Motion Planning Framework Research Articles

Meshless Modeling of Deformable Shapes and their Motion.

We present a new framework for interactive shape deformation modeling and key frame interpolation based on a meshless finite element formulation. Starting from a coarse nodal sampling of an object's volume, we formulate rigidity and volume preservation constraints that are enforced to yield realistic shape deformations at interactive frame rates. Additionally, by specifying key frame poses of the deforming shape and optimizing the nodal displacements while targeting smooth interpolated motion, our algorithm extends to a motion planning framework for deformable objects. This allows reconstructing smooth and plausible deformable shape trajectories in the presence of possibly moving obstacles. The presented results illustrate that our framework can handle complex shapes at interactive rates and hence is a valuable tool for animators to realistically and efficiently model and interpolate deforming 3D shapes.

Real-Time Motion Planning for a Volleyball Robot Task Based on a Multi-Agent Technique

This paper takes the volleyball robot task as an example of a dynamic manipulation task and presents a multi-agent-based motion-planning framework for it. Each subtask in the motion planning is defined as an agent. The motion planning is accomplished by the solution of each agent activated by a blackboard. It is convenient to extend the module and enable real-time control compared to the hierarchy and subsumption architecture. In the solution of each subtask, one difference from the motion planning of other ball-playing robots is that simple fuzzy rules are adopted to determine the hitting position, which leads to smaller hitting velocities beneficial for task control. The other difference is that the task-based directional manipulability measure (TDMM) is used to optimize the robot configuration on the basis of optimal hitting path, so as to improve the manipulating ability on task direction. The simulation of a planar three-link manipulator ball-hitting task is implemented by the MATLAB/Simulink tool. The virtual target of the hitting motion is validated on the ABB manipulator by the ball-catching experiment.

Visual motion planning for mobile robots

This paper presents a novel framework for image-based motion planning, which we term Visual Motion Planning. The method skips the step of transferring image features back to robot pose, and hence makes motion plans directly in the image plane. Analogous to visual servo control, the visual motion planning concept takes advantage of the image features to achieve a direct and fast motion planning solution. It provides a virtual trajectory in the image plane for the robot to track with standard visual servoing techniques. In this paper, we show the result of applying the idea to simulated 2-D and 3-D mobile robot systems. Within this motion planning paradigm, we also discuss the mechanisms for taking advantage of surplus features and incorporating image-based constraints, such as requiring that the images remain in the field of view. Experimental results using a Pioneer AT ground mobile robot are presented, showing excellent agreement with theory.

An Objective-Based Framework for Motion Planning under Sensing and Control Uncertainties

The authors consider the problem of determining robot motion plans under sensing and control uncertainties. Traditional approaches are often based on methodology known as preimage planning, which involves worst-case analysis. The authors have developed a gen eral framework for determining feedback strategies by blending ideas from stochastic optimal control and dynamic game theory with traditional preimage planning concepts. This generalizes classical preimages to performance preimages and preimage planning for de signing motion strategies with information feedback. For a given problem, one can define a performance criterion that evaluates any executed trajectory of the robot. The authors present methods for selecting a motion strategy that optimizes this criterion under either nondeterministic uncertainty (resulting in worst-case analysis) or probabilistic uncertainty (resulting in expected-case analysis). The authors present dynamic programming-based algorithms that nu merically compute performance preimages and optimal strategies; several computed examples of forward projections, performance preimages, and optimal strategies are presented.

A framework for robot motion planning with sensor constraints

Visual feedback can play a crucial role in a dynamic robotic task such as the interception of a moving target. To utilize the feedback effectively, there is a need to develop robot motion planning techniques that also take into account properties of the sensed data. We propose a motion planning framework that achieves this with the help of a space called the perceptual control manifold (PCM) defined on the product of the robot configuration space and an image-based feature space. We show how the task of intercepting a moving target can be mapped to the PCM, using image feature trajectories of the robot end-effector and the moving target. This leads to the generation of motion plans that satisfy various constraints and optimality criteria derived from the robot kinematics, the control system, and the sensing mechanism. Specific interception tasks are analyzed to illustrate this vision-based planning technique.