensor, so as to position the second image sensor above the located article; acquiring an image of the located article by means of the second image sensor positioned above the located article;analyze the image of the located item using the computer system in order to assess the risk of multiple picking if the located item is picked up by the individual item handling unit, and in particular assess whether the risk of multiple picking is associated with a probability lower or higher than a certain predefined value; in response to the analysis of the image of the located item, command the individual item handling unit to pick up the located item and move it to a destination chosen from among at least one reject bin and a second collective item handling unit.

[0009] One application of this system is to prevent the simultaneous insertion of more than one item into a sorting machine while limiting the decrease in the item handling rate and therefore in their sorting. In particular, the use of two cameras, one of which is positioned directly above an item to be processed, improves the quality of the image used as the basis for detecting the risk of multiple capture and thus improves its reliability. Mechanically, the number of specific treatments aimed at limiting the Multiple transfers of items from the first item collective movement unit to the second item collective movement unit is reduced, which improves the throughput of the sorting system under consideration.

[0010] According to additional, non-limiting features of the first aspect of the invention, considered individually or in any technically feasible combination:

[0011] - the image of the localized article may have a resolution higher than a resolution of the image of the sampling area, these resolutions being able to be expressed in pixels per unit of length;

[0012] - if the probability is greater than a certain predefined value, then the unit of my individual article handling can seize the located article and place it in the waste bin;

[0013] - if the probability is less than a certain predefined value, then the unit of my individual item handling can move the located item up to a second unit of collective item handling;

[0014] - the analysis of the image of the localized article may include a step of determining if an address block is located on a visible face of the localized item;

[0015] - if the computer system determines that the probability is less than the certain predefined value and the address block is not located on a visible face of the located item, then the individual item handling unit can move the located item to an item turning unit;

[0016] - the item return unit can receive and return the item which is brought in and can then move the returned item to the second unit of collective item movement;

[0017] - if the probability is less than a certain predefined value and if the address block is located on a visible face of the pickable item, then the individual item handling unit can move the located item to a second collective item handling unit;

[0018] - the second unit for collectively moving articles can be an inserter of sorting machine;

[0019] - during the acquisition of the image of the located article, the second image sensor is positioned at a predefined height above the localized item in question.

[0020] The invention extends to an individual item handling system capable of managing the risk of simultaneously picking up more than one item and configured to implement the method according to the invention, the system comprising a first item handling unit; a first image sensor configured to acquire images of a picking area of ​​the first item handling unit; a second item handling unit; a ma individual article handling unit, equipped with a second image sensor and configured to move articles from the first article handling unit to the second article handling unit; and a computer device configured to control the individual article handling unit, the computer device being functionally connected to the first image sensor and the second image sensor, and configured to control the individual article handling unit in response to data generated by the first sensor and the second sensor.

[0021] The system according to the invention may further include an item turning unit configured to turn over an item which is brought to it and move the returned item to the second collective item movement unit. BRIEF DESCRIPTION OF THE FIGURES

[0022] Other features and advantages of the invention will become apparent from the detailed description of the invention which follows with reference to the accompanying figures in which:

[0023] [Fig.1] Fig.1 represents an overall view of an installation in which the process according to the invention can be applied;

[0024] [Fig.2] Fig.2 illustrates a system capable of implementing the process according to the invention, using a top view;

[0025] [Fig.3] Fig.3 is a diagram comprising the implementation steps of a method according to the invention; and

[0026] [Fig.4] The [Fig.4] is a variant of the process according to the diagram of the [Fig.3]. DETAILED DESCRIPTION OF THE INVENTION

[0027] Embodiment

[0028] Figures 1 to 4 illustrate an embodiment of a handling method according to the invention, as well as an associated article handling system, applied to the particular case of an article sorting installation.

[0029] Figure 1 illustrates a FAC article sorting installation, comprising a PickZ picking area into which IT articles to be sorted, brought by a Trck truck, are fed in bulk by means of a first CONV article handling unit. A robotic MA manipulator arm, constituting an individual article handling unit, is configured to pick up the IT articles one by one and place them onto a second INS article handling unit. The second article handling unit is, in this embodiment, an INS inserter configured to insert articles into a SORT sorting machine. This sorting machine may, for example, comprise a circular conveyor belt CCB receiving the articles from the INS inserter and sorting outputs Out (three outputs shown here) to which articles are directed according to their final destination.The CONV and INS units for collective movement of articles are movement systems. The system can be a collection of items and can independently consist of one or more conveyors such as conveyor belts or rollers. The installation can, for example, be designed to sort postal parcels and distribute them into delivery routes, each corresponding to one of the sorting outputs.

[0030] Fig. 2 illustrates more specifically an SYS system for individual item handling integrated into the FAC sorting installation, with the first CONV unit for collective item movement CONV with its PickZ picking zone, the second INS unit for collective item movement with its DZ dropping zone, and the MA manipulator arm for individual item handling with its GH gripping head.

[0031] The first CONV unit for collectively moving articles moves the IT articles from a bulk unloading area of ​​the Trck truck to the PickZ picking area in a direction Dirl represented by an arrow in [Fig. 2]. The gripping head GH can be equipped with suction cups connected to a pumping system; each suction cup can be individually controlled to apply a vacuum to the surface of an article to be picked up in the PickZ picking area.

[0032] A CONT unit for controlling the sorting line, and therefore the SYS system for individual item handling, includes a first digital image capture device and a second digital image capture device, such as cameras, respectively as image sensors CAM1 and CAM2, and a computer system INF including a computer memory and being functionally connected to the CAM1 and CAM2 sensors, and to the MA manipulator arm.

[0033] The first CAM1 sensor is configured to acquire images, each representing a general view of the PickZ sampling area. The first CAM1 sensor can be adapted to acquire a pair of digital images capable of forming a depth map of said scene according to known principles of photogrammetry.

[0034] The second CAM2 sensor is mounted on the manipulator arm, preferably on or near the GH gripping head, and configured to acquire images each representing one or more articles located under the gripping head.

[0035] In this embodiment, the IT items brought into the PickZ picking zone by the first CONV collective item movement unit are manipulated by the MA manipulator arm so as to change them from a disordered state resulting from the bulk unloading of the Trck truck onto the first CONV collective item movement unit to an ordered state on the second INS collective item movement unit. The MA manipulator arm's function is to handle these items individually by grasping them one by one and depositing them one by one into a DZ deposit zone on the second INS collective item movement unit.

[0036] The disordered state is characterized by an irregular distribution of the articles, possibly with articles piled one on top of the other or simply placed side by side. to each other. The ordered state is characterized for example by placing the articles one by one on a continuously rotating conveyor belt, in such a way that each of the articles is placed in the middle of the conveyor belt, according to a constant step P and defined as the distance separating two of the articles placed consecutively on the conveyor belt by the manipulator arm.

[0037] The second INS unit for collective item handling is configured to move the items placed on it towards an inlet of the SORT sorting machine, in a direction Dir2 defined as going from an item deposit zone DZ by the MA manipulator arm on the second INS item handling unit towards an inlet of the SORT sorting machine. The items, placed in an orderly fashion on the second INS item handling unit, can be correctly processed by the SORT sorting machine.

[0038] As an alternative to depositing articles in an orderly manner in the deposit zone DZ of the second INS movement unit, when the conditions for transmitting an article from the first to the second collective article movement unit are not met, the manipulator arm can bring the processed articles to either a reject bin REJ and an article turning unit RET, according to the modalities detailed below.

[0039] Fig. 3 illustrates a method for sorting articles implementing, for example, the FAC sorting installation illustrated by Figures 1 and 2.

[0040] At a step S10, IT items are placed in bulk on the first CONV unit of collective item movement, at an item receiving end located opposite the PickZ picking area. This could, for example, involve the bulk unloading of the contents of the Trck truck.

[0041] At step S20, the bulk items are collectively brought to the PickZ picking area by the CONV unit for collective item movement. During this movement, a granulation process may be carried out, reducing the density of the items to facilitate their individual handling at step S80.

[0042] At step S30, the CAM1 sensor acquires an image IMG1 of the sampling area. The IMG1 image has a first resolution, for example, of approximately 1 pixel per millimeter, which means that each pixel of the image represents approximately 1 mm of the sampling area.

[0043] At a step S40, the INF computer system performs an Anall analysis of the IMG1 image of the sampling area, according to classical segmentation and / or photogrammetry techniques, in order to isolate one of the IT articles visible on the image.

[0044] A process following the segmentation can calculate the attributes of the articles: position, height, orientation, or even inclination, attributes on the basis of which the The computer system may optionally associate with each article a pre-nability score calculated in such a way as to define a priority order in the grasping of articles by the manipulator arm, for example on the basis of criteria such as (i) the distance separating the article in question from the drop zone DZ of the second unit INS of collective movement of articles to which it must be taken by the manipulator arm, (ii) the risk of collision with another article on the way to the drop zone, (iii) the possibility of grasping the article and / or (iv) the expected quality of grasping in consideration of the type and configuration of the gripping head used and the geometry, dimensions and arrangement of the article in question.The items are ordered in descending order of their pickability score: the easiest items to pick up, i.e., those that minimize the risks of collision and dropping during movement while optimizing throughput, will be chosen first. See French patent application FR2305495.

[0045] The isolated article may advantageously be the one with the highest pickability score among those present in the Imgl image, which corresponds to a form of optimization in the choice of the order of individual movement of the articles present in the picking area.

[0046] At step S50, based on the analysis of the image IMG1 of the picking area, the INF computer system commands the MA individual item handling unit by means of a COM command, so as to position its gripping head GH and the second image sensor CAM2 above the located item. Preferably, the second image sensor is always placed at the same predefined height, typically 200 mm, above an upper surface of an item to be picked up, for the purpose of acquiring an image of that item.

[0047] At a step S60, the second image sensor CAM2 acquires an image IMG2 of the localized article by means of the second image sensor CAM2 positioned above the localized article, separated by the predefined height from the surface of the article.

[0048] At step S70, the INF computer system performs an Anal2 analysis of the IMG2 image of the located item in order to evaluate the probability of multiple picking if the located item is picked up by the MA individual item handling unit. Step S70 includes a test step S72, which evaluates (action indicated by "MULT?" on the diagram in [Fig. 3]) whether the risk of multiple picking of the located item exceeds (event indicated by Y on the diagram) a given threshold or whether, conversely, this probability is less than this threshold (event indicated by N on the diagram).

[0049] Such an analysis can, for example, be based on the use of a deep neural network, advantageously on a VGG19 model with two output neurons, the activation of one corresponding to the detection of items leading to catches We refer to these as "simple" when the network determines that only one item from image IMG2 will be captured by the manipulator arm's grasping head. Activation of the other corresponds to item detection, leading to multiple captures, when the network determines that more than one item from image IMG2 will be captured by the manipulator arm's grasping head. The goal of the neural network, after training, is to correctly associate the presented images with different classes, "multiple capture" or "single capture" in this case, by activating the appropriate output neuron. This architecture is relatively simple but demonstrates good results on simple classification problems (two or three output classes) and, above all, a controlled execution time.

[0050] The calculation time of the VGG19 models depends almost entirely on the size of the input image. The native images of the CAM2 on-board camera contain a significant background portion, so it is advantageous to select only a part of the IMG2 image centered on the item to be sampled.

[0051] The main difficulty in training the neural network for multiple-pick detection lies in the fact that it is an asymmetric problem: the rate of multiple picks in a stream of items generally fluctuates between 1 and 2%, while that of single picks is greater than 98%. Thus, a 1% misclassification of single picks (false positives) represents approximately 1% of the stream, whereas a 1% misclassification of multiple picks (false negatives) represents barely 0.02% of the stream. It is generally very difficult to reduce both the number of false positives and false negatives simultaneously. For real-world operation of the SYS system, it can be advantageous to keep the false positive rate below 1% and to minimize the false negative rate, which the method and system according to the invention make it possible to do effectively.

[0052] One way to improve the quality of neural network training is to increase the proportion of images representing multiple shots in the training datasets to approach perfect parity, where the number of images representing single shots would be equal to the number of images representing multiple shots. This can be achieved using so-called "Data augmentation" methods, in which data is artificially generated, in this case to increase the proportion of images representing multiple shots.Data augmentation can consist of transforming images from the training database using random color distortions by modifying RGB (red, green, blue) colors, random contrast adjustments, random horizontal inversions, random brightness adjustments, random saturation adjustments, rotations, or elastic deformations from random displacement fields.

[0053] Furthermore, the acquisition of IMG2 images acquired from a constant height above an article of interest and relatively close to it (closer than the first image sensor CAM1) allows to feed a training base with IMG2 images of better resolution (for example on the order of 6 to 10 pixels per millimeter) than the IMG1 images, which makes it possible to further improve the quality of the neural network's learning and the relevance of its response when analyzing an IMG2 image by providing images as close as possible to the images obtained in real operating conditions of the SYS system.

[0054] It should be noted that the neural network does not provide definitive answers, but associates an image provided as input with a class defined by the user and by the network's training phase. This association can only be guaranteed with a certain level of probability that depends on the training and the user's requirements. In other words, at the output of step S70, we can only ascertain whether or not the probability of an item exceeding a certain threshold, evaluated according to the neural network's performance following its training.

[0055] At a step S80, in response to the analysis of the image IMG2, and therefore to a probability of multiple capture of the located article, the computer system INF commands the MA unit for individual handling of articles to grasp the located article and move it to a destination chosen from among a reject bin REJ and a second unit INS for collective movement of articles.

[0056] Thus, if the probability of multiple picking exceeds a certain predefined value, then the MA unit for individual item handling grasps the located item and deposits it in the REJ reject bin. If a neural network as described above is used to analyze the IMG2 image, such a probability results in the activation of the neuron corresponding to item detection, leading to multiple picking. At step S90, items accumulated in the REJ reject bin can be returned to the first CONV unit for collective item handling by means of a Bck return action, for a new transmission attempt to the second (INS) unit for collective item handling. Step S90 can be performed manually or by any conventional automated means of item handling.

[0057] Conversely, if the probability of multiple picking is less than a certain predefined value, then the MA unit for individual item handling moves the located item to the second INS unit for collective item movement, where it deposits the item in a designated drop zone. If a neural network as described above is used to analyze the image IMG2, such a probability results in the activation of the neuron corresponding to item detection, leading to single picking. At step S100, the second INS unit for collective movement inserts the deposited item into the SORT sorting machine. its drop zone DZ.

[0058] Figure 4 illustrates a variant 100' of the process 100 illustrated in Figure 3. One advantage of this variant is that it can be implemented using equipment already present on installations configured to detect the presence of a destination address block of the article on a visible face of the article, and to flip it over if necessary to make the address block visible. For steps with the same identifier as a step in process 100, reference can be made to the explanations concerning the latter.

[0059] From an explanatory point of view, variant 100' differs from process 100 in that the image analysis step S70 of image IMG2 is enhanced by an additional test step S74 designed to detect the presence of an address block on a visible face of the article, visible in image IMG2. The enhanced step S70 is identified as step S70' in the diagram of [Fig. 4], during which an Anal2' analysis of image IMG2 is performed.

[0060] During step S70', if, following step S72 (risk assessment of multiple ingestion), it is determined that the located article does not present a priori a risk of multiple ingestion, the INF computer system proceeds to test step S74 based on the same image IMG2. Reference may be made to European patent application published under number EP 3 984 922 AL

[0061] The test step S74 consists of detecting (action indicated by "ADR?" on the diagram of [Fig.4]) whether an address block is present on a visible face of the located article (event indicated by Y on the diagram) or whether, on the contrary, no address block is detected on a visible face of the article (event indicated by N on the diagram).

[0062] Steps S72 and S74 illustrate the detection principle, which could be implemented using two separate tests as illustrated by steps S72 and S74 in the diagram in [Fig. 4]. Step S74 could also be implemented using a second neural network according to a principle similar to that detailed for step S72, using annotated images indicating the presence or absence of the address block as training data.

[0063] Another method consists of implementing the Anal2' analysis using a neural network such as the one mentioned for the implementation of step S72, this time defining three output classes of the network: a first for items likely to lead to multiple captures, a second for items not likely to lead to multiple captures but without a visible address block, and a third for items not likely to lead to multiple captures and with a visible address block. In this case, the classification of the item located in the first, second, or third class constitutes the image analysis step S70'. IMG2. The neural network is trained using a bank of images annotated to indicate their class of belonging among the three classes defined above.

[0064] At a step S80', the INF computer system controls the individual item handling unit (MA) in response to the analysis step S70' of the image IMG2.

[0065] In the case where the probability of multiple capture is greater than a certain predefined value (i.e. the image is considered by the neural network as belonging to the first class), then the MA unit for individual item handling grasps the located item and deposits it in the reject bin REJ.

[0066] In the case where the probability is less than the certain predefined value and the address block is not detected on a visible face of the localized article (i.e. the image is considered by the neural network as belonging to the second class), then the MA unit for individual article handling moves the localized article to an RET unit for article flipping, which flips the article and transmits it to the second INS unit for collective article movement.

[0067] In the case where the probability is less than the certain predefined value and if the address block is detected on a visible face of the graspable article (i.e. the image is considered by the neural network as belonging to the third class), then the MA unit of individual article handling moves the localized article to the second INS unit of collective article movement.

[0068] In this document, the figures are not necessarily to scale. Certain features and components may be shown exaggerated in relation to other components or in a somewhat schematic form, and certain details of conventional elements may not be shown in the interest of clarity and conciseness.

[0069] Of course the invention is not limited to the described embodiment and alternative embodiments can be made without departing from the scope of the invention as defined by the claims.

Claims

Demands

1. A method (100) for the individual handling of articles capable of managing the risk of simultaneous picking of more than one article, the method comprising the steps of: - transporting (S20) articles on a first unit (CONV) for the collective movement of articles and bringing them to a picking zone (PickZ); - acquiring (S30) an image (IMG1) of the picking zone (PickZ) by means of a first image sensor (CAM1); - analyzing (S40) the image (IMG1) of the picking zone by means of a computer system (INF) so as to locate an article (IT) in the picking zone; - in response to the analysis of the image of the picking zone, the computer system (INF) commands (S50) an individual article handling unit (MA) equipped with a second image sensor (CAM2), so as to position the second image sensor above the located article;- acquire (S60) an image (IMG2) of the located item using the second image sensor (CAM2) positioned above the located item; - analyze (S70, S70') the image (IMG2) of the located item using the computer system (INF) in order to assess the risk of multiple picking if the located item is picked up by the individual item handling unit (MA), and in particular to assess whether the risk of multiple picking is associated with a probability lower or higher than a certain predefined value; - in response to the analysis of the image of the located item, command (S80, S80') the individual item handling unit (MA) to pick up the located item and move it to a destination (INS, RET, REJ) chosen from at least one reject bin (REJ) and a second collective item handling unit (INS).

2. Method (100) according to claim 1, wherein the image (IMG2) of the localized article has a resolution greater than a resolution of the image (IMG1) of the sampling area (PickZ), these resolutions being able to be expressed in pixels per unit length.

3. A method (100) according to claim 1 or 2, wherein, if the probability is greater than a certain predefined value, then the unit (MA) individual handling of articles grasps the located article and places it in the reject bin (REJ).

4. Method (100) according to any one of claims 1 to 3, wherein, if the probability is less than a certain predefined value, then the individual item handling unit (MA) moves the located item to a second collective item moving unit (INS).

5. Method (100') according to any one of claims 1 to 3, wherein the analysis of the image (IMG2) of the located article includes a step (S74) of determining whether an address block (AD) is located on a visible face of the located article.

6. Method (100') according to claim 5, wherein, if the computer system determines (S70') that the probability is less than a certain predefined value and that the address block is not located on a visible face of the located item, then the individual item handling unit (MA) moves the located item to an item turning unit (RET).

7. Method (100') according to claim 6, wherein the item return unit (RET) receives and returns the item brought to it and then moves (S90') the returned item to the second item collective movement unit (CONV).

8. Method (100') according to claim 4, wherein, if the probability is less than a certain predefined value and if the address block is located on a visible face of the graspable article, then the individual article handling unit (MA) moves the located article to a second collective article moving unit (INS).

9. Method (100, 100') according to claim 4 or 8, wherein the second item collective displacement unit (INS) is a sorting machine inserter (SORT).

10. Method (100, 100') according to any one of the preceding claims, wherein, during the acquisition (S60) of the image (IMG2) of the localized article, the second image sensor (CAM2) is positioned at a predefined height above the localized article in question.

11. Individual item handling system (SYS) capable of managing a risk of simultaneous handling of more than one item and configured to implement the process according to any one of the preceding claims, the system comprising: - a first unit (CONV) for collective item movement; - a first image sensor (CAM1) configured to acquire images of a picking zone (PickZ) of the first unit (CONV) of collective item movement, - a second unit (INS) for the collective movement of articles; - an individual item handling unit (MA), equipped with a second image sensor (CAM2) and configured to move items (IT) from the first item handling unit (CONV) to the second item handling unit (INS); and - a computer device (INF) configured to control the individual item handling unit (MA), the computer device being functionally connected to the first image sensor (CAM1) and the second image sensor (CAM2), and configured to control the individual item handling unit (MA) in response to data generated by the first sensor (CAM1) and the second sensor (CAM2).

12. System according to claim 11, further comprising an item return unit (RET) configured to return an item brought to it and move the returned item to the second item group movement unit (CONV).