Robot programming system for assembly: Conceptual graph-based approach

This work develops a method for robot program synthesis. Currently, the programming task is one of the major hurdles of robotic application. Progress towards automatic synthesis of robot programs will ease industrial robot application. The proposed system provides a means towards automated (guided by knowledge) conversion of a user's request, expressed in natural language, to the appropriate conceptual model of the required task. This model incorporates the information necessary for understanding, planning, and sensory-guided performance of the required robotic task.First, we state the problem of robotic assembly and recognize its hierarchic structure as the structure of a system that builds a predesigned assembly. Next, we present and analyze the requirements of the robotic assembly domain. This analysis enables us to draw conclusions concerning the most suitable methodology for the development of a support system for assembly program synthesis and interpretation. It is the conceptual graph-based approach. Then we present the algorithm of the proposed conceptual graph-based system and show how the system synthesizes robotic assembly operations, such as valid assembly sequences and sequences of special treatments for the assembled components (including the determination of the required resources). Finally, a case study illustrates the approach developed here on a large family of multi-axisymmetric components. We also present illustrative examples of working sessions with the current implementation of the system.     

Sensor explication: knowledge-based robotic plan execution throughlogical objects

Complex robot tasks are usually described as high level goals, with no details on how to achieve them. However, details must be provided to generate primitive commands to control a real robot. A sensor explication concept that makes details explicit from general commands is presented. We show how the transformation from high-level goals to primitive commands can be performed at execution time and we propose an architecture based on reconfigurable objects that contain domain knowledge and knowledge about the sensors and actuators available. Our approach is based on two premises: 1) plan execution is an information gathering process where determining what information is relevant is a great part of the process; and 2) plan execution requires that many details are made explicit. We show how our approach is used in solving the task of moving a robot to and through an unknown, and possibly narrow, doorway; where sonic range data is used to find the doorway, walls, and obstacles. We illustrate the difficulty of such a task using data from a large number of experiments we conducted with a real mobile robot. The laboratory results illustrate how the proper application of knowledge in the integration and utilization of sensors and actuators increases the robustness of plan execution.     

Information system structure for a task level robot assembly language for on-line program generation

The increasing industrial need for production automation in one of a kind production and increasing demand for one of a kind or small batch production requires that automation is carried out by programmable automatic equipment in order to ensure that this equipment can be rapidly and easily adjusted and configured to the ever changing production tasks. In order that the adjustment and reconfiguration of the production equipment can be carried out quickly, reliably and in compatibility with the state of the production plant at the time of task execution it is necessary that the equipment tasks can be specified by a specification language, which is much more abstract than the task specification languages that are today commercially available for machine tool and robot programming, and that the corresponding compiler can be controlled by the system controlling the sequence of operations of the assembly plant. To comply with this need a task level robot programming language for assembly operations has been developed with a level of abstraction corresponding to the level of abstraction for the command: Mount part A. The compiler of the language retrieves information about placement and orientation of the parts to be assembled from a database. The information is supplied to the database by proper sensors mounted on the assembly plant, implying that the program which is generated corresponds to the state of the physical plant at program execution time. Additionally, making available information of permitted production assembly sequences has enabled the compiler to be controlled by a system that optimizes the sequence in which a particular assembly should be carried out respecting present availability of resources needed for the assembling tasks.In this paper the designed robot language, the corresponding compiler and the necessary information structures are presented.     

Task Planning For A Mobile Robot In An Indoor Environment Using Object-oriented Domain Information

For a mobile robot to be practical, it needs to navigate in dynamically changing environments and manipulate objects in the environment with operating ease. The main challenges to satisfying these requirements in mobile robot research include the collection of robot environment information, storage and organization of this information, and fast task planning based on available information. Conventional approaches to these problems are far from satisfactory due to their requirement of high computation time. In this paper, we specifically address the problems of storage and organization of the environment information and fast task planning in the area of robotic research. We propose an special object-oriented data model (OODM) for information storage and management in order to solve the first problem. This model explicitly represents domain knowledge and abstracts a global perspective about the robot's dynamically changing environment. To solve the second problem, we introduce a fast task planning algorithm that fully uses domain knowledge related to robot applications and to the given environment. Our OODM based task planning method presents a general frame work and representation, into which domain specific information, domain decomposition methods and specific path planners can be tailored for different task planning problems. This method unifies and integrates the salient features from various areas such as database, artificial intelligence, and robot path planning, thus increasing the planning speed significantly     

Integration of Heterogeneity for Human-Friendly Robotic Operations

The objective of this paper is to develop an analytical scheme to integrate the heterogeneity of human and robot functions to achieve a human-friendly robotic operations. The heterogeneity of human and robot functions can be characterized by the fact that humans are intelligent while robots are fast, powerful and accurate. Humans can use their knowledge and experience to quickly respond to unexpected events, which makes it easy for humans to deal with unstructured environments. In contrast, robots can easily enhance the mechanical power of humans and the ability of humans to work remotely. Therefore, robots are capable of performing precise and repetitive tasks at high speed or in a hazardous environment. The important issue, in light of human/robot heterogeneity, is how to plan and control a robotic operation such that the human and the robot can cooperate in a complementary manner. Thus, a task which cannot be done by either human or robot alone can be performed efficiently and robustly by both. This paper introduces a new paradigm for human/robot interactive systems, heterogeneous function-based human/robot cooperation. A new perceptive action reference frame has been developed in the paper. It matches human perception and robot sensory measurement, and provides a platform for modeling the human/robot cooperative operations. The theoretical results presented in the paper have laid down a foundation for stability analysis as well as a planning and control system design of human/robot integrated systems. The implementations and experimental results have clearly demonstrated the advantages of proposed methods.     

Error management for robot programming

Reliability is a serious problem in computer controlled robot systems. Although robots serve successfully in relatively simple applications such as painting and spot welding, their potential in areas such as automated assembly is hampered by the complexity of programming. A program for assembling parts may be logically correct, execute correctly on a simulator, and even execute correctly on a robot most of the time, yet still fail unexpectedly in the face of real world uncertainties. Recovery from such errors is far more complicated than recovery from simple controller errors, since even expected errors can manifest themselves in unexpected ways. In this paper we present a novel approach for improving robot reliability. Instead of anticipating errors, we use knowledge-based programming techniques so that the robot can autonomously exploit knowledge about its task and environment to detect and recover from failures. We describe a system that we have designed and constructed in our robotics laboratory.     

Data Partitioning for Parallel Spatial Join Processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. In this paper we show that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.     

On the design of intelligent robotic agents for assembly

Robotic agents can greatly be benefited from the integration of perceptual learning in order to monitor and adapt to changing environments. To be effective in complex unstructured environments, robots have to perceive the environment and adapt accordingly. In this paper it is discussed a biology inspired approach based on the adaptive resonance theory (ART) and implemented on an KUKA KR15 industrial robot during real-world operations (e.g. assembly operations). The approach intends to embed naturally the skill learning capability during manufacturing operations (i.e., within a flexible manufacturing system).The integration of machine vision and force sensing has been useful to demonstrate the usefulness of the cognitive architecture to acquire knowledge and to effectively use it to improve its behaviour. Practical results are presented, showing that the robot is able to recognise a given component and to carry out the assembly. Adaptability is validated by using different component geometry during assemblies and also through skill learning which is shown by the robot’s dexterity.     

Task identification and characterisation in mobile robotics through non-linear modelling

The lack of a theory-based design methodology for mobile robot control programs means that control programs have to be developed through an empirical trial-and-error process. This can be costly, time consuming and error prone.In this paper we show how to develop a theory of robot–environment interaction, which would overcome the above problem. We show how we can model a mobile robot’s task (so-called “task identification”) using non-linear polynomial models (NARMAX), which can subsequently be formally analysed using established mathematical methods. This provides an understanding of the underlying phenomena governing the robot’s behaviour.Apart from the paper’s main objective of formally analysing robot–environment interaction, the task identification process has further benefits, such as the fast and convenient cross-platform transfer of robot control programs (“Robot Java”), parsimonious task representations (memory issues) and very fast control code execution times.     

An algorithm for cooperative task allocation in scalable,constrained multiple robot systems

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system.     

Robotic assembly automation using robust compliant control

Industrial robots used to perform assembly applications are still a small portion of total robot sales each year. One of the main reasons is that it is difficult for conventional industrial robots to adapt to any sort of change. This paper proposes a robust control strategy to perform an assembly task of inserting a printed circuit board (PCB) into an edge connector socket using a SCARA robot. The task is very challenging because it involves compliant manipulation in which a substantial force is needed to accomplish the insertion operation and there are some dynamic constraints from the environment. Therefore, a robust control algorithm is developed and used to perform the assembly process. The dynamic model of the robotic system is developed and the dynamic parameters are identified. Experiments were performed to validate the proposed method. Experimental results show that the robust control algorithm can deal with parameter uncertainties in the dynamic model, thus achieve better performance than the model based control method. An abnormal case is also investigated to demonstrate that the robust compliant control method can deal with the abnormal situation without damaging the system and assembly parts, while pure position control method may cause damages. This strategy can also be used in other similar assembly processes with compliant applications.     

Diagnosis and repair of dependent failures in the control system of a mobile autonomous robot

Detecting, locating and repairing faults is a hard task. This holds especially in cases where dependent failures occur in practice. In this paper we present a methodology which is capable of handling dependent failures. For this purpose we extend the model-based diagnosis approach by explicitely representing knowledge about such dependencies which are stored in a failure dependency graph. Beside the theoretical foundations we present algorithms for computing diagnoses and repair actions that are based on these extensions. Moreover, we introduce a case study which makes use of a larger control program of an autonomous and mobile robot. The case study shows that the proposed approach can be effectively used in practice.     

On-line motion synthesis and adaptation using a trajectory database

Autonomous robots cannot be programmed in advance for all possible situations. Instead, they should be able to generalize the previously acquired knowledge to operate in new situations as they arise. A possible solution to the problem of generalization is to apply statistical methods that can generate useful robot responses in situations for which the robot has not been specifically instructed how to respond. In this paper we propose a methodology for the statistical generalization of the available sensorimotor knowledge in real-time. Example trajectories are generalized by applying Gaussian process regression, using the parameters describing a task as query points into the trajectory database. We show on real-world tasks that the proposed methodology can be integrated into a sensory feedback loop, where the generalization algorithm is applied in real-time to adapt robot motion to the perceived changes of the external world.     

Symbolic and numeric knowledge integration in multiple fault troubleshooting

Many expert system researchers have reported in recent years that situation-action symbolic production rules frequently fail to provide adequate knowledge representation schemes without resorting to numeric computation. However, despite the need to integrate symbolic and quantitative computation into one coherent framework of knowledge, surprisingly few architectures have been proposed for achieving this goal. This paper explores the integration of qualitative and numeric processing in expert systems. We address the topic with respect to the construction of expert systems that perform the tasks of design and multiple fault troubleshooting. This paper shows that these tasks can be handled effectively when an appropriate interface is established between the heuristic and the numeric knowledge-based components. Specifically, we demonstrate how to interface heuristic knowledge with non-linear optimization models in order to allow an expert system greater expressiveness. An actual example is presented from the machining domain.     

A scheduling algorithm based on the singular value decomposition heuristic method in a distributed manufacturing system

Because distributed manufacturing technology is the foundation of modernized production and traditional heuristic methods exhibit problems of high complexity and low efficiency, this paper designs a scheduling algorithm based on the singular value decomposition heuristic (SVDH) method. The algorithm uses the device distribution and the transportation relationship between devices in a distributed manufacturing system. The algorithm takes the sequence relationship between tasks and the distance between devices as the implicit relationship between the task and the device. The algorithm makes use of the implicit relationship to amend the processing time matrix of the task and corrects the processing time matrix that contains the transportation relationship. Singular value decomposition principal component analysis is performed on the corrected processing time to find the most suitable processing device for each process, and an initial solution matrix is established. The heuristic solution is used to optimize the initial solution to find the optimal scheduling result based on the initial solution matrix. The establishment of the initial solution can effectively reduce the computational complexity of the heuristic solution, realize a parallelizing solution, and improve the efficiency of the heuristic solutions. In addition, the SVDH scheduling result has a lower transfer time between devices due to the consideration of the topology of tasks and devices, that is, the transit time. In this paper, the experiments are conducted on the heuristic performance, scheduling results, and transportation time. The experimental results show the advantages of SVDH over general heuristic algorithms in terms of efficiency and transit time.     

Logical sensor/actuator : knowledge-based robotic plan execution

Complex tasks are usually described as high-level goals, leaving out the details on how to achieve them. However, to control a robot, the task must be described in terms of primitive commands for the robot. Having the robot move itself to and through an unknown, and possibly narrow, doorway is an example of such a task. It is shown how the transformation from high-level goals to primitive commands can be performed at execution time and an architecture is proposed based on reconfigurable objects that contain domain knowledge and knowledge about the sensors and actuators available. The approach is illustrated using actual data from a real robot.     

Autonomous and fast robot learning through motivation

Research on robot techniques that are fast, user-friendly, and require little application-specific knowledge by the user, is more and more encouraged in a society where the demand of home-care or domestic-service robots is increasing continuously. In this context we propose a methodology which combines reinforcement learning and genetic algorithms to teach a robot how to perform a task when only the specification of the main restrictions of the desired behaviour is provided. Through this combination, both paradigms must be merged in such a way that they influence each other to achieve a fast convergence towards a good robot-control policy, and reduce the random explorations the robot needs to carry out in order to find a solution.Another advantage of our proposal is that it is able to easily incorporate any kind of domain-dependent knowledge about the task. This is very useful for improving a robot controller, for applying a robot-controller to move a different robot-platform, or when we have certain “feelings” about how the task should be solved.The performance of our proposal is shown through its application to solve a common problem in mobile robotics.     

Hierarchically self-organizing visual place memory

A hierarchically organized visual place memory enables a robot to associate with its respective knowledge efficiently. In this paper, we consider how this organization can be done by the robot on its own throughout its operation and introduce an approach that is based on the agglomerative method SLINK. The hierarchy is obtained from a single link cluster analysis that is carried out based on similarity in the appearance space. As such, the robot can incrementally incorporate the knowledge of places into its visual place memory over the long term. The resulting place memory has an order-invariant hierarchy that enables both storage and construction efficiency. Experimental results obtained under the guided operation of the robot demonstrate that the robot is able to organize its place knowledge and relate to it efficiently. This is followed by experimental results under autonomous operation in which the robot evolves its visual place memory completely on its own.