Collision Free Motion Planning on Graphs

Whole-body motion and footstep planning for humanoid robots with multi-heuristic search

Recent progress in motion planning has made it possible to determine homotopy inequivalent trajectories between an initial and terminal configuration in a robot configuration space. Current approaches have however either assumed the knowledge of differential one-forms related to a skeletonization of the collision space, or have relied on a simplicial representation of the free space. Both of these approaches are currently however not yet practical for higher dimensional configuration spaces. We propose 2D topological task projections (TTPs): mappings from the configuration space to 2-dimensional spaces where simplicial complex filtrations and persistent homology can identify topological properties of the high-dimensional free configuration space. Our approach only requires the availability of collision free samples to identify winding centers that can be used to determine homotopy inequivalent trajectories. We propose the Winding Augmented RRT and RRT* (WA-RRT/RRT*) algorithms using which homotopy inequivalent trajectories can be found. We evaluate our approach in experiments with configuration spaces of planar linkages with 2–10 degrees of freedom. Results indicate that our approach can reliably identify suitable topological task projections and our proposed WA-RRT and WA-RRT* algorithms were able to identify a collection of homotopy inequivalent trajectories in each considered configuration space dimension.

Collision-free motion planning allows robots to perform tasks in complex environments, but the conservative bounding curves during collision detection will reduce the robot's dexterity. This paper presents a novel algorithm for real-time motion planning of non-holonomic robots in dynamic scenes. To achieve this, the robot and the obstacles are generally decomposed into a series of superquadric objects. The expanded closed-form Minkowski sums are used to construct the velocity obstacle to deal with the updating orientation of the robot. The algorithm is extended to collision avoidance for different numbers and types of obstacles, which share the same workspace with the robot. We implement our algorithm in various simulations and experiments, where the robot has to avoid collisions with obstacles in real-time. The results demonstrate the effectiveness and efficiency of the proposed algorithm in collision-free motion planning at real-time computation rates and the enhanced dexterity control in complex environments.

Parametrized motion planning algorithms have high degrees of universality and flexibility, as they are designed to work under a variety of external conditions, which are viewed as parameters and form part of the input of the underlying motion planning problem. In this paper, we analyze the parametrized motion planning problem for the motion of many distinct points in the plane, moving without collision and avoiding multiple distinct obstacles with a priori unknown positions. This complements our prior work Cohen et al. [3] (SIAM J. Appl. Algebra Geom. 5, 229–249), where parametrized motion planning algorithms were introduced, and the obstacle-avoiding collision-free motion planning problem in three-dimensional space was fully investigated. The planar case requires different algebraic and topological tools than its spatial analog.

Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.

VLSI-efficient schemes for high-speed construction of tangent graph

A method of planning sub-optimal collision-free motion for redundant manipulators is presented. It is based on using a penalty function approach and scaling the robot dynamic equation to satisfy actuator constraints. The proposed method allows real time computations. A computer example involving a planar redundant manipulator of three revolute kinematics pairs which operates in a work space with obstacles, is also presented.

Path planning has been widely studied by computer scientists. However, it is a very simplified version of the motion planning problems occurring in robotics. This paper examines extensions yielding two important issues: controllability and recognizability. The controllability issue arises when the number of controls is smaller than the number of independent parameters defining the robot's configuration: Can the motions span the configuration space? The recognizability issue occurs when there are errors in control and sensing: Can the robot recognize goal achievement? Both issues have interesting impact on the computational complexity of motion planning. This paper will also discuss a new path planning scheme based on random sampling of configuration space, to deal with many-degree-of-freedom robots. The blend of controllability, recognizability, and complexity issues discussed in this paper is unique to robotics and its study is key to the development of autonomous robots.

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 mopped to the PCM. 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.

This work presents an efficient framework to generate a motion plan of a robot with high degrees of freedom (e.g., a humanoid robot). High-dimensionality of the robot configuration space often leads to difficulties in utilizing the widely-used motion planning algorithms, since the volume of the decision space increases exponentially with the number of dimensions. To handle complications arising from the large decision space, and to solve a corresponding motion planning problem efficiently, two key concepts are adopted in this work: First, the Gaussian process latent variable model (GP-LVM) is utilized for low-dimensional representation of the original configuration space. Second, an approximate inference algorithm is used, exploiting through the duality between control and estimation, to explore the decision space and to compute a high-quality motion trajectory of the robot. Utilizing the GP-LVM and the duality between control and estimation, we construct a fully probabilistic generative model with which a high-dimensional motion planning problem is transformed into a tractable inference problem. Finally, we compute the motion trajectory via an approximate inference algorithm based on a variant of the particle filter. The resulting motions can be viewed in the supplemental video. ( https://youtu.be/kngEaOR4Esc )

Different learning modes and mechanisms allow faster and better acquisition of skills as widely studied in humans and many animals. Specific neurons, called mirror neurons, are activated in the same way whether an action is performed or simply observed. This suggests that observing others performing movements allows to reinforce our motor abilities. This implies the presence of a biological mechanism that allows creating models of others' movements and linking them to the self-model for achieving mirroring. Inspired by such ability, we propose to build a map of movements executed by a teaching agent and mirror the agent's state to the robot's configuration space. Hence, in this study, a neural network is proposed to integrate a motor cortex-like differential map transforming motor plans from task-space to joint-space motor commands and a static map correlating joint-spaces of the robot and a teaching agent. The differential map is developed based on spiking neural networks while the static map is built as a self-organizing map. The developed neural network allows the robot to mirror the actions performed by a human teaching agent to its own joint-space and the reaching skill is refined by the complementary examples provided. Hence, experiments are conducted to quantify the improvement achieved thanks to the proposed learning approach and control scheme.

GINKO, an integrated learning and planning system that has been applied to an autonomous mobile robot domain, is described. The goal of GINKO's learning system is to partition the robot's configuration space into regions in which actions exhibit a uniform qualitative behavior. This partitioning is performed by an inductive learning algorithm that classifies regions of the configuration space with regard to the effects of the robot's actions when executed in those regions. GINKO's learning is driven by its attempts to perform tasks. Thus, the learned effects of actions are directly applicable to normal system performance. >

We introduce an algorithm to achieve complete sensor coverage of complex, three-dimensional structures surveyed by an autonomous agent with multiple degrees of freedom. Motivated by the application of an ocean vehicle performing an autonomous ship hull inspection, we consider a planning problem for a fully-actuated, six degree-of-freedom hovering AUV using a bathymetry sonar to inspect the complex structures underneath a ship hull. We consider a discrete model of the structure to be inspected, requiring only that the model be provided in the form of a closed triangular mesh. A dense graph of feasible paths is constructed in the robot's configuration space until the set of edges in the graph allows complete coverage of the structure. Then, we approximate the minimum-cost closed walk along the graph which observes 100% of the structure. We emphasize the embedding of observations within the edges of the graph as a means of utilizing all available sensor data in planning the inspection.

Traditional motion planning can be computationally burdensome for practical robots, due to extensive collision checking and considerable iterative propagation of cost values. We present a novel neural network architecture which can directly generate the cost-to-go (c2g) function for a given configuration space and a goal configuration. The output of the network is a continuous function whose gradient in configuration space can be directly used to generate trajectories in motion planning without the need for protracted iterations or extensive collision checking. This higher order function (i.e. a function generating another function) representation lies at the core of our motion planning architecture, c2g-HOF, which can take a workspace as input, and generate the cost-to-go function over the configuration space map (C-map). Simulation results for 2D and 3D environments show that c2g-HOF can be orders of magnitude faster at execution time than methods which explore the configuration space during execution. We also present an implementation of c2g-HOF which generates trajectories for robot manipulators directly from an overhead image of the workspace.

Motion planning is a rapidly growing area of research in robotics where topological methods have been applied [14]. From the topological point of view, a motion planning algorithm has as input two states of the motion, i.e., two arbitrary points in the configuration space X . As output it has to provide a continuous path between the two chosen states. In other words, such an algorithm consists of a section (not necessarily continuous) s : X × X −→ XI of the unpointed path fibration p : XI −→ X × X, p(σ) = ( σ(0),σ(1) ) , where XI denotes the space of free paths on X .