Planning in highly dynamic environments: an anytime approach for planning under time constraints

In this paper, we present a novel and domain-independent planner aimed at working in highly dynamic environments with time constraints. The planner follows the anytime principles: a first solution can be quickly computed and the quality of the final plan is improved as long as time is available. This way, the planner can provide either fast reactions or very good quality plans depending on the demands of the environment. As an on-line planner, it also offers important advantages: our planner allows the plan to start its execution before it is totally generated, unexpected events are efficiently tackled during execution, and sensing actions allow the acquisition of required information in partially observable domains. The planning algorithm is based on problem decomposition and relaxation techniques. The traditional relaxed planning graph has been adapted to this on-line framework by considering information about sensing actions and action costs. Results also show that our planner is competitive with other top-performing classical planners.     

Direct Virtual-hand Interface in Robot Assembly Programming

For a long time, robot assembly programming has been produced in two environments: on-line and off-line. On-line robot programming uses the actual robot for the experiments performing a given task; off-line robot programming develops a robot program in either an autonomous system with a high-level task planner and simulation or a 2D graphical user interface linked to other system components. This paper presents a whole hand interface for more easily performing robotic assembly tasks in the virtual tenvironment. The interface is composed of both static hand shapes (states) and continuous hand motions (modes). Hand shapes are recognized as discrete states that trigger the control signals and commands, and hand motions are mapped to the movements of a selected instance in real-time assembly. Hand postures are also used for specifying the alignment constraints and axis mapping of the hand-part coordinates. The basic virtual-hand functions are constructed through the states and modes developing the robotic assembly program. The assembling motion of the object is guided by the user immersed in the environment to a path such that no collisions will occur. The fine motion in controlling the contact and ending position/orientation is handled automatically by the system using prior knowledge of the parts and assembly reasoning. One assembly programming case using this interface is described in detail in the paper.     

Two-handed assembly with immersive task planning in virtual reality

Assembly modelling is the process of capturing entities and activity information related to assembling and assembly. Currently, most CAD systems have been developed to ease the design of individual components, but are limited in their support for assembly designs and planning capability, which are crucial for reducing the cost and processing time in complex design, constraint analysis and assembly task planning. This paper presents a framework of a two-handed virtual assembly (VA) planner for assembly tasks, which coordinates two hands jointly for feature-based manipulation, assembly analysis and constraint-based task planning. Feature-based manipulation highlights the important assembling features (e.g. dynamic reference frames, moving arrow, mating features) to guide users for the ease of assembly and in an efficient and fluid manner. The users can freely navigate and move the mating pair along the collision-free path. The free motion of two-handed input in assembly is further restricted to the allowable motion guided by the constraints recognised on-line. The allowable motion in assembly is planned by the logic steps derived from the analysis of constraints and their translation in the progress of assembly. No preprocessing or predefined assembly sequence is necessary since the planning is produced in real-time upon the two-handed interactions. Mating features and constraints in databases are automatically updated after each assembly to simplify the planning process. The two-handed task planner has been developed and experimented for several assembly examples including a drill (12-parts) and a robot (17-parts). The system can be generally applied for the interactive task planning of assembly-type applications.     

Development of a configuration space motion planner for robot in dynamic environment

In this paper, the on-line motion planning of articulated robots in dynamic environment is investigated. We propose a practical on-line robot motion planning approach that is based upon pre-computing the global configuration space (C-space) connectivity with respect to all possible obstacle positions. The proposed motion planner consists of an off-line stage and an on-line stage. In the off-line stage, the obstacles in the C-space (C-obstacle) with respect to the obstacle positions in the workspace are computed, which are then stored using a hierarchical data structure with non-uniform 2^m trees. In the on-line stage, the real obstacle cells in the workspace are identified and the corresponding 2^m trees from the pre-computed database are superposed to construct the real-time C-space. The collision-free path is then searched in this C-space by using the A* algorithm under a multi-resolution strategy which has excellent computational efficiency. In this approach, the most time-consuming operation is performed in the off-line stage, while the on-line computing only need to deal with the real-time obstacles occurring in the dynamic environment. The minimized on-line computational cost makes it feasible for real-time on-line motion planning. The validity and efficiency of this approach is demonstrated using manipulator prototypes with 5 and 7 degree-of-freedom.     

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.     

基于变维度状态空间的增量启发式路径规划方法研究

在移动机器人路径规划中需要考虑运动几何约束,同时,由于它经常工作于动态、时变的环 境中,因此,还必须保证路径规划算法的效率.本文提出了一种基于变维度状态空间的增量启发式路径规划 方法,该方法既能满足移动机器人的运动几何约束,又能保证规划算法的效率.首先,设计了变维度状态空间, 在机器人周围的局部区域考虑运动几何约束组织高维状态空间,其他区域组织低维状态空间;然后,基于变维 度状态空间,提出了一种增量启发式路径规划方法,该方法在新的规划进程中可以使用以前的规划结果,仅对 机器人周围的局部区域进行重搜索,从而能保证算法的增量性及实时性;最后,通过仿真计算和机器人实验验 证了算法的有效性.     

Whole-body motion planning and control of a quadruped robot for challenging terrain

Quadruped robots working in jungles, mountains or factories should be able to move through challenging scenarios. In this paper, we present a control framework for quadruped robots walking over rough terrain. The planner plans the trajectory of the robot's center of gravity by using the normalized energy stability criterion, which ensures that the robot is in the most stable state. A contact detection algorithm based on the probabilistic contact model is presented, which implements event-based state switching of the quadruped robot legs. And an on-line detection of contact force based on generalized momentum is also showed, which improves the accuracy of proprioceptive force estimation. A controller combining whole body control and virtual model control is proposed to achieve precise trajectory tracking and active compliance with environment interaction. Without any knowledge of the environment, the experiments of the quadruped robot SDUQuad-144 climbs over significant obstacles such as 38 cm high steps and 22.5 cm high stairs are designed to verify the feasibility of the proposed method.     

Reorientation planning for a multifingered hand based on an orientation states network using regrasp primitives

This paper concerns the reorientative manipulation planning for a grasped object by a multifingered robotic hand. This manipulation is characterized as the one in which the grasped object is reoriented by the hand without being released, in order to achieve a certain desired final grasping orientation. Two primitives (rotation and pivoting) effectively perform this task by sequentially executing them. A planner which generates a sequence of the two primitives is presented. Two salient features of the planner are that it plans by considering the geometrical shape of the object to be manipulated and that based on a proposed states network it works in two phases. In its first off-line phase, executed once only for the given object, it constructs a structured states network composed of all those orientation states of the object at which at least one of the rotation or pivoting primitives is feasible. This states network serves as road-map for sequence planning. The second on-line phase of the planner can be repeatedly provoked to find the sequenced route on the network between any two given states—corresponding to the initial and goal orientation of the grasped object. A program has been developed for the proposed planner.     

Modeling and configuration-independent control of a self-mobile space manipulator

TheSelf-Mobile Space Manipulator (SM ²) is a 5-DOF, 1/3-scale, laboratory version of a robot designed to walk on the trusswork and other exterior surfaces of Space Station Freedom. It will be capable of routine tasks such as inspection, parts transportation and simple maintenance and repair. We have designed and built the robot and gravity compensation system to permit simulated zero-gravity experiments. The control ofSM ² is challenging because of significant structural flexibility, relatively high friction at the joints, positioning error amplified from joint errors due to the long reach, and high performance requirements for general 3-D locomotion. In this paper, we focus on the modeling of the robot system and the design of the control system based on the model. We address the kinematics and dynamic modeling of the 3-D motion ofSM ² and demonstrate the simulation and experimental modal analysis results. The robot dynamic characteristics vary significantly when the robot configuration changes. To consider this effect, we develop a control system that is composed of two basic parts, the model-based part and the servo part. The model-based loop can be updated based on the off-line dynamic model, and the servo control loop is updated by a gain schedule according to the off-line relationship between the closed-loop frequency and the modal frequency estimated from the off-line dynamic model. By taking dynamic variation into account in the controller, the control system is independent of the robot configuration, and the motion performance ofSM ² is greatly enhanced in implementation.     

A DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs

A dynamic data driven adaptive multi-scale simulation (DDDAMS) based planning and control framework is proposed for effective and efficient surveillance and crowd control via UAVs and UGVs. The framework is mainly composed of integrated planner, integrated controller, and decision module for DDDAMS. The integrated planner, which is designed in an agent-based simulation (ABS) environment, devises best control strategies for each function of (1) crowd detection (vision algorithm), (2) crowd tracking (filtering), and (3) UAV/UGV motion planning (graph search algorithm). The integrated controller then controls real UAVs/UGVs for surveillance tasks via (1) sensory data collection and processing, (2) control command generation based on strategies provided by the decision planner for crowd detection, tracking, and motion planning, and (3) control command transmission via radio to the real system. The decision module for DDDAMS enhances computational efficiency of the proposed framework via dynamic switching of fidelity of simulation and information gathering based on the proposed fidelity selection and assignment algorithms. In the experiment, the proposed framework (involving fast-running simulation as well as real-time simulation) is illustrated and demonstrated for a real system represented by hardware-in-the-loop (HIL) real-time simulation integrating real UAVs, simulated UGVs and crowd, and simulated environment (e.g. terrain). Finally, the preliminary results successfully demonstrate the benefit of the proposed dynamic fidelity switching concerning the crowd coverage percentage and computational resource usage (i.e. CPU usage) under cases with two different simulation fidelities.     

Automatic grasp planning for visual-servo controlled roboticmanipulators

Vision based grasping holds great promise for grasping in dynamic environments where the object and/or robot are moving. The paper introduces a grasp planning approach for visual servo controlled robots with a single camera mounted at the end effector. Sensory control, mechanical, and geometrical issues in the design of such an automatic grasp planner are discussed and the corresponding constraints are highlighted. In particular, the integration of visual feature selection constraints is emphasized. Some quality measures are introduced to rate the candidate grasps. The grasp planning strategy and implementation issues in the development of an automatic grasp planner (AGP) are described. Simulation and experimental results are presented to verify the correctness of the approach and its effectiveness in dealing with dynamic situations.     

Autonomous multi-mobile robot system: simulation and implementation using fuzzy logic

In an autonomous multi-mobile robot environment, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the potential value from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.     

Hierarchical planning involving deadlines, travel time, and resources

This paper describes a planning architecture that supports a form of hierarchical planning well suited to applications involving deadlines, travel time, and resource considerations. The architecture is based upon a temporal database, a heuristic evaluator, and a decision procedure for refining partial plans. A partial plan consists of a set of tasks and constraints on their order, duration, and potential resource requirements. The temporal database records the partial plan that the planner is currently working on and computes certain consequences of that information to be used in proposing methods to further refine the plan. The heuristic evaluator examines the space of linearized extensions of a given partial plan in order to reject plans that fail to satisfy basic requirements (e.g., hard deadlines and resource limitations) and to estimate the utility of plans that meet these requirements. The information provided by the temporal database and the heuristic evaluator is combined using a decision procedure that determines how best to refine the current partial plan. Neither the temporal database nor the heuristic evaluator is complete and, without reasonably accurate information concerning the possible resource requirements of the tasks in a partial plan, there is a significant risk of missing solutions. A specification language that serves to encode expectations concerning the duration and resource requirements of tasks greatly reduces this risk, enabling useful evaluations of partial plans. Details of the specification language and examples illustrating how such expectations are exploited in decision making are provided.     

A desired landing points walking method enablinga planner quadruped robot to walk on rough terrain

It is necessary for legged robots to walk stably and smoothly on rough terrain.In this paper,a desired landing points(DLP) walking method based on preview control was proposed in which an off-line foot motion trace and an on-line modification of the trace were used to enable the robot to walk on rough terrain.The on-line modification was composed of speed modification,foot lifting-off height modification,step length modification,and identification and avoidance of unsuitable landing terrain.A planner quadruped robot simulator was used to apply the DLP walking method.The correctness of the method was proven by a series of simulations using the Adams and Simulink.     

Semantic grasping: planning task-specific stable robotic grasps

We present an example-based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode task-related constraints, which we call semantic constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robot hand with respect to the object can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to search along this approach direction and generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics. We show experiments planning semantic grasps on everyday objects and applying these grasps with a physical robot.     

A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming

Robots that work in a proper formation show several advantages compared to a single complex robot, such as a reduced cost, robustness, efficiency and improved performance. Existing researches focused on the method of keeping the formation shape during the motion, but usually neglect collision constraints or assume a simplified model of obstacles. This paper investigates the path planning of forming a target robot formation in a clutter environment containing unknown obstacles. The contribution lies in proposing an efficient path planner for the multiple mobile robots to achieve their goals through the clutter environment and developing a dynamic priority strategy for cooperation of robots in forming the target formation. A multirobot system is set up to verify the proposed method of robot path planning. Simulations and experiments results demonstrate that the proposed method can successfully address the collision avoidance problem as well as the formation forming problem.     

An obstacle avoidance algorithm for robot manipulators based on decision-making force

Obstacle avoidance is a significant skill not only for mobile robots but also for robot manipulators working in unstructured environments. Various algorithms have been proposed to solve off-line planning and on-line adaption problems. However, it is still not able to ensure safety and flexibility in complex scenarios. In this paper, a novel obstacle avoidance algorithm is proposed to improve the robustness and flexibility. The method contains three components: A closed-loop control system is used to filter the preplanned trajectory and ensure the smoothness and stability of the robot motion; the dynamic repulsion field is adopted to fulfill the robot with primitive obstacle avoidance capability; to mimic human’s complex obstacle avoidance behavior and instant decision-making mechanism, a parametrized decision-making force is introduced to optimize all the feasible motions. The algorithms were implemented in planar and spatial robot manipulators. The comparative results show the robot can not only track the task trajectory smoothly but also avoid obstacles in different configurations.     

On Multiple Secondary Task Execution of Redundant Nonholonomic Mobile Manipulators

This paper investigates self-motion control of redundant nonholonomic mobile manipulators, to execute multiple secondary tasks including tip-over prevention, singularity removal, obstacle avoidance and physical limits escape. An extended gradient projection method (EGPM) is proposed to determine self-motion directions, and a real-time fuzzy logic self-motion planner (FLSMP) is devised to generate the corresponding self-motion magnitudes. Unlike the task-priority allocation method and the extended Jacobian method, the proposed scheme is simple to implement and is free from algorithm singularities. The proposed dynamic model is established with consideration of nonholonomic constraints of the mobile platform, interactive motions between the mobile platform and the onboard manipulator, as well as self-motions allowed by redundancy of the entire robot. Furthermore, a robust adaptive neural-network controller (RANNC) is developed to accomplish multiple secondary tasks without affecting the primary one in the workspace. The RANNC does not rely on precise prior knowledge of dynamic parameters and can suppress bounded external disturbance effectively. In addition, the RANNC does not require any off-line training and can ensure the control performance by online adjusting the neural-network parameters through adaptation laws. The effectiveness of the proposed algorithm is verified via simulations on a three-wheeled redundant nonholonomic mobile manipulator.     

Incorporating decision-theoretic planning in a robot architecture

The goal of robotics research is to design a robot to fulfill a variety of tasks in the real world. Inherent in the real world is a high degree of uncertainty about the robot’s behavior and about the world. We introduce a robot task architecture, DTRC, that generates plans with actions that incorporate costs and uncertain effects, and states that yield rewards.The use of a decision-theoretic planner in a robot task architecture is demonstrated on the mobile robot domain of miniature golf. The miniature golf domain shows the application of decision-theoretic planning in an inherently uncertain domain, and demonstrates that by using decision-theoretic planning as the reasoning method in a robot task architecture, accommodation for uncertain information plays a direct role in the reasoning process.