Planification de mouvement | EPFL Graph Search

La planification de mouvement (en anglais motion planning) est un ensemble de techniques mathématiques et informatiques permettant de calculer des trajectoires pour un système cinématique, avec pour contrainte l'absence de collision. Il existe deux principales catégories de méthodes pour la planification de mouvement :

La première est composée de méthodes dites déterministes, appelées ainsi car elles permettent de retrouver le même chemin à chaque exécution, sous réserve d'avoir des conditions initiales équivalentes. Les méthodes déterministes sont dites complètes en résolution car elles garantissent de trouver une solution, ou d'indiquer s'il n'y a pas de solution. Quelques algorithmes bien connus : champs de potentiel, décomposition cellulaire, diagrammes de Voronoï...
La seconde catégorie est composée des méthodes probabilistes. Ces méthodes ne sont pas complètes en résolution, mais elles garantissent de trouver une solution s'il en existe une. On dit qu'elles sont complètes

Rigid body dynamics simulation for robot motion planning

Alan Ettlin

The development of robot motion planning algorithms is inherently a challenging task. This is more than ever true when the latest trends in motion planning are considered. Some motion planners can deal with kinematic and dynamic constraints induced by the mechanical structure of the robot. Another class of motion planners fulfill various types of optimality conditions, yet others include means of dealing with uncertainty about the robot and its environment. Sensor-based motion planners gather information typically afflicted with errors about a partially known environment in order to plan a trajectory therein. In another research area it is investigated how multiple robots best cooperate to solve a common task. In order to deal with the complexity of developing motion planning algorithms, it is proposed in this document to resort to a simulation environment. The advantages of doing so are outlined and a system named Ibex presented which is well suited to support motion planner development. The developed framework makes use of rigid body dynamics algorithms as simulation kernel. Further, various components are included which integrate the simulation into existing engineering environments. Simulation content can be conveniently developed through extensions of well-established 3D modelling tools. The co-simulation with components from other domains of physics is provided by the integration into a leading dynamic modelling environment. Robotic actuator models can be combined with a rigid body dynamics simulation using this mechanism. The same configuration also allows to conveniently develop control algorithms for a rigid body dynamics setup and offers powerful tools for handling and analysing simulation data. The developed simulation framework also offers physics-based models for simulating various sensors, most prominently a model for sensor types based on wave propagation, such as laser range finding devices. Application examples of the simulation framework are presented from the mobile robotics rough-terrain motion planning domain. Three novel rough-terrain planning algorithms are presented which are extensions of known approaches. To quantify the navigational difficulty on rough terrain, a new generic measure named "obstacleness" is proposed which forms the basis of the proposed algorithms. The first algorithm is based on Randomised Potential Field Planners (RPP) and consequently is a local algorithm. The second proposed planner extends RRTconnect , a bi-directional Rapidly Exploring Random Tree (RRT) algorithm and biases exploration of the search space towards easily traversable regions. The third planner is an extension of the second approach and uses the same heuristic to grow a series of additional local RRTs. This allows it to plan trajectories through complex distributions of navigational difficulty benefitting from easy regions throughout the motion plan. A complete example is shown in which the proposed algorithms form the basis for sensor-based dynamic re-planning simulated in the presented framework. In the scenario, a simulated planetary rover navigates a long distance over rough terrain while gathering sensor data about the terrain topography. Where obstacles are sensed which interfere with the original motion plan, dynamic re-planning routines are applied to circumnavigate the hindrances. In the course of this document a complete simulation environment is presented by means of a theoretical background and application examples which can significantly support the development of robot motion planning algorithms. The framework is capable of simulating setups which fulfil the requirements posed by stateof-the-art motion planning algorithm development.

EPFL2006

Motion autonomy for humanoids: experiments on HRP-2 No. 14

This paper deals with whole-body motion planning and dynamic control for humanoid from two aspects: locomotion including manipulation and reaching. In the first part, we address a problem of simultaneous locomotion and manipulation planning that combines a geometric and kinematic motion planner with a dynamic humanoid motion generator. The second part deals with whole-body reaching tasks by using a generalized inverse kinematics (IK) method to fully exploit the high redundancy of the humanoid robot. Through experiments using humanoid platform HRP-2 No. 14 installed at LAAS-CNRS, we first verify the validity of each method. An integrated experiment is then presented that unifies the both results via visual perception to execute an object-fetching task. Copyright (C) 2009 John Wiley & Sons, Ltd.

2009

Navigation of Concentric Tube Continuum Robots using Optimal Control

Siva Prasad Chakri Dhanakoti, John Maddocks

Recently developed Concentric Tube Continuum Robots (CTCRs) are widely exploited in, for example in minimally invasive surgeries which involve navigating inside narrow body cavities close to sensitive regions. These CTCRs can be controlled by extending and rotating the tubes one inside the other in order to reach a target point or perform some task. The robot must deviate as little as possible from this narrow space and avoid damaging neighbouring tissue. We consider open-loop optimal control of CTCRs parameterized over pseudo-time, primarily aiming at minimizing the robot's working volume during its motion. External loads acting on the system like tip loads or contact with tissues are not considered here. We also discussed the inclusion of tip's orientation in the optimal framework to perform some tasks. We recall a quaternion-based formulation of the robot configuration, discuss discretization, develop optimization objectives addressing different criteria, and investigate their impact on robot path planning for several numerical examples. This optimal control framework can be applied to any backbone based continuum robot.

SCITEPRESS2022

Rigid body dynamics simulation for robot motion planning

Alan Ettlin

EPFL2006