Optimizing robotic device performance

Abstract

A computer-implemented method for optimizing robotic device performance includes receiving, by a processor, environmental data (104) associated with one or more robotic devices (110) and an environment (102), where the one or more robotic devices (110) are configured to perform one or more activities in the environment (102); analyzing the environment data (104) and one or more activities in the environment (102); identifying one or more performance factors associated with the environment (102); generating one or more simulations associated with the one or more performance factors in the environment (102) and the one or more robotic devices (110); and applying the one or more protection components (114A-114N) to the one or more robotic devices, wherein applying the one or more protection components (114A-114N) to the one or more robotic devices (110) is based at least in part on the one or more simulations. A system and a computer program product are also provided.

Description

Background Art

[0001] Aspects of the present disclosure relate generally to the field of artificial intelligence, and more particularly to using robotic devices to perform various activities.

[0002] As the technology associated with robotics has advanced, a greater understanding of how robots can be applied to different tasks and industrial operations has also developed. The field of robotics has been used to revolutionize the manufacturing and assembly of various objects from computer components to buildings. Robotic devices can also be used to perform various activities in environments that are difficult to access and / or unsafe for people to enter, such as those with extreme temperatures. Summary of the invention

[0003] Embodiments of the present disclosure include methods, computer program products, and systems for optimizing performance of robotic devices.

[0004] The processor may receive environmental data associated with one or more robotic devices and an environment. The one or more robotic devices may be configured to perform one or more activities in the environment. The processor may analyze the environmental data and the one or more activities in the environment. The processor may identify one or more performance factors associated with the environment. The processor may generate one or more simulations associated with the one or more performance factors in the environment and the one or more robotic devices. The processor may apply one or more protection components to the one or more robotic devices. Applying the one or more protection components to the one or more robotic devices may be based at least in part on the one or more simulations.

[0005] The above summary is not intended to describe each illustrated embodiment or every implementation of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The accompanying drawings included in the present disclosure are incorporated into the specification and form a part of the specification. They show embodiments of the present disclosure and are used to explain the principles of the present disclosure together with the specification. The accompanying drawings only illustrate certain embodiments and do not limit the present disclosure.

[0007] Figure 1 A block diagram of an example robotic system in accordance with aspects of the present disclosure is shown.

[0008] Figure 2 A flow chart of an example method for optimizing performance of a robotic device in a smart environment according to aspects of the present disclosure is shown.

[0009] Figure 3A A cloud computing environment according to aspects of the present disclosure is shown.

[0010] Figure 3B Abstract model layers in accordance with aspects of the present disclosure are shown.

[0011] Figure 4 A schematic block diagram of an example computer system that may be used to implement one or more of the methods, tools, and modules described herein, and any related functionality, according to aspects of the present disclosure is shown.

[0012] Although the embodiments herein may have various modifications and alternative forms, the details thereof have been shown by way of example in the accompanying drawings and will be described in detail. However, it should be understood that the specific embodiments described should not be understood as limiting. On the contrary, the present invention will encompass all modifications, equivalents, and alternatives that fall within the scope of the present disclosure. DETAILED DESCRIPTION

[0013] Aspects of the present disclosure generally relate to the field of artificial intelligence, and more specifically, to using robotic devices to perform various activities. Although the present disclosure is not necessarily limited to such applications, various aspects of the present disclosure may be understood through discussion of various examples using this context.

[0014] Robotics has revolutionized how various activities are performed. Although robotic devices are often used in manufacturing or assembly processes, they can also be used to perform specific actions in different environments, such as those that are unsafe or inaccessible to humans. For example, some manufacturing processes may require that a product be in an acidic environment while performing specific steps of a chemical process. Although an acidic environment may be unsafe for a person to enter to retrieve the finished product, a robotic device (e.g., a robotic arm) can be used to enter an acidic environment and retrieve the finished product. Although various embodiments contemplated herein relate to the use of a robotic arm or other mechanism, these embodiments are intended to be examples only and should not be construed as limitations.

[0015] Unfortunately, although the robotic device may be able to enter an environment that is unsafe or inaccessible to humans, in some environments, the robotic device may suffer some damage due to entering a specific environment (e.g., an environment with extreme temperatures) or performing a specific activity (e.g., where the robotic device assists in performing a chemical process). In order to cope with the damage that may be caused to the robotic device when entering a specific environment or performing a specific activity, the robotic device may be configured with various protective devices or protective equipment (e.g., protective modules) that protect the robotic device from environmental influences. Such protective devices / protective equipment usually add extra weight to the robotic device. When the robotic device performs a specific activity in the environment, the extra weight may affect the effectiveness and productivity of the robotic device. When the robotic device may require multiple protective devices / protective equipment in order to perform a specific activity in the environment, this extra weight becomes a greater problem. Therefore, a solution is desired, which provides the benefit of enabling the robotic device to enter a specific environment to perform one or more activities, while actively protecting the robotic device using the optimal amount of protective devices / protective equipment that will not affect the productivity or efficiency of the robotic device performing the activity.

[0016] Before turning to the drawings, it is noted that the benefits / novelty and complexity of the proposed solution are:

[0017] The robotic system can be configured to analyze the activities around the context of the one or more robots to identify whether any of the one or more robots may suffer any damage while performing activities in the assigned surrounding environment. Based on the identified predicted damage, the robotic system can provide appropriate protection (e.g., one or more protection modules) to the one or more robots to prevent damage to the one or more robots.

[0018] The robotic system can be configured to perform visual and Internet of Things (IoT) feed analysis (e.g., using environmental data) of an environment of interest (e.g., where one or more robotic devices will perform one or more activities). The robotic system can use this information (e.g., environmental data) to identify types of environmental and operating parameters and types and / or amounts of damage or potential problems that the robotic device may encounter when performing one or more activities in the environment of interest. The robotic system can generate one or more simulations (e.g., via a digital twin engine and / or a simulation engine) to identify appropriate levels of protection (e.g., protection modules) and apply them to one or more robotic devices.

[0019] The robotic system can be configured to generate one or more digital twins to simulate various outcomes contemplated herein. The digital twins can be modified to correspond to an environment, various activities performed in the environment, and / or one or more robotic devices that can interact with the environment.

[0020] In situations where the environment and current state (e.g., performance factors) are not determined to be disruptive or negatively impacting (e.g., robotic hazards), the robotic system can be configured to reduce to remove one or more protective components from the robotic device. Removing one or more protective components from the robotic device reduces the weight and load of the robotic device, thereby enabling the robotic device to perform one or more activities with greater efficiency.

[0021] Based on environmental data (e.g., performance factors) and one or more activities associated with the environment, one or more robotic devices within the robotic system may be configured to cooperate with each of the other robotic devices. For example, a robotic device may be configured to communicate with other robotic devices in the environment, and a robotic device may require additional protective components (e.g., cooling jackets, shock absorbers, etc.) to complete one or more activities in the environment.

[0022] Based on the context of the surrounding environment (e.g., the environment of interest), the robotic system can identify performance factors or scenario types that may cause damage to one or more robotic devices. In this way, the robotic system can be configured to identify how one or more activities should be sequenced (e.g., optimal sequence) to minimize or reduce the aggregate protection cost associated with one or more robotic devices performing the one or more activities in the environment.

[0023] Reference now Figure 1 , a block diagram of an example robotic system 100 for optimizing robotic assembly according to aspects of the present disclosure is shown. For control. Figure 1 The illustration of only one implementation is provided and does not imply any limitation on the environments in which different embodiments may be implemented. Many modifications to the described environments may be made by those skilled in the art without departing from the scope of the invention as set forth in the claims.

[0024] like Figure 1 As shown, the robotic system 100 can be configured to include an environment 102, environmental data 104, a digital twin engine 106, and a protection module 108. In various embodiments, the environment 102 can refer to any space or area in which one or more activities can be performed. For example, the environment 102 can include, but is not limited to, a room, an industrial floor of a factory, or an open outdoor building site. One or more active objects can include any activity that can be performed by a robotic device, such as a robotic arm (e.g., a robotic device) that performs assembly of a product within the environment 102. The environment 102 can be configured to include a robotic device 110 and one or more smart devices 112A-N. Although the embodiments herein may generally refer to the use of a single robotic device, any number of robotic devices 110 can be used to perform one or more activities.

[0025] In embodiments, one or more robotic devices 110 may include any type of robotic technology that can be used to perform one or more activities in the environment 102. For example, the robotic device 110 may be a robotic arm configured to move throughout the environment 102 and enter the exit environment 102 independently of human interaction. While in some embodiments, the robotic system 100 may configure one or more robotic devices 110 to work independently within the environment 102, in other embodiments, the robotic system 100 may configure one or more robotic devices 110 to echo other robotic devices. In these embodiments, one or more robotic devices 110 may be configured to transmit various information to other robotic devices within the environment 102. The information may include, but is not limited to, various technical capabilities of the robotic device, information associated with the environment 102 (e.g., potential robotic hazards or the need for protective components), and the current ability of the robotic device to perform one or more activities (e.g., the robotic device is damaged in some way). For example, in some embodiments, some robotic devices 110 may be configured with a robotic arm, while other robotic devices 110 may be configured with a robotic swarm or as part of a robotic swarm.

[0026] In an embodiment, the robotic system 100 may be configured to receive environmental data 104 from the environment 102. The environmental data 104 may be associated with one or more robotic devices 110 and the environment 102. In an embodiment, the robotic system 100 may configure the one or more robotic devices 110 to perform one or more activities in the environment 102. The environmental data may include, but is not limited to, information or data associated with: i) the configuration of the environment 102 (e.g., factory / worksite layout, dimensions of the environment); ii) the number and type of robotic devices 110 that may be used within the environment 102 to perform one or more activities (e.g., capabilities and configurations of the robotic devices 110 and / or robotic mechanisms); iii) the location / positioning of each robotic device 110, iv) performance factors associated with the environment (e.g., information associated with what influences in the environment may affect one or more robotic devices 110); v) the number and type of different tasks that may be associated with performing one or more activities; vi) information associated with the robotic devices 110 performing one or more activities (e.g., possible influences and / or robot hazards); vii) real-time information associated with one or more activities in the environment 102; viii) information associated with data associated with the protection module 108 and each of the one or more protection components 114A-N (e.g., protection component capabilities, weight of the protection component, etc.); ix) how the protection components 114A-N can be used when performing one or more activities; x) information / data generated from various analyses contemplated herein (e.g., information / data generated by AI and machine learning analyses via the digital twin 106 and / or simulation engine 116); and xi) a database with information / data associated with one or more activities, which may be the same or similar to activities performed in the environment 102, such as data related to how much time is required to perform various tasks or steps of each of the one or more activities or how particular changes in the environment 102 may affect one or more robotic devices 110 (e.g., when one or more robotic devices 110 are in the environment 102).

[0027] In an embodiment, the robotic system 100 may be configured to store the environmental data 104 collected over time in a historical repository. The historical repository may include any environmental data 104 contemplated herein. In an embodiment, the robotic system 100 may access the historical repository to generate one or more simulations using AI and machine learning capabilities (e.g., digital twin 106 / simulation engine 116). The information generated from these analyses may be considered assembly data and may also be stored in a historical repository.

[0028] In an embodiment, the robotic system 100 can analyze the environmental data 104 using a simulation engine 116 (e.g., via the digital twin engine 106) that is enabled to perform AI and machine learning analysis. While in some embodiments, the robotic system 100 can receive one or more assembly operations (e.g., assembly data) associated with assembling an object of interest from a database, in other embodiments, the robotic system 100 can analyze historical environmental data from a historical repository and determine one or more performance factors that may be required to assemble or construct an object of interest (e.g., via one or more simulations). In some embodiments, the robotic system 100 can configure the simulation engine 116 (e.g., via the digital twin engine 106) to generate one or more simulations of the environment 102 using the environmental data 104. These simulations can be based on the received environmental data collected in real time and / or retrieved from the historical repository.

[0029] In an embodiment, the robotic system 100 may be configured to receive / collect environmental data 104 from one or more smart devices 112A-N. The smart devices 112A-N may include, but are not limited to, devices such as Internet of Things (IoT) devices (e.g., devices with IoT scanning modules enabled), cameras, infrared sensors, ultrasound, chemical sensors, or any combination thereof. In an embodiment, the robotic system 100 may configure one or more smart devices 112A-N to receive / collect environmental data 104 associated with the environment 102 in real time and / or collect environmental data 104 over a specific duration. Such environmental data 104 may be stored in a historical repository and may be accessed by the robotic system 100 as needed by the digital twin engine 106 or the simulation engine 116 (e.g., when performing various simulations / analyses contemplated herein using AI and machine learning capabilities). While some smart devices 112A-N may be configured within the environment 102, in some embodiments, other smart devices 112A-N may be further configured within or associated with one or more robotic devices 110.

[0030] In embodiments, the robot system 100 may be configured to analyze the environmental data 104. In these embodiments, the robot system 100 may use a digital twin engine 106 to analyze and generate one or more simulations using one or more digital twins. The digital twin engine 106 may be implemented using AI and machine learning techniques. The digital twin engine 106 may generate one or more digital twins associated with the environment 102, one or more robot devices 110, a protection module 108, the environmental data 104, or any combination thereof. Although in some embodiments, the digital twin engine 106 may be configured to perform any and all analyses contemplated herein, in other embodiments, the digital twin engine 106 may be configured to also include a simulation engine 116 (e.g., enabled using AI and machine learning techniques). The simulation engine 116 may be configured to generate one or more simulations using one or more digital twins generated by the digital twin engine 106. In some embodiments, the simulation engine 116 may replace the digital twin engine 106 and be configured by the robot system 100 to perform any of the various analyses contemplated herein.

[0031] In an embodiment, the robotic system 100 may include a protection module 108. The protection module 108 may include one or more protection components 114A-N or any other element (e.g., a coolant, a protective coating spray, etc.) that may be used by the robotic system 100 to protect or prevent the robotic device 110 from being damaged (e.g., affected by one or more performance factors) while performing one or more activities in the environment 102. The one or more protection components 114A-N may include, but are not limited to, flame retardant devices, shock absorbers, protective sheaths (e.g., configured to prevent damage associated with acidic or alkaline environments), cooling devices, or any combination thereof. While in some embodiments, the protection module 108 may include additional devices that may be used to couple (e.g., attach, spray, etc.) or disconnect one or more protection components 114A-N to one or more robotic devices 110, in other embodiments, the robotic device 110 may be configured by the robotic system 100 to self-apply one or more protection components 114A-N.

[0032] In an embodiment, the robotic system 100 may analyze the environmental data 104 to identify one or more performance factors (e.g., operational and / or environmental parameters) associated with the environment (e.g., using the digital twin engine 106). A performance factor may refer to any aspect of the environment 102 that may affect how one or more robotic devices 110 perform activities in the environment 102. For example, performance factors may include, but are not limited to: i) environments 102 with extremely high or low temperatures, such as those resulting from the environment 102 being submerged in flames or having sub-zero temperatures (e.g., during a manufacturing process using components that are only stable at low temperatures); ii) surfaces of varying heights (e.g., the robotic device 110 may need to navigate a surface to perform necessary activities); iii) chemically contaminated environments, such as those that may be acidic or corrosive; or iv) any combination thereof. Although the embodiments contemplated herein generally refer to the environment 102 as having a single performance factor, the environment 102 may include any number of performance factors. While in some embodiments, the environment 102 may have specific areas with different performance factors (e.g., one area may have different surfaces at different heights that the robotic device 110 may have to navigate, while another area has extreme temperatures), in other embodiments, multiple performance factors may be associated with the entire environment 102 (e.g., all of the environment 102 is engulfed in flames and has high concentrations of specific corrosive chemicals). In some embodiments, the simulation engine 116 may use historical environmental data stored in a historical repository (e.g., environmental data 104) to generate a knowledge corpus to help identify new or previously unidentified performance factors or changes to the environment 102 (e.g., how the robotic device may be affected, damaged, or otherwise affected when performing one or more activities). In these embodiments, the robotic system 100 may use the knowledge base to further identify one or more of the protection components 114A-N that will mitigate the effects associated with the performance factors (e.g., previously unidentified performance factors).

[0033] In embodiments, the robotic system 100 may generate one or more simulations associated with one or more robotic devices 110 and the identified one or more performance factors in the environment 102 (e.g., via the digital twin engine 106 and / or the simulation engine 116). In these embodiments, the robotic system 100 may use these simulations to determine one or more effects of the performance factors associated with the environment 102 on the one or more robotic devices 110. Using the above simulations, the robotic system 100 may determine the effects of the one or more performance factors on the robotic hazard. When one or more robotic devices 110 are negatively affected (e.g., damaged) by one or more performance factors, the robotic hazard may occur. In embodiments, the robotic system 100 may simulate the identified robotic hazard and the environmental data 104 associated with the protection module 108 (e.g., one or more protection components 114A-N) to identify whether the protection module 108 can mitigate the robotic hazard. If the protection module 108 can mitigate the robotic hazard (e.g., has a specific protection component that can protect the robotic device 110 from the robotic hazard), the robotic system 100 may apply the protection (e.g., the specific protection component) from the protection module 108 to the one or more robotic devices 110.

[0034] In embodiments, the robotic system 100 may consider the environmental data 104 associated with one or more protection components 114A-N associated with the protection module 108. In these embodiments, the robotic system 100 may simulate and determine whether the one or more protection components 114A-N mitigate or eliminate the effects of the robotic hazards (e.g., negatively impacting performance factors) of the environment 102 on the one or more robotic devices 110.

[0035] In embodiments where the robotic system 100 determines (e.g., the digital twin engine 106 and / or the simulation engine 116) that one or more protection components 114A-N may mitigate or reduce the impact of a robotic hazard (e.g., one or more performance factors), the robotic system 100 may apply the one or more protection components 114A-N to the one or more robotic devices 110 before the robotic devices 110 enter the environment 102. For example, in embodiments where the robotic system 100 receives environmental data 104 associated with the environment 102 (e.g., from one or more smart devices 112A-N) and identifies that the environment 102 has a high temperature (e.g., a performance factor), the robotic system 100 may apply a flame retardant cover (e.g., a protection component) to the one or more robotic devices 110 before the robotic devices enter the environment 102. By identifying one or more performance factors or robotic hazards that may affect the one or more robotic devices 110, the robotic system 100 may prevent the robotic devices from being affected (e.g., being negatively affected) while performing one or more activities in the environment 102.

[0036] In some embodiments, the robotic system 100 may analyze the environmental data 104 and determine that one or more robotic devices 110 already have a particular protective component from among the one or more protective components 114A-N applied to the robotic device 110. In these embodiments, the robotic system 100 may analyze the environmental data 104 associated with the robotic device 110 and the particular protective component to determine whether the currently applied particular protective component mitigates or eliminates the effects of the one or more robotic hazards. In embodiments where the particular protective component does not mitigate or eliminate the effects of at least one of the one or more robotic hazards on the robotic device 110, the robotic system 100 may remove the particular protective component. In some embodiments, the robotic system 100 may replace the particular protective component with a different protective component from among the one or more protective components 114A-N that is identified as mitigating or eliminating the effects of the robotic hazard (e.g., performance factor) on the robotic device.

[0037] In some embodiments, the robotic system 100 may be configured to simulate how one or more robotic devices 110 perform one or more activities in the environment 102 (e.g., the simulation engine 116). In these embodiments, the robotic system 100 may use the one or more simulations to identify an optimal amount of one or more protective components 114A-N for each of the one or more robotic devices 110 that perform the one or more activities. Although it may seem easier for the robotic device 110 to include as many protective components 114A-N as possible in order to adequately protect the robotic device, the additional weight and possible reduction in movement caused by the constraints of the protective components (e.g., a cooling jacket may reduce the ability of the robotic arm to move freely) often results in an increase in time and resources associated with performing the one or more activities (e.g., requiring more battery charging or additional robotic manipulation to overcome range of motion issues).

[0038] In this way, by providing each of the one or more robotic devices 110 with an optimal number of one or more protective components 114A-N (e.g., sufficient to prevent damage to the robotic device without adding unnecessary weight), the robotic system 100 ensures that one or more activities performed by the robotic devices 110 in the environment 102 are performed in a productive and efficient manner. While in some embodiments, all robotic devices have the same number or type of one or more protective components 114A-N, in other embodiments, the optimal number of one or more protective components 114A-N may differ between one or more robotic devices 110. In these embodiments, the optimal number of one or more protective components 114A-N may differ based on the type of one or more of the robotic devices 110 and / or what tasks or processes the robotic devices may be performing when performing the one or more activities. For example, some robotic devices 110 may perform activities that require a robotic arm to assemble something, while other robotic devices may be mobile robotic devices configured to move objects and materials into and out of the environment 102.

[0039] In some embodiments, the robotic system 100 may base the optimal amount on a particular sequence of activities for one or more activities. For example, in embodiments performing more than one activity, the robotic device 110 may encounter more than one robotic hazard (e.g., negatively impacting a performance factor of the robotic device 110) at different times. In these embodiments, the robotic system 100 may apply and remove different protective components from one or more robotic devices 110 while the robotic devices 110 are performing one or more activities. For example, the robotic system 100 may instruct one or more robotic devices 110 configured with a shock absorbing device and a flame retardant cover (e.g., protective components 114A-N) to remove the flame retardant cover after performing one activity, but continue to utilize the shock absorbing device while performing the next activity.

[0040] In some embodiments, the robotic system 100 may base an optimal amount of one or more protective components 114A-N applied to one or more robotic devices 110 on a specific duration associated with each of one or more activities and / or tasks that each of the one or more robotic devices 110 may be required to perform in association with the specific activity. The robotic system 100 may use a simulation engine 116 (e.g., a digital twin engine 106) to generate a simulation associated with the optimal amount of one or more protective components 114A-N. In some example embodiments, some robotic devices 110 may occupy the environment 102 for a longer duration to perform one or more activities, and thus have prolonged exposure to one or more performance factors (e.g., robotic hazards) associated with the environment 102. Due to this prolonged exposure, the robotic devices may have different or multiple types of protective components (e.g., optimal amounts) of one type. For example, a robotic device that will be exposed to high temperatures and flames for a longer duration than another robotic device may have multiple types of heat and fire resistant equipment, while the other robotic device may have a single layer of coolant spray.

[0041] In embodiments, the robotic system 100 may analyze environmental data 104 associated with the environment 102 in real time. Using the real-time environmental data 104, the robotic system 100 may utilize the digital twin engine 106 to identify whether one or more performance factors associated with the environment 102 have changed. In some embodiments, the identified changes in the performance factors may be identified as new robotic hazards. Examples of changes may include, but are not limited to, temperature changes, changes in environmental structure (e.g., the environment 102 is a building in the process of collapse), changes in chemical concentrations (e.g., the environment 102 is no longer highly acidic), etc. In these embodiments, the robotic system 100 may simulate the changes and one or more robotic devices to determine the impact of the changes on the one or more robotic devices (e.g., whether the changes are robotic hazards). The robotic system 100 may use the one or more simulations to determine whether the changes in the performance factors may require additional protective components or fewer protective components. In these embodiments, the robotic system 100 may dynamically update one or more protective components 114A-N applied to one or more robotic devices 110.

[0042] Reference now Figure 2 , a flowchart according to an embodiment of the present disclosure illustrates an example method 200 for optimizing the performance of a robotic device. Figure 2 The illustration of only one implementation is provided and does not imply any limitation on the environments in which different embodiments may be implemented. Many modifications to the described environments may be made by those skilled in the art without departing from the scope of the invention as set forth in the claims.

[0043] In some embodiments, method 200 begins at operation 202, where a processor may receive environmental data associated with one or more robotic devices and an environment. In embodiments, one or more robotic devices may be configured to perform one or more activities in the environment. In some embodiments, method 200 proceeds to operation 204.

[0044] At operation 204 , the processor may analyze the environmental data and one or more activities in the environment. In some embodiments, method 200 proceeds to operation 206 .

[0045] At operation 206 , the processor may identify one or more performance factors associated with the environment. In some embodiments, method 200 may proceed to operation 208 .

[0046] At operation 208 , the processor may generate one or more simulations associated with the one or more performance factors and the one or more robotic devices in the environment. In some embodiments, method 200 may proceed to operation 210 .

[0047] At operation 210, the processor may apply one or more protection components to one or more robotic devices. In some embodiments, applying one or more protection components to one or more robotic devices may be based at least in part on one or more simulations. In some embodiments, Figure 2 As depicted in , after operation 208 , method 200 may end.

[0048] In some embodiments, discussed below are one or more operations of method 200, which are not depicted for the sake of brevity and are discussed in the present disclosure. Thus, in some embodiments, the processor may analyze environmental data associated with the environment in real time to identify that a change associated with the environment has occurred. In these embodiments, the change and one or more robotic devices may be used to determine the impact of the change on the one or more robotic devices. In some embodiments, the processor dynamically updates one or more protection components applied to the one or more robotic devices.

[0049] In some embodiments, the processor may simulate the impact of one or more performance factors on one or more robotic devices performing one or more activities to identify the impact of the performance factors on the one or more robotic devices. The processor may then determine whether the impact is a robotic hazard. In these embodiments, the processor may further simulate the robotic hazard and one or more protection components. This may identify a specific protection module of the one or more protection components configured to mitigate the robotic hazard. The processor may then apply the specific protection module to the one or more robotic devices.

[0050] In some embodiments, when applying one or more protection components to one or more robotic devices, the processor may simulate the one or more robotic devices performing one or more activities to identify an optimal amount of the one or more protection modules for each of the one or more robotic devices. In these embodiments, the optimal amount may be associated with a particular activity sequence of the one or more activities.

[0051] In some embodiments, when applying one or more protection components to one or more robotic devices, the processor may simulate the one or more robotic devices performing one or more activities to identify an optimal amount of the one or more protection modules for each of the one or more robotic devices. In these embodiments, the optimal amount may be associated with a specific duration associated with each of the one or more activities.

[0052] It should be understood that although the present disclosure includes detailed descriptions about cloud computing, the implementation of the teachings set forth herein is not limited to cloud computing environments. Instead, the embodiments of the present disclosure can be implemented in conjunction with any other type of computing environment now known or later developed.

[0053] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be quickly provisioned and released with minimal management effort or interaction with the service provider. The cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0054] Features are as follows:

[0055] On-demand self-service: Cloud consumers can unilaterally and automatically provision computing capabilities, such as server time and network storage, as needed without requiring manual interaction with the service provider.

[0056] Wide Area Network Access: Capabilities are available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0057] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated based on demand. There is a sense of partial independence, as consumers typically do not control or know the exact portion of the resources provided, but may be able to specify portions at a higher level of abstraction (e.g., country, state, or data center).

[0058] Rapid elasticity: In some cases, the ability to scale out quickly and in quickly can be provisioned quickly and elastically. To the consumer, the capacity available for provisioning often appears to be unlimited and can be purchased in any quantity at any time.

[0059] Metered Services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of the utilized services.

[0060] The service model is as follows:

[0061] Software as a Service (SaaS): The capability provided to the consumer is to use the provider's applications running on the cloud infrastructure. The applications are accessible from a variety of client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including the network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0062] Platform as a Service (PaaS): The capability provided to consumers is to deploy consumer-created or acquired applications onto cloud infrastructure, where the applications are created using programming languages ​​and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possible configuration of the application hosting environment.

[0063] Infrastructure as a Service (IaaS): The capabilities provided to consumers are processing, storage, networking, and other basic computing resources on which consumers can deploy and run arbitrary software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but have control over the operating system, storage, deployed applications, and possibly limited control over selected networking components (e.g., host firewalls).

[0064] The deployment model is as follows:

[0065] Private Cloud: The cloud infrastructure is operated only for the organization. It can be managed by the organization or a third party and can exist inside or outside the building.

[0066] Community cloud: The cloud infrastructure is shared by several organizations and supports a specific community with shared concerns (e.g., mission, security requirements, policies, and compliance considerations). It can be managed by the organization or a third party and can exist on-premises or off-premises.

[0067] Public cloud: Cloud infrastructure is available to the general public or large industrial groups and is owned by an organization that sells cloud services.

[0068] Hybrid cloud: A cloud infrastructure is a combination of two or more clouds (private, community or public) that remain unique entities but are bound together by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing between clouds).

[0069] The cloud computing environment is service-oriented, with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the core of cloud computing is an infrastructure consisting of a network of interconnected nodes.

[0070] Figure 3A A cloud computing environment 310 is shown. As shown, the cloud computing environment 310 includes one or more cloud computing nodes 300 with which a local computing device used by a cloud consumer can communicate, such as a personal digital assistant (PDA) or cell phone 300A, a desktop computer 300B, a laptop computer 300C, and / or an automobile computer system 300N. The nodes 300 can communicate with each other. They can be physically or virtually grouped (not shown) in one or more networks, such as a private cloud, community cloud, public cloud, or hybrid cloud, or a combination thereof, as described above.

[0071] This allows the cloud computing environment 310 to provide infrastructure, platform and / or software as a service for which cloud consumers do not need to maintain resources on local computing devices. Figure 3A The types of computing devices 300A-N shown in are intended to be illustrative only, and computing node 300 and cloud computing environment 310 may communicate with any type of computing device over any type of network and / or network addressable connection (eg, using a web browser).

[0072] Figure 3B The cloud computing environment 310 ( Figure 3A ) provides a set of functional abstraction layers. It should be understood in advance that Figure 3B The components, layers and functions shown in are intended to be illustrative only, and the embodiments of the present disclosure are not limited thereto. As described below, the following layers and corresponding functions are provided.

[0073] The hardware and software layer 315 includes hardware and software components. Examples of hardware components include: host 302; server 304 based on RISC (Reduced Instruction Set Computer) architecture; server 306; blade server 308; storage device 311; and network and networking components 312. In some embodiments, software components include network application server software 314 and database software 316.

[0074] Virtualization layer 320 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual servers 322 ; virtual storage 324 ; virtual networks 326 , including virtual private networks; virtual applications and operating systems 328 ; and virtual clients 330 .

[0075] In one example, the management layer 340 may provide the functionality described below. Resource provisioning 342 provides dynamic procurement of computing resources and other resources used to perform tasks within a cloud computing environment. Metering and pricing 344 provides cost tracking when resources are utilized in a cloud computing environment, as well as accounting or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides authentication for cloud consumers and tasks, as well as protection for data and other resources. User portal 346 provides access to the cloud computing environment for consumers and system administrators. Service level management 348 provides cloud computing resource allocation and management so that the required service levels are met. Service level agreement (SLA) planning and fulfillment 350 provides pre-scheduling and procurement of cloud computing resources, where future demand is anticipated based on the SLA.

[0076] Workload layer 360 provides examples of functionality that can take advantage of a cloud computing environment. Examples of workloads and functionality that can be provided from this layer include: mapping and navigation 362; software development and lifecycle management 364; virtual classroom education delivery 366; data analytics processing 368; transaction processing 370; and robotics optimization 372.

[0077] Figure 4 A high-level block diagram of an example computer system 401 according to an embodiment of the present disclosure is shown, which computer system can be used to implement one or more of the methods, tools, and modules herein and any related functionality (e.g., using one or more processor circuits of a computer or a computer processor). In some embodiments, the main components of the computer system 401 may include one or more CPUs 402, a memory subsystem 404, a terminal interface 412, a storage interface 416, an I / O (input / output) device interface 414, and a network interface 418, all of which may be directly or indirectly communicatively coupled for inter-component communication via a memory bus 403, an I / O bus 408, and an I / O bus interface unit 410.

[0078] Computer system 401 may include one or more general purpose programmable central processing units (CPUs) 402A, 402B, 402C, and 402D, collectively referred to herein as CPUs 402. In some embodiments, computer system 401 may include multiple processors typical of relatively large systems; however, in other embodiments, computer system 401 may instead be a single CPU system. Each CPU 402 may execute instructions stored in memory subsystem 404 and may include one or more levels of onboard cache.

[0079] System memory 404 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 422 or cache memory 424. Computer system 401 may also include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 426 may be provided for reading from and writing to non-removable, non-volatile magnetic media such as "hard drives". Although not shown, a disk drive for reading from and writing to a removable, non-volatile disk (e.g., "floppy disk") may be provided, or an optical drive or other optical medium for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM, etc. may be provided. In addition, memory 404 may include flash memory, such as a flash stick drive or a flash drive. The memory device may be connected to memory bus 403 via one or more data medium interfaces. Memory 404 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of different embodiments.

[0080] One or more programs / utilities 428 may be stored in memory 404, each having at least one set of program modules 430. Programs / utilities 428 may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data, or some combination thereof, may include an implementation of a networking environment. Programs 428 and / or program modules 430 typically perform the functions or methods of various embodiments.

[0081] Although the memory bus 403 is Figure 4403, but in some embodiments, the memory bus 403 may include multiple different buses or communication paths, which may be arranged in any of a variety of forms, such as hierarchical point-to-point links, star or mesh configurations, multi-layer buses, parallel and redundant paths, or any other suitable type of configuration. In addition, although the I / O bus interface 410 and the I / O bus 408 are shown as a single corresponding unit, in some embodiments, the computer system 401 may include multiple I / O bus interface units 410, multiple I / O buses 408, or both. In addition, although multiple I / O interface units are shown that separate the I / O bus 408 from the various communication paths to various I / O devices, in other embodiments, some or all of the I / O devices may be directly connected to one or more system I / O buses.

[0082] In some embodiments, computer system 401 may be a multi-user mainframe computer system, a single-user system, or a server computer or similar device having little or no direct user interface but receiving requests from other computer systems (clients). In addition, in some embodiments, computer system 401 may be implemented as a desktop computer, a portable computer, a laptop or notebook computer, a tablet computer, a pocket computer, a phone, a smart phone, a network switch or router, or any other suitable type of electronic device.

[0083] Notice, Figure 4 The main components of the exemplary computer system 401 are intended to be depicted. However, in some embodiments, the various components may have more Figure 4 The greater or lesser complexity represented in , may exist different from Figure 4 Components may be those shown in or in addition to those shown in, and the number, type, and configuration of these components may vary.

[0084] As discussed in greater detail herein, it is contemplated that some or all of the operations of some embodiments of the methods described herein may be performed in an alternate order or may not be performed at all; furthermore, multiple operations may occur concurrently or as internal parts of a larger process.

[0085] The present disclosure may be a system, method and / or computer program product at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or multiple media) having computer-readable program instructions thereon, and the computer-readable program instructions are used to cause a processor to perform aspects of the present disclosure.

[0086] A computer-readable storage medium may be a tangible device capable of retaining and storing instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device such as a punch card or a raised structure in a groove on which instructions are recorded, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be interpreted as a temporary signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (e.g., a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0087] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in a computer-readable storage medium within the corresponding computing / processing device.

[0088] The computer-readable program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data of an integrated circuit, or source code or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages, such as Smalltalk, C++, etc.) and procedural programming languages ​​(such as "C" programming language or similar programming languages). The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider through the Internet). In some embodiments, an electronic circuit including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions to personalize the electronic circuit by utilizing the state information of the computer-readable program instructions, so as to perform aspects of the present disclosure.

[0089] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It will be understood that each block of the flowchart and / or block diagram and the combination of blocks in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0090] These computer-readable program instructions can be provided to a processor of a computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create a device for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can guide the computer, programmable data processing device and / or other equipment to work in a specific manner, so that the computer-readable storage medium having the instructions stored therein includes an article of manufacture, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0091] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0092] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure.In this regard, each frame in the flow chart or block diagram can represent a module, segment or part of an instruction, which includes one or more executable instructions for realizing the specified logical function.In some alternative embodiments, the function noted in the frame may not occur in the order noted in the figure.For example, the two frames shown in succession can actually be implemented as a step, and are performed simultaneously, substantially simultaneously, in a partially or entirely time-overlapping manner, or these frames can sometimes be performed in reverse order, depending on the function involved.It will also be noted that the combination of each frame of the block diagram and / or flow chart illustration and the frame in the block diagram and / or flow chart illustration can be implemented by a dedicated hardware-based system that performs a specified function or action or performs a combination of special-purpose hardware and computer instructions.

[0093] The description of various embodiments of the present disclosure has been presented for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or technical improvements existing in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

[0094] Although the present disclosure has been described in terms of specific embodiments, it is contemplated that changes and modifications thereof will become apparent to those skilled in the art. Therefore, the appended claims are intended to be interpreted as covering all such changes and modifications that fall within the true scope of the present disclosure.

Claims

1. A computer-implemented method, the method comprising: receiving, by a processor, environmental data associated with one or more robotic devices and an environment, wherein the one or more robotic devices are configured to perform one or more activities in the environment; analyzing the environment data and the one or more activities in the environment; identifying one or more performance factors associated with the environment; generating one or more simulations associated with the one or more performance factors and the one or more robotic devices in the environment; as well as One or more protection components are applied to the one or more robotic devices, wherein applying the one or more protection components to the one or more robotic devices is based at least in part on the one or more simulations.

2. The method according to claim 1, further comprising: analyzing said environmental data associated with said environment in real time; identifying that a change associated with the environment has occurred; as well as The change and the one or more robotic devices are simulated to determine an effect of the change on the one or more robotic devices.

3. The method according to claim 2, further comprising: The one or more protection components applied to the one or more robotic devices are dynamically updated.

4. The method according to claim 1, further comprising: simulating an effect of the one or more performance factors on the one or more robotic devices performing the one or more activities; identifying an impact of the performance factor on the one or more robotic devices; and The impact is determined to be a robot hazard.

5. The method according to claim 4, comprising: simulating the robot hazard and the one or more protective components; identifying a particular protective component of the one or more protective components that is configured to mitigate a hazard to the robot; as well as Applying the specific protection component to the one or more robotic devices.

6. The method of claim 1, wherein applying the one or more protection components to the one or more robotic devices comprises: simulating the one or more robotic devices to perform the one or more activities; as well as An optimal amount of the one or more protection modules for each of the one or more robotic devices is identified, wherein the optimal amount is associated with a particular sequence of activities among the one or more activities.

7. The method of claim 1, wherein applying the one or more protection components to the one or more robotic devices comprises: simulating the one or more robotic devices to perform the one or more activities; as well as An optimal amount of the one or more protection modules for each of the one or more robotic devices is identified, wherein the optimal amount is associated with a specific duration associated with each of the one or more activities.

8. A system, comprising: Memory; as well as a processor in communication with the memory, the processor configured to perform operations including: receiving environment data associated with one or more robotic devices and an environment, wherein the one or more robotic devices are configured to perform one or more activities in the environment; analyzing the environment data and the one or more activities in the environment; identifying one or more performance factors associated with the environment; generating one or more simulations associated with the one or more performance factors and the one or more robotic devices in the environment; as well as One or more protection components are applied to the one or more robotic devices, wherein applying the one or more protection components to the one or more robotic devices is based at least in part on the one or more simulations.

9. The system according to claim 8, further comprising: analyzing said environmental data associated with said environment in real time; identifying that a change associated with the environment has occurred; as well as The change and the one or more robotic devices are simulated to determine an effect of the change on the one or more robotic devices.

10. The system according to claim 9, further comprising: The one or more protection components applied to the one or more robotic devices are dynamically updated.

11. The system according to claim 8, further comprising: simulating an effect of the one or more performance factors on the one or more robotic devices performing the one or more activities; identifying an impact of the performance factor on the one or more robotic devices; and The impact is determined to be a robot hazard.

12. The system according to claim 11, comprising: simulating the robot hazard and the one or more protective components; identifying a particular protective component of the one or more protective components that is configured to mitigate a hazard to the robot; as well as Applying the specific protection component to the one or more robotic devices.

13. The system of claim 8, wherein applying the one or more protection components to the one or more robotic devices comprises: simulating the one or more robotic devices to perform the one or more activities; as well as An optimal amount of the one or more protection modules for each of the one or more robotic devices is identified, wherein the optimal amount is associated with a particular sequence of activities among the one or more activities.

14. The system of claim 8, wherein applying the one or more protection components to the one or more robotic devices comprises: simulating the one or more robotic devices to perform the one or more activities; as well as An optimal amount of the one or more protection modules for each of the one or more robotic devices is identified, wherein the optimal amount is associated with a specific duration associated with each of the one or more activities.

15. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions being executable by a processor to cause the processor to perform functions, the functions comprising: receiving environment data associated with one or more robotic devices and an environment, wherein the one or more robotic devices are configured to perform one or more activities in the environment; analyzing the environment data and the one or more activities in the environment; identifying one or more performance factors associated with the environment; generating one or more simulations associated with the one or more performance factors and the one or more robotic devices in the environment; as well as One or more protection components are applied to the one or more robotic devices, wherein applying the one or more protection components to the one or more robotic devices is based at least in part on the one or more simulations.

16. The computer program product of claim 15, further comprising: analyzing said environmental data associated with said environment in real time; identifying that a change associated with the environment has occurred; as well as The change and the one or more robotic devices are simulated to determine an effect of the change on the one or more robotic devices.

17. The computer program product of claim 15, further comprising: The one or more protection components applied to the one or more robotic devices are dynamically updated.

18. The computer program product of claim 15, further comprising: simulating an effect of the one or more performance factors on the one or more robotic devices performing the one or more activities; identifying an impact of the performance factor on the one or more robotic devices; and The impact is determined to be a robot hazard.

19. The computer program product according to claim 18, comprising: simulating the robot hazard and the one or more protective components; identifying a particular protective component of the one or more protective components that is configured to mitigate a hazard to the robot; as well as Applying the specific protection component to the one or more robotic devices.

20. The computer program product of claim 15, wherein applying the one or more protection components to the one or more robotic devices comprises: simulating the one or more robotic devices to perform the one or more activities; as well as An optimal amount of the one or more protection modules for each of the one or more robotic devices is identified, wherein the optimal amount is associated with a particular sequence of activities among the one or more activities.

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