A path planning and path-following control framework for a general 2-trailer with a car-like tractor

Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented.Comment: Preprin

Motion Planning for Manipulation With Heuristic Search

Heuristic searches such as A* search are a popular means of finding least-cost plans due to their generality, strong theoretical guarantees on completeness and optimality, simplicity in implementation, and consistent behavior. In planning for robotic manipulation, however, these techniques are commonly thought of as impractical due to the high-dimensionality of the planning problem. As part of this thesis work, we have developed a heuristic search-based approach to motion planning for manipulation that does deal effectively with the high-dimensionality of the problem. In this thesis, I will present the approach together with its theoretical properties and show how to apply it to single-arm and dual-arm motion planning with upright constraints on a PR2 robot operating in non-trivial cluttered spaces. Then I will explain how we extended our approach to manipulation planning for n-arms with regrasping. In this work, the planner itself makes all of the discrete decisions, including which arm to use for the pickup and putdown, whether handoffs are necessary and how the object should be grasped at each step along the way. An extensive experimental analysis in both simulation and on a physical PR2 shows that, in terms of runtime, our approach is on par with some of the most common sampling-based approaches. This includes benchmarking our planning framework on two domains that we constructed that are common to manufacturing: pick-and-place of fast moving objects and the autonomous assembly of small objects. Between these applications, the planner exhibited fast planning times and the ability to robustly plan paths into and out of tight working environments that are common to assembly. The closing work of this thesis includes an exhaustive study of the natural tradeoff that occurs between planning efficiency versus solution quality for different values of the heuristic inflation factor. A comparison of the solution quality of our planner to paths computed by an asymptotically optimal approach given a great deal of time for path optimization is included as well. Finally, a set of experimental results are included that show that due to our approach\u27s deterministic cost-minimization, similar input tends to lead to similarity in the output. This kind of local consistency is important to the predictability of the robot\u27s motions and contributes to human-robot safety

Anisotropic Fast-Marching on cartesian grids using Lattice Basis Reduction

We introduce a modification of the Fast Marching Algorithm, which solves the generalized eikonal equation associated to an arbitrary continuous riemannian metric, on a two or three dimensional domain. The algorithm has a logarithmic complexity in the maximum anisotropy ratio of the riemannian metric, which allows to handle extreme anisotropies for a reduced numerical cost. We prove the consistence of the algorithm, and illustrate its efficiency by numerical experiments. The algorithm relies on the computation at each grid point of a special system of coordinates: a reduced basis of the cartesian grid, with respect to the symmetric positive definite matrix encoding the desired anisotropy at this point.Comment: 28 pages, 12 figure