Abstract: Summary This paper examines the role of the human driver as the primary control element within the traditional driver-vehicle system. Lateral and longitudinal control tasks such as path-following, obstacle avoidance, and headway control are examples of steering and braking activities performed by the human driver. Physical limitations as well as various attributes that make the human driver unique and help to characterize human control behavior are described. Example driver models containing such traits and that are commonly used to predict the performance of the combined driver-vehicle system in lateral and longitudinal control tasks are identified.

Abstract: The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obstacles behave as attractors and repellers of heading, respectively, whose strengths depend on distance. The observed behavior was modeled as a dynamical system in which angular acceleration is a function of goal and obstacle angle and distance. By linearly combining terms for goals and obstacles, one could predict whether participants adopt a route to the left or right of an obstacle to reach a goal (Experiment 3). Route selection may emerge from on-line steering dynamics, making explicit path planning unnecessary.

Abstract: This paper presents the first motion planning methodology applicable to articulated, nonpoint nonholonomic robots with guaranteed collision avoidance and convergence properties. It is based on a new class of nonsmooth Lyapunov functions and a novel extension of the navigation function method to account for nonpoint articulated robots. The dipolar inverse Lyapunov functions introduced are appropriate for nonholonomic control and offer superior performance characteristics compared to existing tools. The new potential field technique uses diffeomorphic transformations and exploits the resulting point-world topology. The combined approach is applied to the problem of handling deformable material by multiple nonholonomic mobile manipulators in an obstacle environment to yield a centralized coordinating control law. Simulation results verify asymptotic convergence of the robots, obstacle avoidance, boundedness of object deformations, and singularity avoidance for the manipulators.

Abstract: Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable sensors, safety mechanisms, and general integrated software tools and techniques are needed. We believe that the development of practical motion planning algorithms and obstacle avoidance software for humanoid robots represents an important enabling technology. This paper gives an overview of some of our recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions. We show experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware.

Abstract: The artificial potential field (APF) based path planning methods have a local minimum problem, which can trap mobile robots before reaching it's goal. In this study, a new concept using a virtual obstacle is proposed to escape local minimums occurred in local path planning. A virtual obstacle is located around local minimums to repel a mobile robot from local minimums. A sensor based discrete modeling method is also proposed for modeling of the mobile robot with range sensors. This modeling method is adaptable for a real-time path planning because it provides lower complexity.

Abstract: In this paper, we propose a motion-planning method of multiple mobile robots for cooperative transportation of a large object in a three-dimensional environment. This task has various kinds of problems, such as obstacle avoidance and stable manipulation. All of these problems cannot be solved at once, since it would result in a dramatic increase of the computational time. Accordingly, we divided the motion planner into a global path planner and a local manipulation planner, designed them, and integrated them. The aim was to integrate a gross motion planner and a fine motion planner. Concerning the global path planner, we reduced the dimensions of the configuration space (C-space) using the feature of transportation by mobile robots. We used the potential field to find the solution by searching in this smaller-dimension reconstructed C-space. In the global path planner, the constraints of the object manipulation are considered as the cost function and the heuristic function in the A/sup */ search. For the local manipulation planner, we developed a manipulation technique, which is suitable for mobile robots by position control. We computed the conditions in which the object becomes unstable during manipulation and generated each robot's motion, considering the robots' motion errors and indefinite factors from the planning stage. We verified the effectiveness of our proposed motion planning method through simulations.

Abstract: NavBelt and GuideCane are computerized devices based on advanced mobile robotics obstacle-avoidance technologies. NavBelt is worn by the user like a belt and is equipped with an array of ultrasonic sensors. It provides acoustic signals via a set of stereo earphones that guides the user around obstacles or "displays" a virtual acoustic panoramic image of the traveler's surroundings. One limitation of the NavBelt is that it is exceedingly difficult for the user to comprehend the guidance signals in time to allow fast walking. A newer device, called GuideCane, effectively overcomes this problem. The GuideCane uses the same mobile robotics technology as the NavBelt but is a wheeled device pushed ahead of the user via an attached cane. When the GuideCane detects an obstacle, it steers around it. The user immediately feels this steering action and can follow the GuideCane's new path easily without any conscious effort. This article describes the two devices, including the mechanical, electronic, and software components, user-machine interface, and some experimental results.

Abstract: A novel driver-assist stability system for all-wheel-drive electric vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a fuzzy logic system that independently controls wheel torque to prevent vehicle spin. Another fuzzy wheel slip controller is used to enhance vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease of implementing fuzzy controllers gives a potential for vehicle stability enhancement.

Abstract: In this study, dual-task interference in obstacle-avoidance tasks during human walking was examined. Ten healthy young adults participated in the experiment. While they were walking on a treadmill, an obstacle suddenly fell on the treadmill in front of their left leg during either midswing, early stance, or late stance of the ipsilateral leg. Participants were instructed to avoid the obstacle, both as a single task and while they were concurrently performing a cognitive secondary task (dual task). Rates of failure, avoidance strategy, and a number of kinematic parameters were studied under both task conditions. When only a short response time was available, rates of failure on the avoidance task were larger during the dual task than during the single task. Smaller crossing swing velocities were found during the dual task as compared with those observed in the single task. The difference in crossing swing velocities was attributable to increased stiffness of the crossing swing limb. The results of the present study indicated that divided attention affects young and healthy individuals' obstacle-avoidance performance during walking.

Abstract: We contribute a fast system for avoiding unknown obstacles on a mobile robot using a simple camera as the only sensor. The vision module detects objects, both known and unknown, around the robot. Unknown objects are detected by paying attention to occlusions of a floor of known colors. Range and angle to the objects is calculated and used to create a radial model of the vicinity of the robot. This modeling component keeps tracks of objects that are currently outside the field of view of the camera allowing the robot to avoid obstacles it is not currently looking at. We show the effectiveness of the vision and modeling algorithms by creating a simple behavior which wanders around while avoiding obstacles.

Abstract: We present the local path planning and obstacle avoidance method used on the autonomous tour-guide robot RoboX. It has proven its value during a 5 month operation of ten such robots in a real-world application, a very crowded exhibition. Three known approaches (DWA, elastic band, NF1) have been integrated into a system that performs smooth motion efficiently, in the sense of computational effort as well as goal-directedness. Apart from modifications to the DWA and the elastic band, we present the formulations that allow this fusion.

Abstract: We present the local path planning and obstacle avoidance method used on the autonomous tour-guide robot RoboX. It has proven its value during a 5 month operation of ten such robots in a real-world application, a very crowded exhibition. Three known approaches (DWA, elastic band, NF1) have been integrated into a system that performs smooth motion efficiently, in the sense of computational effort as well as goal-directedness. Apart from modifications to the DWA and the elastic band, we present the formulations that allow this fusion.

Abstract: Needle insertion for percutaneous therapies is formulated as a trajectory planning and control problem. A new concept of needle steering is developed and a Needle Manipulation Jacobian is defined using numerical needle insertion models that include needle deflection and soft tissue deformation. This concept is used in conjunction with a potential-field-based path planning technique to demonstrate needle tip placement and obstacle avoidance. Results from open loop insertion experiments are provided.

Abstract: In this paper, we present an approach to obstacle avoidance for a group of unmanned vehicles moving in formation. The goal of the group is to move through a partially unknown environment with obstacles and reach a destination while maintaining the formation. We address this problem for a class of dynamic unicycle robots. Using Input-to-State Stability we combine a general class of formation-keeping control schemes with a new dynamic window approach to obstacle avoidance in order to guarantee safety and stability of the formation as well as convergence to the goal position. An important part of the proposed approach can be seen as a formation extension of the configuration space obstacle concept. We illustrate the method with a challenging example.

Abstract: A cellular hardware implementation of a spiking neural network with run-time reconfigurable connectivity is presented. It is implemented on a compact custom FPGA board, which provides a powerful reconfigurable hardware platform for hardware and software design. Complementing the system, a CPU synthesized on the FPGA takes care of interfacing the network with the external world. The FPGA board and the hardware network are demonstrated in the form of a controller embedded on the Khepera robot for a task of obstacle avoidance. Finally, future implementations on new multi-cellular hardware are discussed.

Abstract: A vision system for a vehicle that identifies and classifies objects (targets) located proximate a vehicle. The system comprises a sensor array that produces imagery that is processed to generate depth maps of the scene proximate a vehicle. The depth maps are processed and compared to pre-rendered templates of target objects that could appear proximate the vehicle. A target list is produced by matching the pre-rendered templates to the depth map imagery. The system processes the target list to produce target size and classification estimates. The target is then tracked as it moves near a vehicle and the target position, classification and velocity are determined. This information can be used in a number of ways. For example, the target information may be displayed to the driver, the information may be used for an obstacle avoidance system that adjusts the trajectory or other parameters of the vehicle to safely avoid the obstacle. The orientation and/or configuration of the vehicle may be adapted to mitigate damage resulting from an imminent collision, or the driver may be warned of an impending collision.

Abstract: This paper presents a novel method of combining topological and feature-based mapping strategies to create a hierarchical approach to simultaneous localization and mapping (SLAM). More than simply running both processes in parallel, we use the topological mapping procedure to organize local feature-based methods. The result is an autonomous exploration and mapping strategy that scales well to large environments and higher dimensions while confronting the issue of obstacle avoidance. We have obtained successful results of our approach in an area spanning 5000 square meters.

Abstract: This paper presents a review of self-organizing feature maps (SOFMs), in particular, those based on the Kohonen algorithm, applied to adaptive modeling and control of robotic manipulators. Through a number of references we show how SOFMs can learn nonlinear input–output mappings needed to control robotic manipulators, thereby coping with important robotic issues such as the excess degrees of freedom, computation of inverse kinematics and dynamics, hand–eye coordination, path-planning, obstacle avoidance, and compliant motion. We conclude the paper arguing that SOFMs can be a much simpler, feasible alternative to MLP and RBF networks for function approximation and for the design of neurocontrollers. Comparison with other supervised/unsupervised approaches and directions for further work on the field are also provided.

Abstract: The authors used a stimulus-response compatibility paradigm to assess the effect of changing the estimated time to obstacle contact A limb-selection cue was presented in different phases of gait to young (n = 5) and to older (n = 4) adults while they were moving toward a foam obstacle in the walking path A downward saccade was initiated after the cue; the saccade typically occurred during the stance phase of the target limb (the foot cued to lead the step over the obstacle) The mean saccade-step latency after the cue was on the order of -500 ms in both young and elderly participants On reaching the obstacle, both groups generated an upward saccade approximately -300 ms before target footlift in both groups Saccades following the limb-selection cue appeared to direct the gaze toward footfall targets just beyond the obstacle, whereas saccades generated just before obstacle footlift moved the gaze to the forward-looking direction The elderly had significantly longer saccade-trailing-footlift latencies and prolonged gaze-fixation times than did the younger adults Transient disruptions in optical flow appeared to be necessary for successful obstacle-avoidance behavior when there was an unexpected change in the estimated time to obstacle contact

Abstract: Path Optimization for Nonholonomic Systems: Application to Reactive Obstacle Avoidance and Path Planning.- From Dynamic Programming to RRTs: Algorithmic Design of Feasible Trajectories.- Control of Nonprehensile Manipulation.- Motion Planning and Control Problems for Underactuated Robots.- Motion Description Languages for Multi-Modal Control in Robotics.- Polynomial Design of Dynamics-based Information Processing System.- Actuation Methods For Human-Centered Robotics and Associated Control Challenges.- Control of a Flexible Manipulator with Noncollocated Feedback: Time Domain Passivity Approach.- Cartesian Compliant Control Strategies for Light-Weight, Flexible Joint Robots.- Toward the Control of Self-Assembling Systems.- Towards Abstraction and Control for Large Groups of Robots.- Omnidirectional Sensing for Robot Control.- A Passivity Approach to Vision-based Dynamic Control of Robots with Nonlinear Observer.- Visual Servoing Along Epipoles.- Toward Geometric Visual Servoing.- Vision-Based Online Trajectory Generation and Its Application to Catching.- Stability Analysis of Invariant Visual Servoing and Robustness to Parametric Uncertainties.

Abstract: A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.

Abstract: This paper presents a stable receding horizon controller for the minimum time trajectory optimization problem with a vehicle flying in a complex environment with obstacles and no-fly zones. The trajectory optimization is done using mixed-integer linear programming (MILP), which can directly incorporate logical constraints such as obstacle avoidance and waypoint selection and provides an optimization framework that can account for basic dynamic constraints such as turn limitations. Previous work introduced a receding horizon control that significantly reduces the computational effort for solving MILP problems. A straight line approximation used beyond the planning horizon gives a good estimate of the cost-to-go, but is shown to fail when no kinodynamically feasible trajectory could be constructed. A new formulation in this paper solves this problem by using a modified form of Dijkstra’s algorithm to construct a path approximation that is kinodynamically feasible from the start to the goal. With this revised path approximation and the new terminal constraints in the MILP formulation, the receding horizon MILP optimization problem is proven to have a feasible solution, which guarantees that the vehicle can reach the goal in bounded time. The simulation results show this new formulation is computationally tractable.

Abstract: This paper presents a global navigation strategy for autonomous mobile robots in large-scale uncertain environments. The aim of this approach is to minimize collision risk and time delays by adapting to the changes in a dynamic environment. The issue of obstacle avoidance is addressed on the global level. It focuses on a navigation strategy that prevents the robot from facing the situations where it has to avoid obstacles. To model the partially known environment, a grid-based map is used. A modified wave-transform algorithm is described that finds several alternative paths from the start to the goal. Case-based reasoning is used to learn from past experiences and to adapt to the changes in the environment. Learning and adaptation by means of case-based reasoning permits the robot to choose routes that are less risky to follow and lead faster to the goal. The experimental results demonstrate that using case-based reasoning considerably increases the performance of the robot in a difficult uncertain environment. The robot learns to take actions that are more predictable, minimize collision risk and traversal time as well as traveled distances.

Abstract: This paper presents ROCI, a framework for developing applications for multi-robot teams. In ROCI, each robot is considered a node, which contains several modules and may export different types of services and capabilities to other nodes. Each node runs a kernel that mediates the interactions of the robots in a team. This kernel keeps an updated database of all nodes and the functionalities that they export. Multi-robot applications can be built dynamically by connecting modules that may be running on different nodes over the network. As an example, we present an obstacle avoidance task implemented using our framework and also discuss the use of ROCI in a multi-robot scenario.

Abstract: The dynamics of each agent of a multi-agent controlled dynamical system can be formulated in several possible ways: differential inclusion, flatness parameterization, higher-order inclusions and so on. A plethora of techniques have been proposed for each of these formulations but they are typically not portable across equivalent mathematical formulations. Further complications arise as a result of path constraints such as those imposed by obstacle avoidance or control saturation. In this paper, we present a unified computational framework based on pseudospectral methods to handle the optimal control of dynamical systems where the description of the governing equations or that of the path constraint is not a limitation. We illustrate our ideas by way of multiple formulations of a flexible link manipulator problem that includes a differentially flat formulation subject to control saturation. A comparison of our approach to a recent method reveals that we get an almost 30% improvement in the cost. Our results also show that equivalent mathematical formulations can yield varying run times leading to some surprising questions on flatness parameterization for real-time computation.

Abstract: The design of an agent system for robotics is a problem that involves aspects coming from many different disciplines (robotics, artificial intelligence, computer vision, software engineering). The most difficult part of it, often consists in producing and tuning the algorithms that incorporates the robot behavior (planning, obstacle avoidance,...) and abilities (vision, manipulation, navigation,...). Frequently, the reuse of these parts is left to a copy and paste procedure from previous applications to the new one. In so doing many problems could arise. We propose a comprehensive approach for multi-agent systems oriented to robotics applications that uses a complete design methodology supported by a specific design tools and a pattern repository that interacting each other and with the designer allow the production of a coherent design that easily incorporates patterns coming from previously experienced features and automatically produces a large part of the final code.