Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

Task Space Regions: A framework for pose-constrained manipulation planning

Abstract: We present a manipulation planning framework that allows robots to plan in the presence of constraints on end-effector pose, as well as other common constraints. The framework has three main components: constraint representation, constraint-satisfaction strategies, and a general planning algorithm. These components come together to create an efficient and probabilistically complete manipulation planning algorithm called the Constrained BiDirectional Rapidly-exploring Random Tree (RRT) - CBiRRT2. The underpinning of our framework for pose constraints is our Task Space Regions (TSRs) representation. TSRs are intuitive to specify, can be efficiently sampled, and the distance to a TSR can be evaluated very quickly, making them ideal for sampling-based planning. Most importantly, TSRs are a general representation of pose constraints that can fully describe many practical tasks. For more complex tasks, such as manipulating articulated objects, TSRs can be chained together to create more complex end-effector pose constraints. TSRs can also be intersected, a property that we use to plan with pose uncertainty. We provide a detailed description of our framework, prove probabilistic completeness for our planning approach, and describe several real-world example problems that illustrate the efficiency and versatility of the TSR framework.

Optimal path planning of mobile robots: A review

Abstract: Mobile robots are increasingly used in automated industrial environments. There are also other applications like planet exploration, surveillance, landmine detection, etc. In all these applications, in order that the mobile robots perform their tasks, collision-free path planning is a prerequisite. This article provides an overview of the research progress in path planning of a mobile robot for off-line as well as on-line environments. Commonly used classic and evolutionary approaches of path planning of mobile robots have been addressed. Review shows that evolutionary optimization algorithms are computationally efficient and hence are increasingly being used in tandem with classic approaches while handling Non-deterministic Polynomial time hard (NP-hard) problems. Also, challenges involved in developing a computationally efficient path planning algorithm are addressed. Key words: Path planning, mobile robot, off-line environment, on-line environment, classic, evolutionary algorithms.

Motion planning under uncertainty using iterative local optimization in belief space

Abstract: We present a new approach to motion planning under sensing and motion uncertainty by computing a locally optimal solution to a continuous partially observable Markov decision process (POMDP). Our approach represents beliefs (the distributions of the robot's state estimate) by Gaussian distributions and is applicable to robot systems with non-linear dynamics and observation models. The method follows the general POMDP solution framework in which we approximate the belief dynamics using an extended Kalman filter and represent the value function by a quadratic function that is valid in the vicinity of a nominal trajectory through belief space. Using a belief space variant of iterative LQG (iLQG), our approach iterates with second-order convergence towards a linear control policy over the belief space that is locally optimal with respect to a user-defined cost function. Unlike previous work, our approach does not assume maximum-likelihood observations, does not assume fixed estimator or control gains, takes into account obstacles in the environment, and does not require discretization of the state and action spaces. The running time of the algorithm is polynomial (O[n6]) in the dimension n of the state space. We demonstrate the potential of our approach in simulation for holonomic and non-holonomic robots maneuvering through environments with obstacles with noisy and partial sensing and with non-linear dynamics and observation models.

Dynamics Filter - concept and implementation of online motion Generator for human figures

Abstract: Humanoid robots are required to make a variety of dynamics and even expressive motions in changing environments. However, the conventional methods for generating humanoid motions fail do achieve this requirement since they can only generate quite artificial and predefined motions through rather complicated optimization processes. In this paper, we propose the concept of "dynamics filter" which transforms a physically inconsistent motion into a consistent one, and provide an example of its implementation using feedback control and local optimization. The optimization is based on the equation of motion of constrained kinematic chains, which is derived from our previously proposed method for computing the dynamics of structure-varying kinematic chains. The proposed method can be applied to online motion generator of humanoid robots.

Coordinating locomotion and manipulation of a mobile manipulator

Abstract: A planning and control algorithm for coordinating the motion of a mobile manipulator is presented. The design criterion is to control the mobile platform so that the manipulator is maintained in a configuration which maximizes the manipulability measure. The effectiveness of the method was verified by simulations on two representative trajectories. The algorithm was implemented with an actual mobile manipulator and tested on one of the trajectories for comparison purposes. >