Forestry machines are not easily accessible for experimentation or demonstration of research results. These mobile robots are massive, very expensive, and require a large outdoor space and permits to operate. These factors hinder conducting experiments on real forestry robots. Thus, it is essential to design experimental setups utilizing easily accessible robots in indoor labs that can effectively replicate the behavior of interest of a forestry machine. We design a setup to resemble a log-loader crane and grapple motions using a Kinova Jaco2 arm by manufacturing a specialized end-effector to attach passively to the Jaco2 arm. Passively attached grapple causes undesirable sway, which is problematic and dangerous in forestry. To address the sway problem, we employ dynamic programming to develop an anti-sway motion planner, and validate its performance for different point-to-point maneuvers in our experimental setup. We also repeat each experiment at least 6 times to ensure the repeatability and reliability of the experiments. The experimental results showcase the excellent sway-damping performance of our planner and also the very good repeatability of our experiments.

We propose two novel motion planners for a robotic manipulator with a passive end-effector that is free to sway during and after the robot’s motion. The planners utilize Dynamic Programming to generate trajectories that damp the end-effector’s residual sway while ensuring that the boom tip—the point to which the end-effector is attached—follows a collision-free path or time-dependent waypoints. Our use case is a crane of a forwarder machine, a log-loading machine in the forestry industry, with a passive grapple. In the cluttered forest environment, accurate path following and grapple sway damping are critical to increase the operation efficiency and avoid harming the machine and environment. The results of the simulation in a high-fidelity multibody-dynamics simulator showcase the effectiveness of our methodology in achieving exact path following or timed waypoints following and the residual sway damping. In particular, the average of the maximum residual sway is only 1.9°, showing an average reduction of 75%, as compared to fifth, sixth, and tenth order polynomial trajectories, in six test cases, including common paths used by operators to pick and place logs. Monte-Carlo simulations also showed that our planners have very good robustness against payload mass uncertainty. Other merits of our Dynamic Programming trajectories are that they are smooth, computationally inexpensive, and result in reduced residual sway even for nonzero initial sway conditions. Moreover, the generality of our methodology opens a new way to design anti-sway motion planners for construction cranes or quadrotors with a slung payload, in addition to serial manipulators with passive end-effectors.Note to Practitioners—This work was motivated by the problems arising in the operation of log-loading cranes in the forestry industry: the problems of the end-effector’s large sway during crane reconfiguration and the collision between the crane and obstacles, which are detrimental to the efficiency of the operation. Similar issues arise, for example, in construction cranes transporting large hanging objects. We propose a novel methodology to address both problems by generating smooth and computationally inexpensive trajectories for the crane joint motion. The approach begins with the model of the sway motion and the definition of the collision-free path. Then, our Dynamic Programming algorithm generates anti-sway trajectories that satisfy the joint constraints. The results in a high-fidelity simulator show that our motion planners lead to precise path following and significant sway damping, and also confirm its superiority compared to polynomial trajectories, commonly used in industries. Monte-Carlo simulation also confirms our planners’ robustness. Our methodology is also applicable to other dynamic systems with freely hanging objects, such as multi-degree-of-freedom robotic manipulators, construction cranes, and quadrotors carrying a slung payload. A possible limitation is that the methodology is model-based and necessitates finding the sway dynamics model and estimating payload properties.

This paper presents a novel shared autonomy and baseline policy adapting framework for human-robot interactions in high-level context-aware robotic tasks. With a unique methodology that leverages hierarchies in decision-making as well as variational analysis of human policy, we propose a mathematical model of shared autonomy policy. The framework aims at interpretable high-level decision-making for efficient robot operation with human in the loop. We modeled the decision-making process using hierarchical Markov decision processes (MDPs) in an algorithm we calledpolicy adapting, where the autonomous system policy is adapted, and hence, shaped by incorporating design variables contextual to the robot, human, task, and pre-training. By integrating deep reinforcement learning within a multi-agent hierarchical context, we present an end-to-end algorithm to train a baseline policy designed for shared autonomy. We showcase the effectiveness of our framework, and particularly the interplay between different design elements and human's skill level, in a pilot study with a human user in a simulated sequence of high-level pick-and-place tasks. The proposed framework advances the state-of-the-art in shared autonomy for robotic tasks, but can also be applied to other domains of autonomous operation.

The timber-harvesting industry is lagging its peer industries, such as mining and agriculture, with respect to deployment of robotic, AI and autonomous technologies. In this paper, we tackle automation of a critical task that arises in transporting logs from the forest to the sawmill: the log loading operation. This work is motivated by the acute shortages of human operators and the need to improve the efficiencies of timber-harvesting processes. To this end, we demonstrate the full autonomy pipeline for the log loading operation with a fixed-base manipulator (a.k.a., the crane), starting with perception of logs around the machine, then grasp planning for where to grasp logs, through motion planning and control of the log loading maneuver. Our main contribution is in the full integration of the necessary elements to achieve a completely autonomous loading cycle, where the crane picks up and loads all logs within its reach on a trailer. Notable features of our implementation are a generalizable perception stack, a grasp planner to pick up multiple logs at a time and an extensive experimental campaign conducted outdoors, on a commercial log loader retrofitted for autonomy. Our results demonstrate an overall 87% success rate of the log loading operation, with primary failure cases due to log segmentation errors and deficiencies in the final height adjustment algorithm for grasping logs. We also present detailed timing results of the main parts of the autonomy pipeline, which support the feasibility of deployment in operational environment.

In this paper, a computationally efficient chance-constrained rollover-free motion planning method is presented. Specifically, the method is developed to plan motions for reconfigurable vehicles with the knowledge of a 3-D terrain model that has limited accuracy. The overall motion planning problem is formulated as a nonlinear optimal control problem (NOCP) that employs a constraint in the form of a bound on the probability of rollover under terrain-induced vehicle orientation uncertainty. To increase the computational efficiency of the NOCP with nonlinear chance constraint, a geometric interpretation of the chance constraint is derived based on the characteristics of SO(3), the 3-D rotation group. Monte Carlo simulations are provided to demonstrate the usefulness of the geometric interpretation through comparisons with other methods. Experimental data gathered from driving a mobile robot through real forests are also used to validate the proposed model. Finally, path and trajectory generation results obtained with the proposed planning method for a feller-buncher machine traversing through uncertain 3-D terrain are presented to showcase the method’s overall performance and efficiency.

Log loading constitutes a key operation in timber harvesting, and despite the recent spike of interest in introducing automation to the forestry sector, efficient and intelligent grasping of logs remains unresolved. This paper presents a grasp planning pipeline that relies on the identification of logs' characteristics and pose in the environment of a log-loading machine, to generate high quality grasps. The proposed pipeline involves replicating identified logs in a virtual environment where grasp planning is carried out by using a convolutional neural network and a virtual depth camera. The network relies solely on depth information and the virtual camera can be positioned at a strategically selected location or to follow a certain trajectory to enhance exposure of the logs, all this without having to move the log-loader's crane. The grasp planning pipeline is evaluated through simulated grasping trials and experiments on a large-scale log-loading test-bed with several configurations of wood logs ranging from a single to multiple logs. The grasp planning pipeline proved to be successful with a grasping rate of 98.33 % in the simulated trials and 96.67 % in the experimental trials. The grasp planner was able to overcome log characterization and localization uncertainties, thus allowing the log-loader to pick individual logs, and multiple logs at once when possible.

When a robot end-effector is attached to the arm via passive joints, undesirable end-effector sway will occur. In a forestry crane, such as the log-loading or harvesting machine, this sway is problematic as it hinders the efficiency and also can harm the machine and environment. Here, we tackle the sway problem of the forestry forwarder by proposing a methodology for generating anti-sway trajectories in fast maneuvers. We employ the dynamic programming algorithm, combined with a suitable linearization approach, the latter identified through a comparative study. The solution has low computational cost and provides excellent performance for residual sway damping. We demonstrate the dynamic programming solution on the virtual model of the forwarder by using a high-fidelity multibody-dynamics simulator to validate its performance. The results show our optimal trajectories can suppress the residual sway effectively to be, on average, less than 10% of the sway when using fifth order polynomial trajectories, in point-to-point maneuvers starting from rest or from initial sway conditions.

Operating an articulated machine is a complex and hierarchical task, involving several levels of decision making. Motivated by the timber-harvesting applications of these machines, we are interested in developing a collaborative framework for operating an articulated machine/robot in order to increase its level of autonomy. In this paper, we consider two problems in the context of collaborative operation of a feller-buncher: first, the problem of planning a sequence of cut/grasp/bunch tasks for the trees in the vicinity of the machine. Here we propose a human-inspired planning algorithm based on our observations of the operators in the field. Then, a Markov Decision Process (MDP) framework is provided, which enables us to obtain an optimal sequence of tasks. We provide numerical illustrations of how our MDP framework works. Second is the problem of inferring the operator's goal from the motions of the machine. The goal inference algorithm presented here enables the robot equipped with the planning intelligence to perceive the human's intent in real-time. We evaluate the performance of our goal inference algorithm through a user-study with a feller-buncher simulator. The results show the benefits of our algorithm over a robot that assumes the human is moving to the closest target.

This paper presents a path planning refinement technique that allows the efficient collision and rollover-free motion planning for mobile manipulator robots working on rough terrain. First, the necessary theoretical background on a mobile manipulator's kinematics and dynamic stability measure is introduced. Then, after the brief introduction of the sampling-based path planning problem, the additional refinement stage and its problem formulation will be introduced. Within the refinement stage, the novel Bézier control point addition method is introduced to allow for fast, collision and rollover-free path smoothing using curvature-continuous parametrized curves. Analytical proofs and simulated comparisons are provided in the paper to show effectiveness. The beneficial effect of the refined path on trajectory planning will also be demonstrated through simulation.

We present two novel nonlinear MPC formulations for reduced attitude tracking on quadrotors with bidirectional thrust capabilities. Reduced attitude tracking is relevant to recovery from partial thrust loss, which can occur due to the failure of one or more motors. The first formulation builds on a linearization of the quadrotor attitude dynamics on$S(2)$to achieve simultaneous tracking of reduced attitude and total thrust targets. The second formulation, meanwhile, accomplishes the same goal using a linearization of the dynamics on the Lie algebra of$SO(3)$and a proposed method for projecting Lie algebra errors onto reduced attitude errors. Both methods achieve global tracking on$S(2)$without requiring the use of computationally expensive sequential quadratic program solvers. Through simulations, we show that the second approach generally tracks aggressive attitude references better, while the first controller offers more reliable regulation.

This paper presents a dynamic stability-constrained optimal motion planning algorithm developed for a timber harvesting machine working on rough terrain. First, the kinematics model of the machine, and the Zero Moment Point (ZMP) stability measure is presented. Then, an approach to simplify the model to gain insight and achieve a fast solution of the optimization problem is introduced. The performance and computation time of the motion plan obtained with the simplified model is compared against that obtained with the full kinematics model of the machine with the help of MATLAB simulations. The results demonstrate feasibility of fast motion planning while satisfying the dynamic stability constraint.

Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work. The targeted application is of a robotic manipulator executing a complex environment interaction task, in particular, cutting a wooden object. We train two flavours of Variational Autoencoders-standard and Vector-Quantised-to learn the latent space which is then used to infer certain properties of the cutting operation, such as whether the robot is cutting or not, as well as, material and geometry of the object being cut. The two VAE models are evaluated with reconstruction, prediction and a combined reconstruction/prediction decoders. The results demonstrate the expressiveness of the latent space for robotic interaction inference and the competitive prediction performance against recurrent neural networks.

We consider the problem of collaborative transport of a payload using several quadrotor vehicles. The payload is assumed to be a rigid body and is attached to the vehicles with rigid rods. The model of the system is presented and is employed to formulate a Model Predictive Controller. The centralized MPC formulation differs from others in the literature in the way the linearized model of the system is employed about a non-equilibrium state-input pair. We then present a decentralized formulation of MPC by distributing the computations among the vehicles. Simulations of both versions of the controller are carried out for a four-quadrotor system carrying out a transport maneuver of a box payload, for a cost penalizing the deviations of the vehicles from the desired trajectory and the attitude perturbations of the payload. The results confirm that the decentralized controller can yield a comparable performance to the centralized MPC implementation, for the same computation time of the two algorithms.

Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators. This approach leads to a significant dimensionality reduction which in turn results in better learning and data efficiency. We explore two formulations for the cascading: the inward and outward, both along the manipulator chain topology. The learned modeling is tested in conjunction with the classical inverse dynamics model (semi-parametric) and on its own (non-parametric) in the context of feed-forward control of the arm. Experimental results are obtained with Jaco 2 six-DOF and SARCOS seven-DOF manipulators for randomly defined sinusoidal motions of the joints in order to evaluate the performance of cascading against the standard GP learning. In addition, experiments are conducted using Jaco 2 on a task emulating a pouring maneuver. Results indicate a consistent improvement in learning speed with the inward cascaded GP model and an overall improvement in data efficiency and generalization.

Small quadrotor UAVs are projected to fly increasingly in urban environments for a wide variety of applications such as disaster response, police surveillance, civil infrastructure inspection, and air quality measurement. Micro UAVs can detect and avoid obstacles using onboard cameras; nevertheless, disturbances such as wind gusts, operator error, or failure of onboard vision can still result in dangerous collisions with objects. In the urban setting, the most predominant obstacles are walls and poles. With the aim of developing collision recovery control solutions for quadrotor UAVs, this paper investigates the collision dynamics between a propeller-protected quadrotor UAV and a vertical pole. Simulations provide insight into a quadrotor's post-collision dynamics and experimental trials demonstrate the feasibility of autonomously recovering to stable flight using only inertial onboard sensing in real-time.

Collisions between quadrotor UAVs and the environment often occur, for instance, under faulty piloting, from wind gusts, or when obstacle avoidance fails. Airspace regulations are forcing drone companies to build safer drones; many quadrotor drones now incorporate propeller protection. However, propeller protected quadrotors still do not detect or react to collisions with objects such as walls, poles and cables. In this paper, we present a collision recovery pipeline which controls propeller protected quadrotors to recover from collisions. This pipeline combines concepts from impact dynamics, fuzzy logic, and aggressive quadrotor attitude control. The strategy is validated via a comprehensive Monte Carlo simulation of collisions against a wall, showing the feasibility of recovery from challenging collision scenarios. The pipeline is implemented on a custom experimental quadrotor platform, demonstrating feasibility of real-time performance and successful recovery from a range of pre-collision conditions. The ultimate goal of the research is to implement a general collision recovery solution as a safety feature for quadrotor flight controllers.

This paper presents an account of the experimentations with a ground-based facility emulating interception scenarios with non-cooperative space debris using a robotic manipulator. A sampling-based motion-planning algorithm is used to autonomously solve the dynamic interception problem without collisions and under velocity, acceleration and jerk constraints. Invariant Kalman filter methodologies are employed to estimate and predict the motion of a neutrally-buoyant airship emulating a free-floating target. Discussions are presented on the mitigation of practical issues of system integration and the operation of a sensitive, but cost-effective, test-bed for aerial neutral-buoyancy experiments. An account of ground-based experiments is presented as well as results showing good success rates on the tested scenarios and methods that constitute the first reported set of experiments on the interception phase of active space debris removal.

This paper presents the development, simulation and experimental testing of a non-linear Kalman filter for attitude estimation. This non-linear filter is able to conserve the invariants of the Kalman filter, i.e., the expectations on state estimates and their covariances, by operating in the Lie algebra of SO(3) and along the trajectory of evolving angular momentum. The main feature of this novel discrete-time filter is that the linearization of the Gaussian uncertainty around these permanent trajectories leads to a locally optimal Kalman gain matrix. Results confirm that this Invariant Momentum-tracking Kalman Filter (IMKF) out-performs state-of-the-art approaches such as the Extended Kalman Filter (EKF), and Invariant Extended Kalman Filter (IEKF). At very-low sampling rates, EKFs suffer from divergence as the uncertainty propagation is corrupted by the underlying system approximations. The IMKF suffers no such problems according to the theoretical developments and results reported here.

Although legged locomotion for robots has been studied for many years, the research of autonomous wheellegged robotics is much more recent. Robots of this type, also described as hybrid, can take advantage of the energy efficiency of wheeled locomotion while adapting to more difficult terrain with legged locomotion when necessary. The Micro Hydraulic Toolkit (MHT), developed by engineers at Defence R&D Canada - Suffield, is a good example of such a robot. Investigation into control and optimization techniques for MHT leads to a better understanding of hybrid vehicle control for terrestrial exploration and reconnaissance. Control of hybrid robots has been studied by several researchers during the last decade. The methodology applied in this work uses an inverse kinematics algorithm developed previously for a hybrid robot Hylos, and implements an optimization technique to minimize torques occurring at crucial actuators. As well, some added functionality is incorporated into the control method to implement stepping maneuvers. This paper will present the results obtained via co-simulation using Matlab's Simulink and a high-fidelity model of MHT in LMS Virtual Lab.

This paper presents a quasi-static step climbing behaviour for a minimal sensing wheel-legged quadruped robot called PAW. In the quasi-static climbing maneuver, the robot benefits from wheel traction and uses its legs to reconfigure itself with respect to the step during the climb. The control methodology with the corresponding controller parameters is determined and the state machine for the maneuver is developed. With this controller, PAW is able to climb steps higher than its body clearance. Furthermore, any step height up to this maximum achievable height can be negotiated autonomously with a single set of controller parameters, without knowledge of the step height or distance to the step.

Biological vessels are characterized by their substantial compliance and low friction which present a major challenge for crawling robots for minimally invasive medical procedures. Quite a number of studies considered the design and construction of crawling robots, however, very few focused on the interaction between the robots and the flexible environment. In a previous study, we derived the analytical efficiency of worm locomotion as a function of the number of cells, friction coefficients, normal forces and local (contact) tangential compliance. In this paper, we generalize our previous analysis to include dynamic and static coefficients of friction, determine the conditions of locomotion as function of the external resisting forces and experimentally validate our previous and newly obtained theoretical results. Our experimental setup consists of worm robot prototypes, flexible interfaces with known compliance and a Vicon motion capture system to measure the robot positioning. Separate experiments were conducted to measure the tangential compliance of the contact interface which is required for computing the analytical efficiency. The validation experiments are shown to be in clear match with the theoretical predictions. Specifically, the convergence of the tangential deflections to an arithmetic series and the partial and overall loss of locomotion verify the theoretical predictions.

This paper presents the integration of a fast fluid solver based on the vortex particle method with an open source robot simulation environment for the purpose of simulating wind in urban areas. It is desired for the wind simulation to run at realtime speeds so that high-level landing behaviors can be developed for a small rotary-wing UAV. Due to the realtime constraint, some inaccuracies in the simulation are tolerated. It is found that the present method captures some key aspects of fluid flow that are important to flying small aircraft near environmental obstacles, but a simplified treatment of boundary conditions leads to incomplete development of vortices at the fluid-solid boundary.

This paper presents a new trajectory generation algorithm for a space manipulator after capturing an uncooperative tumbling target. In particular, the previously developed Adaptive ReactionLess Control algorithm (ARLC) is extended to obtain minimum base reaction motion of the manipulator with consideration of joint limit constraints. A task-priority redundancy resolution technique is formulated within an adaptive control scheme with the primary task to maintain minimum disturbance to the base and the secondary task to avoid the physical joint limits. This control scheme is intended for use in the transition phase of the capture mission from the instant of capture till the unknown parameters are identified and/or the available post-capture stabilization methods can be applied properly. To verify the validity and feasibility of the proposed concept, MSC.Adams simulation platform is employed to implement a planar base-manipulator-target model as well as the three-dimensional model of Engineering Test Satellite VII system. The numerical results show that the proposed control scheme is able to generate the reactionless maneuver without violating joint limits of the arm, after capture of an unknown tumbling target.

Recent attempts at designing untethered devices for locomotion inside compliant biological vessels, highlighted the requirements for energy efficiency for prolonged duration inside living bodies. Quite a number of studies considered the design and construction of crawling robots but very few focused on the interaction between the robots and the flexible environment. In previous studies, we derived the efficiency, defined as the actual advance divided by the optimal advance, of worm locomotion. In this paper, we analyze the force, minimum energy and power requirements for worm locomotion over flexible surfaces. More importantly, we determine the optimum conditions of locomotion as a function of the number of cells, friction coefficients, stroke length, energy recovery factor, and tangential compliance. Optimality is defined with respect to energy and power requirements. The analytical results are obtained by integrating the force over the actuator motion and alternatively by summing up the overall energy losses due to friction and elastic losses with the surface and the efficient work performed by the robot. The theoretical predictions are compared to numerical simulations modeling worm robots crawling over flexible surfaces and are found in perfect match.

This paper presents a new adaptive algorithm to generate reactionless motion for a space manipulator during and after capturing an unknown tumbling target. The intended application scenario is on-orbit servicing whereby the service spacecraft/manipulator system must dock to, or get a hold of the target satellite in order to conduct the required operations. In the course of these missions, it is important to maintain the base attitude of the servicer unchanged. However, the changes in the dynamics parameters of the system, as a result of capturing an unknown target, degrade the performance of the attitude stabilization system. To overcome this problem in the post capture scenario, the adaptive reactionless control algorithm to produce the arm motions with minimum disturbance to the base, without knowledge of target dynamics, is proposed in this study. This algorithm is intended for use in the transition phase from the instant of capture till the unknown parameters are identified and/or the available stabilization methods can be applied properly. The proposed approach is developed based on the momentum conservation of the system, while recursive least squares algorithm is employed for parameter adaptation. To verify the validity and feasibility of the proposed concept, MSC Adams simulation platform is employed to implement a planar base-manipulator-target model. Two basic scenarios are considered: one where the initial (prior to capture) angular momentum of the target is zero and the second where the target is spinning. The numerical results show that the space manipulator is able to perform reactionless motion after capturing an unknown spinning target.

An inherent characteristic of biological vessels and tissues is that they exhibit significant compliance or flexibility, both in the normal and tangential directions. The latter in particular is atypical of standard engineering materials and presents additional challenges for designing robotic mechanisms for navigation inside biological vessels by crawling on the tissue. Several studies aimed at designing and building such robots have been carried out but little was done on analyzing the interactions between the robots and their flexible environment. In this study, we will analyze the interaction between earthworm robots and biological tissues where contact mechanics is the dominant factor. Specifically, the efficiency of locomotion of earthworm robots is derived as a function of the tangential flexibility, friction coefficients, number of cells in the robot and external forces.

This paper investigates and demonstrates the feasibility of identifying contact dynamics parameters forstiffroboticpayloadsusing a robotic system. The contact dynamics model for stiff payloads is motivated, and theoretical parameter values and bounds are provided. Then, the effect of nonidealities such as surface roughness and plastic deformation on the theoretical values is demonstrated. A row-wise-scaled total least-squares parameter estimation algorithm is proposed and applied to experimental data measured using the special purpose dexterous manipulator task verification facility manipulator at the Canadian Space Agency. The experimental results are compared to a separate set of experiments with a material testing machine as well as finite-element modeling results. Finally, the experimental findings are generalized by providing guidelines for the maximum identifiable payload stiffness as a function of the position resolution, the maximum exertable force, and the structural stiffness of the robotic system.

Over the past four years, researchers at McGill University have developed a novel concept for studying dynamics and control of robotic grasping of objects in space. This problem arises in several applications - those currently under investigation include on-orbit servicing of satellites and removal of space debris. The main difficulty in experimental testing of such tasks is how to emulate the gravity-free environment of space here on the ground. A number of experimental facilities have been developed around the world to emulate the gravity-free conditions for space robotics research. For example, one popular approach involves floating the system under investigation - a robotic arm or a mock-up satellite - on air-bearings on a glass- covered or granite table. A few research establishments have invested in neutral buoyancy water tanks for three- dimensional, high-fidelity, albeit very costly, emulation of weightlessness. We have proposed a novel idea which involves using a small helium blimp to emulate a free-floating object. Although not perfect, this concept is simpler, less expensive to implement and is more suitable for 3D emulation of the gravity-free conditions of space. An experimental facility has been developed in the Aerospace Mechatronics Laboratory to implement this concept in an indoor laboratory setting. The main components of our facility are: a six-degree-of-freedom robotic manipulator placed on a 3 m linear track, a spherical helium airship 6 ft in diameter, made neutrally buoyant and balanced, a Vicon six-camera system and associated control hardware and software interfaces. This video submission focuses on the control of the airship for satellite emulation.

This paper addresses the issue of implementing an intelligent velocity controller on the Platform for Ambulating Wheels (PAW). The PAW robot is a hybrid quadrupedal wheeled-legged robot that can bound, gallop, roll and brake at high speeds and perform inclined turning. The goal of implementing intelligent control is to increase the robotpsilas versatility and autonomy in order to traverse various terrain types and complete tasks. A Levenberg-Marquardt learning algorithm is executed at the top of flight instant in a stride and computes the forward and rear foot touchdown and liftoff positions. This enables the robot to track desired velocity in a Matlab-Adams co-simulation model. Initial steps are also taken to implement this learning algorithm on the physical robot by way of developing an Extended Kalman Filter (EKF) to estimate the forward center of mass velocity. Additionally, a discussion on the future steps towards autonomous control of the PAW robot is presented.

This paper presents experiments with a new, three-dimensional bipedal running behaviour for our robotic hexapod, RHex. The robot and the bipedal gait are underactuated, using only one actuated degree of freedom per compliant leg. We doubled up RHex's hind legs by attaching a duplicate set of hind legs at 180deg, forming 'S' shaped legs. This reduces the actuator speed requirements during non-contact, while preserving the bipedal dynamics and control challenges. Stable running at average speeds between 0.67 and 1.07 m/s with a success rate of 100% over thirty runs is obtained with only leg angle and body orientation feedback

This paper discusses current wheeled mobility work on a hybrid wheeled-leg robot called PAW. In addition to providing design details, controllers are proposed for inclined turning and sprawled braking which take advantage of the hybrid nature of the platform and improve stability. Power consumption values for a number of its basic behaviours are given, as is the range of the robot

This paper discusses the first implementation of a dynamically stable bounding gait on a hybrid wheeled-leg robot. Design of the robot is reviewed and the controllers which allow this mode of mobility to occur are discussed. Experimental results demonstrating the key dynamic characteristics of the gait, including footfall patterns, are given. The hypothesis that varying leg takeoff angles can lead to regulation of forward speed of the bounding gait is presented and verified. In addition, comparisons are made between the bounding gait which uses active wheel control and bounding which uses passive mechanical blocking of the wheels

This work presents a new methodology for synthesizing force-closure two-finger and three-finger grasps for planar and revolute parts. Most real world objects can be modeled using such parts. The grasp synthesis explicitly takes into account the gripper geometry constraints, but unlike previous related work, we optimize several grasp quality criteria simultaneously, rather than individually. The methodology also has two distinct features that makes it complete: corners are considered to ensure every existing grasp is found and an accessibility check is made around the part contour to avoid synthesizing impossible grasps. The algorithm is therefore practical while it ensures high quality grasps. Results are presented for grasp synthesis of several objects with SARAH, an under-actuated three-finger robotic hand.

A new method of damping vibrations in large, flexible, macro-manipulators using a small, rigid, distal end micro-manipulator is introduced in this paper. This method, entitled frequency matching, is demonstrated experimentally on a planar macro-micro manipulator constructed at the University of Victoria. Its performance is compared to the pseudo-passive energy dissipation (P-PED) method proposed previously. The comparison shows that frequency matching outperforms P-PED in most nominal micro-manipulator configurations.

This paper first outlines the generally accepted procedures for model validation. Four dynamics models are then described which represent the full range of nonlinear complexity for modeling structural flexibility in robotic manipulators. Test manoeuvres are performed on a 2-DOF flexible-link direct-drive manipulator. The natural frequencies and mode shapes are captured very well by the finite element model when damping is incorporated. The performance of the four dynamics models with and without damping is also compared to one another and to the experiment. High order nonlinearities and model damping are shown to be less important for slow manoeuvres by close agreement between the simple and complex models. The importance of the high order terms is also shown to decrease for fast manoeuvres in the presence of damping. Results for the exact nonlinear model are inconclusive because of its poor convergence characteristics.

The paper describes the implementation on a transputer system of a novel parallel algorithm for dynamics simulation of a multibody chain. The algorithm is formulated at a level of parallelism which is natural for the problem but is essentially unavailable to other simulation dynamics algorithms. The experimental results demonstrate that one can improve efficiency of computation by exploiting this level of parallelism. However, analysis of the performance shows that the serial component of the resulting parallel algorithm grows to be a large fraction of the total parallel execution time and therefore limits the speedup that can be achieved with this approach.

These parallel algorithms described are based on a new O(N) solution to the problem. The underlying feature of this O(N) method is a different strategy for decomposition of interbody force which results in a new factorization of mass matrix (M). Specifically, a factorization of inverse of the mass matrix in the form of Schur complement is derived as M/sup -1/=C-D/sup t/A/sup -1/B wherein A, B, and C are block tridiagonal matrices. The new O(N) algorithm is then derived as a recursive implementation of this factorization of M/sup -1/. It is shown that the resulting algorithm is strictly parallel. Strategies for multilevel exploitation of parallelism in the computation are also discussed, resulting in more efficient parallel O(log N) algorithms. The parallel algorithms developed in this paper, in addition to their theoretical significance, are also important from a practical implementation standpoint due to their simple architectural requirements.>

In this paper, two parallel O(log N) algorithms for the computation of manipulator forward dynamics are presented. They are based on a new O(N) algorithm for the problem which is developed from a new factorization of mass matrix M. Specifically, a factorization of the inverse M/sup -1/ in the form of a Schur complement is derived. The new O(N) algorithm is then developed as a recursive implementation of this factorization. It is shown that the resulting algorithm is strictly parallel, that is, it is less efficient than other algorithms for serial computation of the problem. However, to our knowledge, it is the only algorithm that can be parallelized to derive both a time-optimal O(logN) - and processor-optimal - O(N) - parallel algorithm for the problem. A more efficient parallel O(logN) algorithm based on a multilevel exploitation of parallelism is also briefly described. In addition to their theoretical significance, these parallel algorithms allow a practical implementation due to their simple architectural requirements.>

Two important issues relevant to modeling of flexible-link robotic manipulators are addressed. The authors examine the question of which terms should be included in the equations of motion for purposes of simulation. A complete model incorporating all nonlinearities that couple rigid-body and elastic motions is presented, along with a rational scheme for classifying their inclusion. The issue of basis function selection for spatial discretization of the elastic displacements is discussed. The finite element method and an eigenfunction expansion techniques are presented and compared. Both issues are examined numerically in the context of the Space Shuttle remote manipulator system. It is shown that certain key nonlinear elastic terms are required if numerical instability of the simulation is to be averted. Simulation results for two discretization schemes are included.>

A solution procedure for simulation dynamics of elastic multibody systems specifically designed for parallel processing is presented. The method is applicable to open chains with general (rotational and/or translational) interbody constraints. It is based on obtaining an explicit solution for the joint constraint forces by means of iterative techniques. Numerical results for a three-link anthropomorphic flexible-link manipulator are presented. The simulated comparison, on a serial computer, of different parallel iterative schemes indicates that the preconditioned conjugate-gradient methods are computationally most efficient. Moreover, their parallel implementation yields computational complexity that, based on theoretical estimates, is approximately constant with the number of bodies in the chain.>

A solution procedure is presented for the simulation dynamics of elastic multibody systems specifically designed for parallel processing. The method is applicable to open chains with general (rotational and/or translational) interbody constraints. It is based on obtaining an explicit solution for the joint constraint forces by means of iterative techniques. Numerical results for a three-link anthropomorphic flexible-link manipulator are presented. Comparison of different parallel iterative schemes indicates that the preconditioned conjugate-gradient method is the most computationally efficient method on a series computer. Moreover, its parallel implementation can potentially outperform the recursive method of analysis.>

A computer simulation procedure for the dynamics of topological chains, using a recursive Newton-Euler formulation, is presented. The bodies of the chain are, in general, elastic and the joints can permit arbitrary (rotational and/or translational) interbody motion. Relative interbody translation, however, is assumed small. As an example, a three-link quasianthropomorphic flexible-link manipulator is studied. The simulation results underscore the importance of modeling structural flexibility.>