A global approach to kinematic path planning to 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. The problem of finding a kinematically feasible path that satisfies a given set of holonomic and nonholonomic constraints, of both equality and inequality types is addressed. The path planning problem is first posed as a finite time nonlinear control problem. This problem is subsequently transformed to a static root finding problem in an augmented space which can then be iteratively solved. The algorithm has shown promising results in planning feasible paths for redundant arms satisfying Cartesian path following and goal endpoint specifications, and mobile vehicles with multiple trailers. In contrast to local approaches, this algorithm is less prone to problems such as singularities and local minima

Proliferated LEO Autonomy Architecture for Capability with Scalability

A next generation space architecture focused on proliferated low-Earth Orbit (p-LEO) constellations holds the promise of improved situational awareness, responsiveness, and resiliency. A variety of proliferated space constellation efforts are underway in the National Security Space Arena, all demanding innovations in ubiquitous satellite command, control, and communications. Whether communications, science, or defense missions, the expansion into PLEO constellations drives new demands upon autonomy, software, and communications architectures. Previous groundbreaking autonomy work was performed on the Deep Space 1 mission, which eventually led to NASA Mars and Earth Observing-1 autonomy. In Autonomous Rendezvous, Proximity Operations, and Docking (ARPOD), Defense Advanced Research Projects Agency (DARPA)\u27s Orbital Express and the Air Force XSS-10 mission helped establish the state of the art. While similarities exist, mission autonomy for these individual spacecraft missions fundamentally differs from PLEO constellations in their demands and constraints

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 highfidelity simulation and stereo-vision hardware testbed environments. Testing indicated that in most cases, the modelbased 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