Robot Execution Research Articles

Learning situation-dependent costs: Improving planning from probabilistic robot execution

Physical domains are notoriously hard to model completely and correctly, especially to capture the dynamics of the environment. In this article, we present Rogue, a robot that learns from its execution experiences. Since actions may have different costs under different conditions, we introduce the concept of situation-dependent rules, in which situational features are attached to the costs or probabilities, reflecting patterns and dynamics encountered in the environment. Rogue extracts learning opportunities from massive, continual, probabilistic execution traces. It then correlates these learning opportunities with environmental features, creating situation-dependent costs for its actions. We present the development and use of these rules for a robotic path planner. Our empirical results show that situation-dependent rules effectively improve the planner’s model of the environment, thus allowing the planner to predict and avoid failures, to create plans that are tailored to the real world, and to respond to a changing environment.

Interleaving Planning and Robot Execution for Asynchronous User Requests

ROGUE is an architecture built on a real robot which provides algorithms for the integration of high-level planning, low-level robotic execution, and learning. ROGUE addresses successfully several of the challenges of a dynamic office gopher environment. This article presents the techniques for the integration of planning and execution. ROGUE uses and extends a classical planning algorithm to create plans for multiple interacting goals introduced by asynchronous user requests. ROGUE translates the planner‘s actions to robot execution actions and monitors real world execution. ROGUE is currently implemented using the PRODIGY4.0 planner and the Xavier robot. This article describes how plans are created for multiple asynchronous goals, and how task priority and compatibility information are used to achieve appropriate efficient execution. We describe how ROGUE communicates with the planner and the robot to interleave planning with execution so that the planner can replan for failed actions, identify the actual outcome of an action with multiple possible outcomes, and take opportunities from changes in the environment. ROGUE represents a successful integration of a classical artificial intelligence planner with a real mobile robot.

Sensor explication: knowledge-based robotic plan execution through logical 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.

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.

Toward automatic robot instruction from perception-mapping human grasps to manipulator grasps

Our approach of programming a robot is by direct human demonstration. The system observes a human performing the task, recognizes the human grasp, and maps it onto the manipulator. This paper describes how an observed human grasp can be mapped to that of a given general-purpose manipulator for task replication. Planning the manipulator grasp based upon the observed human grasp is done at two levels: the functional and physical levels. Initially, at the functional level, grasp mapping is achieved at the virtual finger level; the virtual finger is a group of fingers acting against an object surface in a similar manner. Subsequently, at the physical level, the geometric properties of the object and manipulator are considered in fine-tuning the manipulator grasp. Our work concentrates on power or enveloping grasps and the fingertip precision grasps. We conclude by showing an example of an entire programming cycle from human demonstration to robot execution.

Adaptive Control for Robotic Rebar Bending

: Computers are becoming essential tools for integrating design, planning, fabrication, and construction. Computer-controlled construction equipment is poised to receive information from data generated in the design phase. The robotic execution of construction operations, however, depends on complex models for autonomous and intelligent control. This paper introduces a strategy for the robotic bending of rebar and presents the results of experimental work with an actual testing facility. Mechanical properties and behavior of rebar are examined to develop rules for control. Focus is placed on exploring the dynamics between the bender and steel bars. The concepts of impedance, admittance, and compliance of both the machine and the environment will be discussed in the context of control strategy development.

Stability and control aspects of flexible link robot manipulators during constrained motion tasks

AbstractThe effect of robotic manipulator structural compliance on system stability and trajectory tracking performance and the compensation of this structural compliance has been the subject of a number of publications for the case of robotic manipulator noncontact task execution. The subject of this article is the examination of dynamics and stability issues of a robotic manipulator modeled with link structural flexibility during execution of a task that requires the robot tip to contact fixed rigid objects in the work environment. The dynamic behavior of a general n degree of freedom flexible link manipulator is investigated with a previously proposed nonlinear computed torque constrained motion control applied, computed based on the rigid link equations of motion. Through the use of techniques from the theory of singular perturbations, the analysis of the system stability is investigated by examining the stability of the “slow” and “fast” subsystem dynamics. The conditions under which the fast subsystem dynamics exhibit a stable response are examined. It is shown that if certain conditions are satisfied a control based on only the rigid link equations of motion will lead to asymptotic trajectory tracking of the desired generalized position and force trajectories during constrained motion. Experiments reported here have been carried out to investigate the performance of the nonlinear computed torque control law during constrained motion of the manipulator. While based only on the rigid link equations of motion, experimental results confirm that high‐frequency structural link modes, exhibited in the response of the robot, are asymptotically stable and do not destabilize the slow subsystem dynamics, leading to asymptotic trajectory tracking of the overall system. © 1992 John Wiley & Sons, Inc.

Mobile robot path planning and execution based on a diffusion equation strategy

This paper discusses a method for map and sensor-based planning and execution, i.e. navigation, of smooth collision-free robot motion paths. Application of an unsteady diffusion equation strategy t...

High Speed Optical Scanning Techniques for Dimensional Measurement

Recently, human–robot cooperation (HRC) research activities have focused on the development of new methodologies for the generation of safe robot trajectories. However, the applicability of such methodologies in a real context is limited due to the inherent uncertainty of robot trajectory execution time (i.e. the robot can avoid the worker by modifying its velocity along the path). This paper proposes an approach to estimate a confidence interval on robot trajectory execution time for scenarios in which human–robot space sharing is required. First, human arm movements are studied for a given set of assembly collaborative tasks: worker occupancy volumes and occupancy volume probabilities are derived. Then, a finite number of alternative robot trajectories, crossing human occupancy volumes with different occupancy probabilities, are generated. It is therefore possible to estimate a probability for the robot to reduce its velocity, and a confidence interval on the robot execution time. The application on a real assembly case is discussed.