Local Randomization in Neighbor Selection Improves PRM Roadmap Quality

Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. This work proposes a new neighbor selection policy called LocalRand(k, k'), that first computes the k' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. A methodology for selecting the parameters k and k' is provided, and an experimental comparison for both rigid and articulated robots show that LocalRand results in roadmaps that are better connected than the traditional k-closest or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to the cost of k-closest

Adaptive local learning in sampling based motion planning for protein folding

BACKGROUND: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. RESULTS: We develop a local learning algorithm that exploits the past performance of methods within the neighborhood of the current connection attempts as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52–114 residues. We compare the success rate when using our methods and other methods. We demonstrate a clear need for learning (i.e., only learning methods were able to validate against all available experimental data) and show that local learning is superior to global learning producing, in many cases, significantly higher quality results than the other methods. CONCLUSIONS: We present an algorithm that uses local learning to select appropriate connection methods in the context of roadmap construction for protein folding. Our method removes the burden of deciding which method to use, leverages the strengths of the individual input methods, and it is extendable to include other future connection methods

Bi-objective Motion Planning Approach for Safe Motions: Application to a Collaborative Robot

International audienceAccepted version freely available here: [ http://bit.ly/2qlyjJ6 ] Online version via SpringerLink: [ http://link.springer.com/article/10.1007/s10846-019-01110-1 ] Abstract: This paper presents a new bi-objective safety-oriented path planning strategy for robotic manipulators. Integrated into a sampling-based algorithm, our approach can successfully enhance the task safety by guiding the expansion of the path towards the safest configurations. Our safety notion consists of avoiding dangerous situations, e.g. being very close to the obstacles, human awareness, e.g. being as much as possible in the human vision field, as well as ensuring human safety by being as far as possible from human with hierarchical priority between human body parts. Experimental validations are conducted in simulation and on the real Baxter research robot. They revealed the efficiency of the proposed method, mainly in the case of a collaborative robot sharing the workspace with humans

Improved Sampling Based Motion Planning Through Local Learning

Every motion made by a moving object is either planned implicitly, e.g., human natural movement from one point to another, or explicitly, e.g., pre-planned information about where a robot should move in a room to effectively avoid colliding with obstacles. Motion planning is a well-studied concept in robotics and it involves moving an object from a start to goal configuration. Motion planning arises in many application domains such as robotics, computer animation (digital actors), intelligent CAD (virtual prototyping and training) and even computational biology (protein folding and drug design). Interestingly, a single class of planners, sampling-based planners have proven effective in all these domains. Probabilistic Roadmap Methods (PRMs) are one type of sampling-based planners that sample robot configurations (nodes) and connect them via viable local paths (edges) to form a roadmap containing representative feasible trajectories. The roadmap is then queried to find solution paths between start and goal configurations. Different PRM strategies perform differently given different input parameters, e.g., workspace environments and robot definitions. Motion planning, however, is computationally hard – it requires geometric path planning which has been shown to be PSPACE hard, complex representational issues for robots with known physical, geometric and temporal constraints, and challenging mapping/representing requirements for the workspace environment. Many important environments, e.g., houses, factories and airports, are heterogeneous, i.e., contain free, cluttered and narrow spaces. Heterogeneous environments, however, introduce a new set of problems for motion planning and PRM strategies because there is no ideal method suitable for all regions in the environment. In this work we introduce a technique that can adapt and apply PRM methods suitable for local regions in an environment. The basic strategy is to first identify a local region of the environment suitable for the current action based on identified neighbors. Next, based on past performance of methods in this region, adapt and pick a method to use at this time. This selection and adaptation is done by applying machine learning. By performing the local region creation in this dynamic fashion, we remove the need to explicitly partition the environment as was done in previous methods and which is difficult to do, slows down performance and includes the difficult process of determining what strategy to use even after making an explicit partitioning. Our method handles and removes these overheads. We show benefits of this approach in both planning robot motions and in protein folding simulations. We perform experiments on robots in simulation with different degrees of freedom and varying levels of heterogeneity in the environment and show an improvement in performance when our local learning method is applied. Protein folding simulations were performed on 23 proteins and we note an improvement in the quality of pathways produced with comparable performance in terms of time needed to build the roadmap

ADAPTIVE PROBABILISTIC ROADMAP CONSTRUCTION WITH MULTI-HEURISTIC LOCAL PLANNING

The motion planning problem means the computation of a collision-free motion for a movable object among obstacles from the given initial placement to the given end placement. Efficient motion planning methods have many applications in many fields, such as robotics, computer aided design, and pharmacology. The problem is known to be PSPACE-hard. Because of the computational complexity, practical applications often use heuristic or incomplete algorithms. Probabilistic roadmap is a probabilistically complete motion planning method that has been an object of intensive study over the past years. The method is known to be susceptible to the problem of “narrow passages”: Finding a motion that passes a narrow, winding tunnel can be very expensive. This thesis presents a probabilistic roadmap method that addresses the narrow passage problem with a local planner based on heuristic search. The algorithm is suitable for planning motions for rigid bodies and articulated robots including multirobot systems with many degrees-of-freedom. Variants of the algorithm are describe

Autonomous Navigation for Unmanned Aerial Systems - Visual Perception and Motion Planning

L'abstract è presente nell'allegato / the abstract is in the attachmen

High-Dimensional Motion Planning and Learning Under Uncertain Conditions

Many existing path planning methods do not adequately account for uncertainty. Without uncertainty these existing techniques work well, but in real world environments they struggle due to inaccurate sensor models, arbitrarily moving obstacles, and uncertain action consequences. For example, picking up and storing childrens toys is a simple task for humans. Yet, for a robotic household robot the task can be daunting. The room must be modeled with sensors, which may or may not detect all the strewn toys. The robot must be able to detect and avoid the child who may be moving the very toys that the robot is tasked with cleaning. Finally, if the robot missteps and places a foot on a toy, it must be able to compensate for the unexpected consequences of its actions. This example demonstrates that even simple human tasks are fraught with uncertainties that must be accounted for in robotic path planning algorithms. This work presents the first steps towards migrating sampling-based path planning methods to real world environments by addressing three different types of uncertainty: (1) model uncertainty, (2) spatio-temporal obstacle uncertainty (moving obstacles) and (3) action consequence uncertainty. Uncertainty is encoded directly into path planning through a data structure in order to successfully and efficiently identify safe robot paths in sensed environments with noise. This encoding produces comparable clearance paths to other planning methods which are a known for high clearance, but at an order of magnitude less computational cost. It also shows that formal control theory methods combined with path planning provides a technique that has a 95% collision-free navigation rate with 300 moving obstacles. Finally, it demonstrates that reinforcement learning can be combined with planning data structures to autonomously learn motion controls of a seven degree of freedom robot despite a low computational cost despite the number of dimensions