Trajectory Planning on Grids: Considering Speed Limit Constraints

Trajectory (path) planning is a well known and thoroughly studied field of automated planning. It is usually used in computer games, robotics or autonomous agent simulations. Grids are often used for regular discretization of continuous space. Many methods exist for trajectory (path) planning on grids, we address the well known A* algorithm and the state-of-the-art Theta* algorithm. Theta* algorithm, as opposed to A*, provides ‘any-angle‘ paths that look more realistic. In this paper, we provide an extension of both these algorithms to enable support for speed limit constraints.We experimentally evaluate and thoroughly discuss how the extensions affect the planning process showing reasonability and justification of our approach

Using humanoid robots to study human behavior

Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans “program” behavior in-or train-each other

Exact and explicit optimal solutions for trajectory planning and control of single-link flexible-joint manipulators

An optimal trajectory planning problem for a single-link, flexible joint manipulator is studied. A global feedback-linearization is first applied to formulate the nonlinear inequality-constrained optimization problem in a suitable way. Then, an exact and explicit structural formula for the optimal solution of the problem is derived and the solution is shown to be unique. It turns out that the optimal trajectory planning and control can be done off-line, so that the proposed method is applicable to both theoretical analysis and real time tele-robotics control engineering

Fast Motion Planning of UAVs

Fast motion planning (FMP) of autonomous vehicles has been advanced rapidly for robotics research, particularly for trajectory planning of spacecraft. The FMP team at JPL and Caltech has developed an algorithm for autonomous vehicles in environments with many fixed obstacles. The spherical expansion and sequential convex programming (SE-SCP) algorithm is computationally efficient and guarantees any-time local optimality for a given function on top of being faster than other sampling-based motion planning methods. Spherical Expansion (SE) is randomized sampling to explore the workspace of the autonomous vehicle and it finds an initial cost-minimized path. Sequential convex programming (SCP) then optimizes this path and computes a locally optimal trajectory. Current development and simulation of the SE-SCP algorithm is still being tested with MATLAB software as well as the collaborative robotics software called the Robot Operating System (ROS). ROS has advantages over MATLAB since it is a flexible framework for writing robotics software and includes a collection of tools, libraries, and conventions specifically for robotics improvement. By developing a SE-SCP simulation in ROS, a ROS package can be created and uploaded online, which provides opportunities for the public to easily utilize the software and apply the SE-SCP algorithm for motion planning to their own autonomous vehicles

Guidance algorithms for a free-flying space robot

Robotics is a promising technology for assembly, servicing, and maintenance of platforms in space. Several aspects of planning and guidance for telesupervised and fully autonomous robotic servicers are investigated. Guidance algorithms for proximity operation of a free flyer are described. Numeric trajectory optimization is combined with artificial intelligence based obstacle avoidance. An initial algorithm and the results of its simulating platform servicing scenario are discussed. A second algorithm experiment is then proposed