Sanjeev Seereeram

A global approach to path planning for redundant manipulators

An approach to the path planning of redundant manipulators is presented. The path planning problem is posed as a finite-time nonlinear control problem which can be solved by a Newton-Raphson type algorithm. This technique is capable of handling various goal task definitions, as well as incorporating both joint and task space constraints. The algorithm shows promising results in planning joint path sequences and Cartesian tip tracking and goal endpoint specifications. In contrast to local approaches, this algorithm is less prone to problems such as singularities and local minima. Applications to planar 3-R and 4-R arms, cooperating 3-R arms and a spatial nine-degrees-of-freedom arm are included.<<ETX>>

Vision-based relative pose estimation for autonomous rendezvous and docking

Autonomous rendezvous and docking is necessary for planned space programs such as DARPA ASTRO, NASA MSR, ISS assembly and servicing, and other rendezvous and proximity operations. Estimation of the relative pose between the host platform and a resident space object is a critical ability. We present a model-based pose refinement algorithm, part of a suite of algorithms for vision-based relative pose estimation and tracking. Algorithms were tested in high-fidelity simulation and stereo-vision hardware test bed environments. Testing indicated that in most cases, the model-based pose refinement algorithm can handle initial attitude errors up to about 20 degrees, range errors exceeding 10% of range, and transverse errors up to about 2% of range. Preliminary point tests with real camera sequences of a 1/24 scale Magellan satellite model using a simple fixed-gain tracking filter showed potential tracking performance with mean errors of < 3 degrees and < 2% of range

Autonomous hierarchical control of multiple unmanned combat air vehicles (UCAVs)

In this paper we present a hierarchical control scheme that enables multiple UCAVs to achieve demanding missions in hostile environments autonomously. The objective is to use a swarm of UCAVs for a SEAD type mission: fly the UCAVs in a formation to an enemy territory populated with different kinds of threats, collect enemy information or destroy certain targets, and return to the base, all without human intervention. The scheme is an integration of four distinct components, including: (1) high level Voronoi diagram based path planner to avoid static threats; (2) low level path planner to avoid popup threats; (3) differential flatness based trajectory generator to generate dynamically feasible trajectory; and (4) semi-globally stable formation control algorithm to maintain the formation. The scheme was implemented in Matlab and demonstrated very effective path planning, trajectory generation, and formation flying capabilities. We also developed an interface from Matlab to IWARS, a high fidelity battlefield simulation environment developed by Boeing. This enabled us to study the effectiveness of our scheme under various battle scenarios using IWARS.

Autonomous failure detection, identification and fault-tolerant estimation with aerospace applications

In this paper, we propose a novel approach for Failure Detection and Identification (FDI) in nonlinear systems based on the Interacting Multiple Model (IMM) Extended Kalman Filter (EKF) approach. In the nonlinear system FDI application, the main idea consists of representing each failure mode by a model and combining the outputs of EKFs based on different models in a near-optimal way. This IMM-FDI filter provides not only failure detection and identification but also a near-optimal estimate of the system state (even during a failure). The approach has been applied successfully to a problem of spacecraft autonomy for the detection and identification of sensor (gyro, star tracker) and actuator failures. The results of this application show that IMM-EKF detects and identifies failures much more rapidly and reliably than the multi-hypothesis EKF. Furthermore, it handles satisfactorily both permanent and transient failures. Current efforts are underway to perform extensive validation testing on high-fidelity simulation models of representative spacecraft.

Kinematic Path Planning for Robots with Holonomic and Nonholonomic Constraints

Robots in applications may be subject to holonomic or nonholonomic constraints. Examples of holonomic constraints include a manipulator constrained through the contact with the environment, e.g., inserting a part, turning a crank, etc., and multiple manipulators constrained through a common payload. Examples of nonholonomic constraints include no—slip constraints on mobile robot wheels, local normal rotation constraints for soft finger and rolling contacts in grasping, and conservation of angular momentum of in—orbit space robots. The above examples all involve equality constraints; in applications, there are usually additional inequality constraints such as robot joint limits, self collision and environment collision avoidance constraints, steering angle constraints in mobile robots, etc.

Failure Detection and Identification Using a Nonlinear Interactive Multiple Model (IMM) Filtering Approach with Aerospace Applications

Abstract In this paper, we propose a novel approach for Failure Detection and Identification (FDI) in nonlinear systems based on the Interactive Multiple Model (IMM) Extended Kalman Filter (EKF) approach. In the nonlinear-system FDI application, the main idea consists of representing each failure mode by a model and combining the outputs of EKF’s based on different models in a near-optimal way. This IMM-FDI filter provides not only failure detection and identification but also a near-optimal estimate of the system state (even during a failure). The approach has been applied successfully to a problem of spacecraft autonomy for the detection and identification of sensor (gyro, star tracker) and actuator failures. The results of this application show that IMM-EKF detects and identifies failures much more rapidly and reliably than the multi-hypothesis EKF. Furthermore, it handles satisfactorily both permanent and transient failures.

Nonlinear predictive control applied to spacecraft attitude control

A class of iterative methods have recently been proposed for the path planning for a variety of fully and under-actuated mechanical systems, including robots and spacecrafts. These methods all involve the basic idea of warping an initial path iteratively to an acceptable final path by using a Newton-type algorithm. Once a path is found off-line, a feedback controller can then be used to follow the path. A modification of the off-line methods transforms them directly into a nonlinear predictive feedback controller, with guaranteed closed-loop asymptotic stability when the system model is known, and certain robustness when the model information is imperfect. This method represents a special class of model predictive control (MPC), since the control action at each time instance is determined based on the future predicted trajectory. Preliminary results are presented illustrating the application of this nonlinear controller to three-axis attitude control of fully-actuated and underactuated spacecraft.

An all-geodesic algorithm for filament winding of a T-shaped form

An algorithm that generates all-geodesic paths for the complete surface coverage of a T-shaped form composed of the adjoining of two cylinders of equal radii is presented. This has been recognized as a challenging filament-winding problem as its form is nonaxisymmetric. This algorithm was implemented on the robotic filament-winding system developed at Rensselaer Polytechnic Institute. >

Unscented kalman filter for multiple spacecraft formation flying

Abstract In this paper, Unscented Kalman Filter (UKF) is presented both in its canonical form and in a form that is suitable for spacecraft attitude estimation using quaternions. An atti- tude quaternion has a unit norm, resulting in a singular co- variance matrix. Techniques have been developed in the past to deal with this problem, in the context of Extended Kalman Filter (EKF), and the current paper presents the counterpart in the context of UKF. Simulation studies con- firm that at low noise level, EKF and UKF behave in essen- tially the same way, while at high noise level, UKF is more accurate and robust. For multiple-spacecraft formation fly- ing, robustness with respect to noise level and choice of co- ordinate frame is highly desirable, hence it is argued that UKF is better suited for the task than EKF, especially in view of the fact that the additional computational cost asso- ciated with UKF is not significant. 1 Introduction Relative state estimation is an integral part of any forma- tion flying mission. One way of solving the nonlinear es- timation problem is by using the Extended Kalman Filter

A real-time computer controller for a Robotic Filament Winding system

This paper presents an integrated real-time control system for a Robotic Filament Winding manufacturing cell. The architecture is based on a multiprocessor system employing four processor boards. The system incorporates a hierarchical control layout with the task specification at the highest level and the robot and winder set point tracking at the lowest. Eventual design goals include a PC front-end unit capable of winding path generation and testing prior to actual part production. Preliminary experimental results on cylindrical, elbow and T-shaped pipe-fittings are included.