Probabilistic Road Map sampling strategies for multi-robot motion planning

This paper presents a Probabilistic Road Map (PRM) motion planning algorithm to be queried within Dynamic Robot Networks—a multi-robot coordination platform for robots operating with limited sensing and inter-robot communication.

First, the Dynamic Robot Networks (DRN) coordination platform is introduced that facilitates centralized robot coordination across ad hoc networks, allowing safe navigation in dynamic, unknown environments. As robots move about their environment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network.

Second, a fast single-query Probabilistic Road Map (PRM) to be called within the DRN platform is presented that has been augmented with new sampling strategies. Traditional PRM strategies have shown success in searching large configuration spaces. Considered here is their application to on-line, centralized, multiple mobile robot planning problems. New sampling strategies that exploit the kinematics of non-holonomic mobile robots have been developed and implemented. First, an appropriate method of selecting milestones in a PRM is identified to enable fast coverage of the configuration space. Second, a new method of generating PRM milestones is described that decreases the planning time over traditional methods. Finally, a new endgame region for multi-robot PRMs is presented that increases the likelihood of finding solutions given difficult goal configurations.

Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled. This allows robots to navigate safely in environments that are both dynamic and unknown. Simulations and real robot experiments are presented that demonstrate: (1) speed improvements accomplished by the sampling strategies, (2) centralized robot coordination across Dynamic Robot Networks, (3) on-the-fly motion planning to avoid moving and previously unknown obstacles and (4) autonomous robot navigation towards individual goal locations.     

Parametric Planning for Multiple Cooperative Robots

A novel planning strategy, parametric planning, is proposed to negotiate the task-oriented object manipulation of multiple coordinated robots. The approach provides an advantage to improve flexibility of robotic cooperation, in which the desired trajectories in Cartesian space derived from task requirements are converted into the trajectories of robots in joint space for a fixed-coordinated multi-robot system. For this purpose, a parametric cooperative index matrix is introduced to handle the relationship of the input desired Cartesian trajectories and the position of robots. A case study of 2-dimension object-motion trajectory tracking using four robots is presented in the end. It proved that the proposed approach effectively delivers trajectory task requirements to the joint trajectories of robots.     

Self-indexed motion planning

Motion planning is a central problem for robotics. A practical way to address it is building a graph-based representation (a roadmap) capturing the connectivity of the configuration space. The Probabilistic Road Map (PRM) is perhaps the most widely used method by the robotics community based on that idea. A key sub-problem for discovering and maintaining a collision-free path in the PRM is inserting new sample points and connecting them with the $k$-nearest neighbors in the previous set. Instead of following the usual solution of indexing the points and then building the PRM with successive $k$-NN queries, we propose an approximation of the $k$-Nearest Neighbors Graph using the PRM as a self-index. The motivation for this construction comes from the Approximate Proximity Graph (APG), which is an index for searching proximal objects in a metric space. Using this approach the estimation of the $k$-NN is improved while simultaneously reducing the total time and space needed to compute a PRM. We present simulations for high-dimensional configuration spaces with and without obstacles, showing significant improvement over the standard techniques used by the robotics community.     

Learning object deformation models for robot motion planning

In this paper, we address the problem of robot navigation in environments with deformable objects. The aim is to include the costs of object deformations when planning the robot’s motions and trade them off against the travel costs. We present our recently developed robotic system that is able to acquire deformation models of real objects. The robot determines the elasticity parameters by physical interaction with the object and by establishing a relation between the applied forces and the resulting surface deformations. The learned deformation models can then be used to perform physically realistic finite element simulations. This allows the planner to evaluate robot trajectories and to predict the costs of object deformations. Since finite element simulations are time-consuming, we furthermore present an approach to approximate object-specific deformation cost functions by means of Gaussian process regression. We present two real-world applications of our motion planner for a wheeled robot and a manipulation robot. As we demonstrate in real-world experiments, our system is able to estimate appropriate deformation parameters of real objects that can be used to predict future deformations. We show that our deformation cost approximation improves the efficiency of the planner by several orders of magnitude.     

Discovering and restoring changes in object positions using an autonomous robot with laser rangefinders

This paper describes an object rearrangement system for an autonomous mobile robot. The objective of the robot is to autonomously explore and learn about an environment, to detect changes in the environment on a later visit after object disturbances and finally, to move objects back to their original positions. In the implementation, it is assumed that the robot does not have any prior knowledge of the environment and the positions of the objects. The system exploits Simultaneous Localisation and Mapping (SLAM) and autonomous exploration techniques to achieve the task. These techniques allow the robot to perform localisation and mapping which is required to perform the object rearrangement task autonomously. The system includes an arrangement change detector, object tracking and map update that work with a Polar Scan Match (PSM) Extended Kalman Filter (EKF) SLAM system. In addition, a path planning technique for dragging and pushing an object is also presented in this paper. Experimental results of the integrated approach are shown to demonstrate that the proposed approach provides real-time autonomous object rearrangements by a mobile robot in an initially unknown real environment. Experiments also show the limits of the system by investigating failure modes.     

Multi-agent Rapidly-exploring Pseudo-random Tree

Real-time motion planning and control for groups of heterogeneous and under-actuated robots subject to disturbances and uncertainties in cluttered constrained environments is the key problem addressed in this paper. Here we present the Multi-agent Rapidly-exploring Pseudo-random Tree (MRPT), a novel technique based on a classical Probabilistic Road Map (PRM) algorithm for application in robot team cooperation. Our main contribution lies in the proposal of an extension of a probabilistic approach to be used as a deterministic planner in distributed complex multi-agent systems, keeping the main advantages of PRM strategies like simplicity, fast convergence, and probabilistic completeness. Our methodology is fully distributed, addressing missions with multi-robot teams represented by high nonlinear models and a great number of Degrees of Freedom (DoFs), endowing each agent with the ability of coordinating its own movement with other agents while avoiding collisions with obstacles. The inference of the entire team’s behavior at each time instant by each individual agent is the main improvement of our method. This scheme, which is behavioral in nature, also makes the system less susceptible to failures due to intensive traffic communication among robots. We evaluate the time complexity of our method and show its applicability in planning and executing search and rescue missions for a group of robots in S E3 outdoor scenarios and present both simulated and real-world results.     

Laying out objects with geometric and physical constraints

Modeling scenes involves two tasks:object modeling andobject layout. This paper focuses on object layout and proposes a constraint-based approach which yields a powerful object layout environment. The approach uses collision detection and physical simulation to ensure geometric and physical consistency of the resulting scenes, such as no interpenetration, and physical stability of the objects. A prototype system is developed, providing six basic operations; PUT, PUSH/PULL, TURN/TILT, PICK-UP, TRANSLATE, and ROTATE. The system: ensures geometric and physical consistency; provides easy-to-use operations analogous to object placement in real life; allows twodimensional control easily specified by mouse. Interactive speed is achieved on graphics workstations by using rasterized collision detection and simple quasi-static motion simulation. The system is interfaced to modeling/rendering/animation systems, and realizes an integrated environment for object modeling, object layout, rendering, and animation. We describe several scenes that have been modeled using the system and argue that these experiments confirm that the scene modeling task is greatly simplified by our constraint-based approach.     

An instrumented glove for grasp specification invirtual-reality-based point-and-direct telerobotics

Hand posture and force, which define aspects of the way an object is grasped, are features of robotic manipulation. A means for specifying these grasping “flavors” has been developed that uses an instrumented glove equipped with joint and force sensors. The new grasp specification system will be used at the Pennsylvania State University (Penn State) in a Virtual Reality based Point-and-Direct (VR-PAD) robotics implementation. Here, an operator gives directives to a robot in the same natural way that human may direct another. Phrases such as “put that there” cause the robot to define a grasping strategy and motion strategy to complete the task on its own. In the VR-PAD concept, pointing is done using virtual tools such that an operator can appear to graphically grasp real items in live video. Rather than requiring full duplication of forces and kinesthetic movement throughout a task as is required in manual telemanipulation, hand posture and force are now specified only once. The grasp parameters then become object flavors. The robot maintains the specified force and hand posture flavors for an object throughout the task in handling the real workpiece or item of interest     

Randomized path planning for tasks requiring the release and regrasp of objects

This paper presents a randomized planning algorithm for manipulation tasks that require the robot to release and regrasp an object in different robot postures. Such problems arise, for example, in robotic suturing and knot tying, and in assembly tasks where parts must be guided through complex environments. Formulating the problem as one of planning on a foliated manifold, we present a randomized planning algorithm that, unlike existing methods, involves sampling and tree propagation primarily in the task space manifold; such an approach significantly improves computational efficiency by reducing the number of projections to the constraint manifold, without incurring any significant increases in the number of release-regrasp sequences. We also propose a post-processing topological exploration algorithm and path refinement procedure for reducing the number of release-regrasp sequences in a solution path, independent of the algorithm used to generate the path. Experiments involving spatial open chains with up to 10 degrees of freedom, operating in complex obstacle-filled environments, show that our algorithm considerably outperforms existing algorithms in terms of computation time, path length, and the number of release-regrasp operations.     

Autonomous Path Planning and Experiment Study of Free-floating Space Robot for Target Capturing

Space robotic systems are expected to play an increasingly important role in the future. The robotic on-orbital service, whose key is the capturing technology, becomes research hot in recent years. In this paper, the authors propose an autonomous path planning method for target capturing. The task is described in Cartesian space and it can drive the manipulator to approach the target along the closest path. Firstly, the target feature is extracted based on the measured information via the hand-eye camera, and the target pose (position and orientation) and velocities (linear velocity and angular velocity) are estimated using Kalman filtering technology. Then, a numerically feasible approach is presented to plan the manipulator motion and avoid the dynamic singularities, which are transformed into real-time kinematic singularities avoiding problem. Thirdly, the potential disturbance on the base due to the manipulator’s motion is estimated, and the joint rates are autonomously adjusted to reduce the disturbance if it is beyond the allowed bound. At last, a ground experiment system is set up based on the concept of dynamic emulation and kinematic equivalence. With the experiment system, the autonomous target capturing experiments are conducted. The experiment results validate the proposed algorithm.     

Illumination setup planning for a hand-eye system based on an environmental model

In hand-eye systems for advanced robotic applications such as assembly, the degrees of freedom of the vision sensor should be increased and actively made use of to cope with unstable scene conditions. Particularly, in the case of using a simple vision sensor, an intelligent adaptation of the sensor is essential to compensate for its inability to adapt to a changing environment. This paper proposes a vision sensor setup planning system which operates based on environmental models and generates plans for using the sensor and its illumination assuming freedom of positioning for both. A typical vision task in which the edges of an object are measured to determine its position and orientation is assumed for the sensor setup planning. In this context, the system is able to generate plans for the camera and illumination position, and to select a set of edges best suited for determining the object's position. The system operates for stationary or moving objects by evaluating scene conditions such as edge length, contrast, and relative angles based on a model of the object and the task environment. Automatic vision sensor setup planning functions, as shown in this paper, will play an important role not only for autonomous robotic systems, but also for teleoperation systems in assisting advanced tasks.     

Assembly task execution using visual 3D surface reconstruction: An integrated approach to parts mating

This paper is focused on assembly tasks executed by an industrial robotic manipulator in the presence of uncertainties. The goal is to achieve higher levels of autonomy and flexibility of robotic systems in the execution of such tasks. In particular, as a well-established paradigm of assembly tasks, a Peg-in-Hole task has been considered, where the pose of the target object with respect to the robot is known with uncertainties far larger than the task tolerance, e.g., due to manual positioning of the object in the workcell. The proposed approach is based on the reconstruction of the object surface by means of a number of point clouds provided by a depth sensor. The reconstruction is then compared with a known CAD model of the surface, in order to localize the position and tilt of the holes. Finally, the peg insertion is performed in two steps: a search phase, in which the peg tip gently slides on the surface following a trajectory described by Lissajous functions, and a mechanical coupling phase, in which a compliant behavior is imposed to the peg. Experiments on a collaborative manipulator confirm that the proposed approach allows to achieve a better degree of autonomy and flexibility for a class of robotic tasks in partially structured environments.     

Cooperative control through objective achievement

Cooperative control is a key issue for multirobot systems in many practical applications. In this paper, we address the problem of coordinating a set of mobile robots in the RoboCup soccer middle-size league. We show how the coordination problem that we face can be cast as a specific coalition formation problem, and we propose a distributed algorithm to efficiently solve it. Our approach is based on the distributed computation of a measure of satisfaction (called Agent Satisfaction) that each agent computes for each task. We detail how each agent computes the Agent Satisfaction by acquiring sensor perceptions through an omnidirectional vision system, extracting aggregated information from the acquired perception, and integrating such information with that communicated by the teammates. We empirically validate our approach in a simulated scenario and within RoboCup competitions. The experiments in the simulated scenario allow us to analyse the behaviour of the algorithm in different situations, while the use of the algorithm in real competitions validates the applicability of our approach to robotic platforms involved in a dynamic and complex scenario.     

A Flexible Microrobot-Based Microassembly Station

A wide range of microcomponents can today be produced using various microfabrication techniques. The assembly of complex microsystems consisting of several single components (i.e., hybrid microsystems) is, however, a difficult task that is seen to be a real challenge for the robotic research community. It is necessary to conceive flexible, highly precise and fast microassembly methods. In this paper, the development of a microrobot-based microassembly station is presented. Mobile piezoelectric microrobots with dimensions of some cm³ and with at least 5 DOF can perform various manipulations either under a light microscope or within the vacuum chamber of a scanning electron microscope. The components of the station developed and its control system are described. The latter comprises a vision-based sensor system for automatic robot control, user interfaces, a re-configurable parallel computer array and an assembly planning system. Specific problems that occur when using microrobots in an SEM and our research activities on the development of force microsensors integrated into the microrobots" grippers are described as well.     

Modeling robotic operations controlled by natural language

There are multiple ways to control a robotic system. Most of them require the users to have prior knowledge about robots or get trained before using them. Natural language based control attracts increasing attention due to its versatility and less requirements for users. Since natural language instructions from users cannot be understood by the robots directly, the linguistic input has to be processed into a formal representation which captures the task specification and removes the ambiguity inherent in natural language. For most of existing natural language controlled robotic system, they assume the given language instructions are already in correct orders. However, it is very likely for untrained users to give commands in a mixed order based on their direct observation and intuitive thinking. Simply following the order of the commands can lead to failures of tasks. To provide a remedy for the problem, we propose a novel framework named dependency relation matrix (DRM) to model and organize the semantic information extracted from language input, in order to figure out an executable sequence of subtasks for later execution. In addition, the proposed approach projects abstract language input and detailed sensory information into the same space, and uses the difference between the goal specification and temporal status of the task under implementation to monitor the progress of task execution. In this paper, we describe the DRM framework in detail, and illustrate the utility of this approach with experiment results.     

Target Reaching by Using Visual Information and Q-learning Controllers

This paper presents a solution to the problem of manipulation control: target identification and grasping. The proposed controller is designed for a real platform in combination with a monocular vision system. The objective of the controller is to learn an optimal policy to reach and to grasp a spherical object of known size, randomly placed in the environment. In order to accomplish this, the task has been treated as a reinforcement problem, in which the controller learns by a trial and error approach the situation-action mapping. The optimal policy is found by using the Q-Learning algorithm, a model free reinforcement learning technique, that rewards actions that move the arm closer to the target.The vision system uses geometrical computation to simplify the segmentation of the moving target (a spherical object) and determines an estimate of the target parameters. To speed-up the learning time, the simulated knowledge has been ported on the real platform, an industrial robot manipulator PUMA 560. Experimental results demonstrate the effectiveness of the adaptive controller that does not require an explicit global target position using direct perception of the environment.     

Virtual Force/Tactile Sensors for Interactive Machines Using the User's Biological Signals

This study proposes a new approach to virtual realization of force/tactile sensors in machines equipped with no real sensors. The key of our approach is that a machine exploits the user's biological signals. Therefore, this approach is not dependent on controlled objects and is expected to be widely applicable for a variety of machines including robots. This article describes an example robotic system comprised of an industrial robot manipulator, a motion capture system and a surface electromyogram (EMG) measurement apparatus. By monitoring/recording the user's surface EMG and postural information in real-time, we show that a robot equipped with no force/tactile sensors behaved similarly to one possessing sensors over its body. Another advantage of our approach is demonstrated by a task in which a robot and a user cooperatively hold and move a heavy load.     

Automatic cooperative disassembly robotic system: Task planner to distribute tasks among robots

This paper presents a task planner based on decision trees. Two different types of cooperative tasks are described: common task and parallel task. In the first type of task two or more robots are required to accomplish the task. In the second type, several tasks can be performed in parallel by different robots to reduce the total disassembly time. The planner presented is based on a hierarchical representation of the product and performs the distribution of the tasks among robots using decision trees. The system takes into consideration the work area of each robot and its own characteristics. The work cell can be composed of j robotic manipulators. Finally, a practical application of a PC disassembly system is shown.     

Hybrid Force and Vision-Based Contour Following of Planar Robots

Manipulators interacted with uncalibrated environments have limited dexterity due to constraints imposed by unknown environments. However, to perform science or industrial operations, it is necessary to be able to position and orient these manipulators on targets in order to accomplish required control tasks. This article describes how one might enhance manipulator dexterity for planar contour following tasks using hybrid force and vision-based control. The proposed control approach can guarantee task precision employing only a single-axis force sensor and an imprecisely calibrated CCD camera whose optical axis is perpendicular to the planar workspace. The goal of the autonomous task is to drive an instrument mounted on the end-effector of a planar robotic manipulator to follow a visually determined planar contour and continue tracking the contour in desired pose, contact force, and speed, all demanding time-varying, with precision. The proposed control architecture is suitable for applications that require simultaneous force and pose control in unknown environments. Our approach is successfully validated in a real task environment by performing experiments with an industrial robotic manipulator.