Abstract: As an alternative to cumbersome aerial vehicles with considerable maintenance requirements and flight envelope restrictions, the X4 flyer is chosen as the basis for the Stanford testbed of autonomous rotorcraft for multi-agent control (STARMAC). This paper outlines the design and development of a miniature autonomous waypoint tracker flight control system, and the creation of a multi-vehicle platform for experimentation and validation of multi-agent control algorithms. This testbed development paves the way for real-world implementation of recent work in the fields of autonomous collision and obstacle avoidance, task assignment formation flight, using both centralized and decentralized techniques.

Abstract: We discuss the problem of minimum cost trajectory planning for robotic manipulators. It consists of linking two points in the operational space while minimizing a cost function, taking into account dynamic equations of motion as well as bounds on joint positions, velocities, jerks and torques. This generic optimal control problem is transformed, via a clamped cubic spline model of joint temporal evolutions, into a non-linear constrained optimization problem which is treated then by the Sequential Quadratic Programming (SQP) method. Applications involving grasping mobile object or obstacle avoidance are shown to illustrate the efficiency of the proposed planner.

Abstract: One of the goals of this study was to examine the nature and role of distant visual information sampled during locomotion in the feedforward control of leading and trailing limb while an individual is required to step over an obstacle in the travel path. In addition we were interested in whether or not on-line visual information available while the limb (lead or trail) is stepping over the obstacle influences limb trajectory control and whether the information provided during lead limb cross would be used to calibrate movement of the trail limb. Towards this end, we manipulated availability of vision following an initial dynamic sampling period during the approach phase in proximity to the obstacle and during the lead and trail limb stepping over the obstacle. Ten participants completed 40 trials of obstacle crossing in 8 testing conditions. Initial dynamic visual sampling was sufficient to ensure successful task performance in the absence of vision in the approach phase and during both lead and trail limb stepping over the obstacle. Despite successful task performance, foot placement of the lead and trail limb before obstacle crossing and limb elevation over the obstacle were increased after withdrawal of vision in the approach area. Furthermore, the correlation between toe clearance and foot placement was diminished. While both limbs require feedforward visual information to control the step over the obstacle, only lead limb elevation was influenced by availability of on-line visual information during obstacle crossing. Results were in agreement with the notion of primacy of information inherent in the optic array over those from static samples of the environment in guiding locomotion. It is suggested that the expected proprioceptive feedback information associated with the limb posture before the obstacle, reconstructed using visual memory from dynamic sampling of the environment, mismatched with those from the actual limb position. Accordingly, participants adopted a different strategy that enabled them to clear the obstacle with a higher safety margin.

Abstract: This work presents methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment. For an autonomous robot, obstacle detection and localization as well as representing them in a map are crucial tasks for the success of the robot. Our approach is based on plane extraction from data captured by a stereo-vision system that has been developed specifically for QRIO. We briefly overview the general software architecture composed of perception, short and long term memory, behavior control, and motion control, and emphasize on our methods for obstacle detection by plane extraction, occupancy grid mapping, and path planning. Experimental results complete the description of our system.

Abstract: One important issue in the motion planning and control of kinematically redundant manipulators is the obstacle avoidance. In this paper, a recurrent neural network is developed and applied for kinematic control of redundant manipulators with obstacle avoidance capability. An improved problem formulation is proposed in the sense that the collision-avoidance requirement is represented by dynamically-updated inequality constraints. In addition, physical constraints such as joint physical limits are also incorporated directly into the formulation. Based on the improved problem formulation, a dual neural network is developed for the online solution to collision-free inverse kinematics problem. The neural network is simulated for motion control of the PA10 robot arm in the presence of point and window-shaped obstacle.

Abstract: The theme of this paper is to design a real-time fuzzy target tracking control scheme for autonomous mobile robots by using infrared sensors. At first two mobile robots are setup in the target tracking problem, where one is the target mobile robot with infrared transmitters and the other one is the tracker mobile robot with infrared receivers and reflective sensors. The former is designed to drive in a specific trajectory. The latter is designed to track the target mobile robot. Then we address the design of the fuzzy target tracking control unit, which consists of a behavior network and a gate network. The behavior network possesses the fuzzy wall following control (FWFC) mode, fuzzy target tracking control (FTTC) mode, and two fixed control modes to deal with different situations in real applications. Both the FWFC and FTTC are realized by the fuzzy sliding-mode control scheme. A gate network is used to address the fusion of measurements of two infrared sensors and is developed to recognize which situation is belonged to and which action should be executed. Moreover, the target tracking control with obstacle avoidance is also investigated in this paper. Both computer simulations and real-time implementation experiments of autonomous target tracking control demonstrate the effectiveness and feasibility of the proposed control schemes.

Abstract: This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.

Abstract: This paper relates first experiences using a state-of-the-art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor features a frame rate of up to 30 frames per second. Its capabilities in terms of basic obstacle avoidance and local path-planning are evaluated and compared to the performance of a standard laser scanner.

Abstract: A hand-designed internal representation of the world cannot deal with unknown or uncontrolled environments. Motivated by human cognitive and behavioral development, this paper presents a theory, an architecture, and some experimental results for developmental robotics. By a developmental robot, we mean that the robot generates its “brain” (or “central nervous system,” including the information processor and controller) through online, real-time interactions with its environment (including humans). A new Self-Aware Self-Effecting (SASE) agent concept is proposed, based on our SAIL and Dav developmental robots. The manual and autonomous development paradigms are formulated along with a theory of representation suited for autonomous development. Unlike traditional robot learning, the tasks that a developmental robot ends up learning are unknown during the programming time so that the task-specific representation must be generated and updated through real-time “living” experiences. Experimental results with SAIL and Dav developmental robots are presented, including visual attention selection, autonomous navigation, developmental speech learning, range-based obstacle avoidance, and scaffolding through transfer and chaining.

Abstract: The problem of determining a collision-free path for a mobile robot moving in a dynamically changing environment is addressed in this paper. By explicitly considering a kinematic model of the robot, the family of feasible trajectories and their corresponding steering controls are derived in a closed form and are expressed in terms of one adjustable parameter for the purpose of collision avoidance. Then, a new collision-avoidance condition is developed for the dynamically changing environment, which consists of a time criterion and a geometrical criterion, and it has explicit physical meanings in both the transformed space and the original working space. By imposing the avoidance condition, one can determine one (or a class of) collision-free path(s) in a closed form. Such a path meets all boundary conditions, is twice differentiable, and can be updated in real time once a change in the environment is detected. The solvability condition of the problem is explicitly found, and simulations show that the proposed method is effective.

Abstract: Autonomous control of small and micro air vehicles (SMAV) requires precise estimation of both vehicle state and its surrounding environment. Small cameras, which are available today at very low cost, are attractive sensors for SMAV. 3D vision by video and laser scanning has distinct advantages in that they provide positional information relative to objects and environments, in which the vehicle operates, that is critical to obstacle avoidance and mapping of the environment. This paper presents work on real-time 3D vision algorithms for recovering motion and structure from a video sequence, 3D terrain mapping from a laser range finder onboard a small autonomous helicopter, and sensor fusion of visual and GPS/INS sensors.

Abstract: This paper provides an overview of our framework, called physicomimetics, for the distributed control of swarms of robots. We focus on robotic behaviors that are similar to those shown by solids, liquids, and gases. Solid formations are useful for distributed sensing tasks, while liquids are for obstacle avoidance tasks. Gases are handy for coverage tasks, such as surveillance and sweeping. Theoretical analyses are provided that allow us to reliably control these behaviors. Finally, our implementation on seven robots is summarized.

Abstract: The DARPA Grand Challenge (DGC) was an opportunity to test autonomous vehicles in a competitive situation. In addition to intelligent behaviour, the participating vehicles must also exhibit ruggedness and endurance in order to survive the fast ride over rough terrain ("win with the software- lose with the hardware"). The SciAutonics teams decided to use compact and agile vehicles that employ proven mechanical designs very suitable for the desert environment. 4-wheel drive ensures robust controllability even in slippery ground, and a roll cage protects the vehicle components from damage in case of a collision. The control system relies primarily on a differential GPS (Starfire) and a set of inertial sensors for navigating between the given set of waypoints. A sensor suite using infrared laser (LIDAR) and ultrasound sensing provides the capability of obstacle avoidance and path following. This paper shows the components of the vehicle and results from driving at the DGC.

Abstract: In this short paper, we consider the nonlinear control of kinematically redundant robot manipulators. Specifically, we use a Lyapunov technique to design a model-based nonlinear controller that achieves exponential link position and subtask tracking. We note that the control strategy does not require the computation of positional inverse kinematics and does not place any restriction on the self-motion of the manipulator; hence, the extra degrees of freedom are available for subtasks (i.e., maintaining manipulability, avoidance of mechanical limits and obstacle avoidance). Experimental implementations on a redundant robot are also included to illustrate the performance of the proposed control law.

Abstract: Deploying large numbers of sensors has been receiving a lot of attention for detection of hazardous biological or chemical substances in public buildings, airports, shallow water harbors, etc. The sensor-carrying robots are in fact agents that facilitate the repositioning of network nodes in order to increase their coverage and accuracy. Wireless network communication is an essential technology in transmitting the sensed and telemetry information between robots, but it has traditionally been addressed separately from mobile robot navigation. In this work we propose to use a potential field framework to control the behavior of the mobile sensor nodes by combining classical robotic team concepts (obstacle avoidance, goal attainment, flight formation, environment mapping and coverage) with traditional sensor network concepts (node energy minimization, optimal data rate and congestion control, routing in ad-hoc networks). Simulation results are used to illustrate the proposed concepts, and an experimental mobile sensor fleet is built at the author's institution.

Abstract: A system and method for detecting an obstacle comprises determining a vegetation height of a vegetation canopy at corresponding coordinates within a field. An object height is detected, where the object height of an object exceeds the vegetation height. A range of the object is estimated. The range of the object may be references to a vehicle location, a reference location, or absolute coordinates. Dimensions associated with the object are estimated. An obstacle avoidance zone is established for the vehicle based on the estimated range and dimensions.

Abstract: A method for flying in a degraded visual environment comprising the steps of collecting environmental data (102), processing the data and fusing (110) the data together into a combined input output which is fed into a head down display (114), head mounted or heads up display (120) and, preferably to a fly-by-wire (104) vertical take-off and landing capable vehicle wherein the fly-by-wire system makes automatic adjustments to the helicopter.

Abstract: In this paper, we discuss the limitations of current evolutionary robotics models and we propose a new framework that might solve some of these problems and lead to an open-ended evolutionary process in hardware. More specifically, the paper describes a novel approach where the usual concepts of population, generations and fitness are made implicit in the system. Individuals co-evolve embedded in their environment. Exploiting the self-assembling capabilities of the (simulated) robots, the genotype of a successful individual can spread in the population. In this way, interesting behaviours emerge spontaneously, resulting in chasing and evading other individuals, collective obstacle avoidance and co-ordinated motion of self-assembled structures.

Abstract: The goal of the study was to examine the accuracy and precision of control of adaptive locomotion using haptic information in normally sighted humans before and after practice. Obstacle avoidance paradigm was used to study adaptive locomotion; individuals were required to approach and step over different sizes of obstacles placed in the travel path under three sensory conditions: full vision (FV); restricted lower visual field (RLVF) using blinders on custom glass frames; and no vision (NV) using haptic information only. In the NV condition, individuals were a given an appropriate-sized cane to guide their locomotion. Footfall patterns were recorded using the GAITRite system, and lead and trail limb trajectories were monitored using the OPTOTRAK system, which tracked infrared diodes placed on the toes and the cane. Approach step lengths were reduced for the haptic condition: this slowed the forward progression and allowed greater time for haptic exploration, which ranged from 2.5 to 4 s and consisted of horizontal cane movements (to detect the width and relative location of the obstacle) and vertical cane movements (to detect the height of the obstacle). Based on feed-forward and on-line sensory (under both vision and haptic conditions) information about location of the obstacle relative to the individual, variability of foot placement reduced as the individual came closer to the obstacle, as has been shown in the literature. The only difference was that the reduction in variability of foot placement under haptic condition occurred in the last step compared with earlier under vision. Considering that the obstacle is detected only when the cane comes in contact, as opposed to vision condition when it is visible earlier, this difference is understandable. Variability and magnitude of lead and trail limb elevation for the haptic condition was higher than the RLVF and FV conditions. In contrast, only the magnitude of lead and trail limb elevation was higher in the RLVF condition when compared with the FV condition. This suggests that it is the inability of the haptic sense to provide accurate information about obstacle characteristics compared with the visual system, and not simple caution that lead to higher limb elevation. In the haptic and RLVF condition when vision was unavailable for on-line monitoring of lead limb elevation, kinesthetic information from lead limb elevation was used to fine-tune trail limb elevation. Both the control of approach phase and limb elevation findings held up even after sufficient practice to learn haptic guidance of adaptive locomotion in the second experiment. These results provide a clear picture of the efficacy of the haptic sensory system to guide locomotion in a cluttered environment.

Abstract: The artificial life approach to evolutionary robotics is used as a fundamental framework for the development of a modular neural control of autonomous mobile robots. The applied evolutionary technique is especially designed to grow different neural structures with complex dynamical properties. This is due to a modular neurodynamics approach to cognitive systems, stating that cognitive processes are the result of interacting dynamical neuro-modules. The evolutionary algorithm is described, and a few examples for the versatility of the procedures are given. Besides solutions for standard tasks like exploration, obstacle avoidance and tropism, also the sequential evolution of morphology and control of a biped is demonstrated. A further example describes the co-evolution of different neuro-controllers co-operating to keep a gravitationally driven art-robot in constant rotation.

Abstract: Autonomous path planning capability is a critical requirement for future unmanned vehicle operations. This paper presents a novel path planning approach for rapid construction of feasible, yet agile, trajectories. The approach relies upon separating the planning task into an on-line and an o-line component. The o-line component consists of building a library of motion primitives using nonlinear simulation, the primitives are then sequenced on-line using an A* search. The main benefit of this separation is that it shifts the computationally intensive work to the o-line component and leaves the on-line task relatively light. A major benefit of using A* is that the search algorithm is unaected by the changes to the library of trim primitives. Thus in the event that a UAVs capabilities are degraded, e.g. due to damage, any unavailable motion primitives are simply removed from the library and A* will not included them in the path construction. To speed up the on-line search time, a sub-optimal modification is made to the standard A* algorithm that delivers a feasible sub-optimal path quickly with relatively small increase in total path cost. Examples of waypoint planning and obstacle avoidance are given to illustrate the path planning approach.

Abstract: Real-time obstacle avoidance is one of the key issues in successful application of mobile robot systems. Some new real-time obstacle avoidance methods for mobile robots have already been developed and implemented. This paper introduces the developments in this area, and summarizes Potential Field method (PFM) and the Enhanced Vector Field Histogram method (VFH+), which are widely used for autonomous mobile robot obstacle avoidance. After a brief introduction of laser measurement system (LMS), a new radar-based obstacle avoidance method for mobile robots, based on the combination and improvement of PFM and VFH+ method, is presented. This method, named the Vector Polar Histogram method (VPH), uses laser radar to detect obstacles and takes the physical meaning of the vector polar into account, so as to get the best avoidance choice. The new method permits the detection of unknown obstacles and avoids collisions in real time while simultaneously steering the mobile robot towards the target.

Abstract: Objectives: To test the hypothesis that vertical footlift asymmetries and low obstacle clearing distance during obstacle avoidance are characteristics of elderly people classified as high risk for falls Design: Controlled cross-sectional design with two conditions to cue selection of the foot-for-step initiation: sound cue and visual cue Setting: Senior independent living facilities Participants: Eighteen community-dwelling elderly with a history of falling or prolonged Timed Up and Go score greater than 135 seconds, 16 elderly with no fall history and Timed Up & Go score of 135 seconds or less, and 15 younger subjects Measurements: Video kinematic analysis of bilateral footlift displacement and velocity using reflective markers as subjects stepped over foam obstacles scaled to a maximum tolerated height Results: High-risk elders contacted the obstacle more frequently and had significantly greater vertical footlift asymmetries adjusted for obstacle/subject height (mean±standard error asymmetry index for sound cue 325±042 cm, for visual cue 251±045 cm) than low-risk and younger subjects (P<001) In low-risk elderly and younger subjects, the asymmetry index approached 0, which indicated symmetrical lower limb movements when stepping over the obstacles Conclusion: High-risk elderly show a marked asymmetry in foot clearance while stepping over an obstacle, with the lag foot clearing the obstacle at a much lower distance than the lead foot Possible mechanisms responsible for these findings (limited hip extension and deficits in executive cognitive function) are discussed

Abstract: An object detection system utilizing one or more thin, planar structured light patterns projected into a volume of interest, along with digital processing hardware and one or more electronic imagers looking into the volume of interest. Triangulation is used to determine the intersection of the structured light pattern with objects in the volume of interest. Applications include navigation and obstacle avoidance systems for autonomous vehicles (including agricultural vehicles and domestic robots), security systems, and pet training systems.