Léonard Jaillet

Planning human-aware motions using a sampling-based costmap planner

This paper addresses the motion planning problem while considering Human-Robot Interaction (HRI) constraints. The proposed planner generates collision-free paths that are acceptable and legible to the human. The method extends our previous work on human-aware path planning to cluttered environments. A randomized cost-based exploration method provides an initial path that is relevant with respect to HRI and workspace constraints. The quality of the path is further improved with a local path-optimization method. Simulation results on mobile manipulators in the presence of humans demonstrate the overall efficacy of the approach.

Randomized tree construction algorithm to explore energy landscapes

In this work, a new method for exploring conformational energy landscapes is described. The method, called transition‐rapidly exploring random tree (T‐RRT), combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: on the one hand, it is naturally biased toward yet unexplored regions of the space; on the other, a Monte Carlo‐like transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved due to a self‐tuning mechanism. The method is able to efficiently find both energy minima and transition paths between them. As a proof of concept, the method is applied to two academic benchmarks and the alanine dipeptide.

Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

The situation arising in path planning under kinematic constraints, where the valid configurations define a manifold embedded in the joint ambient space, can be seen as a limit case of the well-known narrow corridor problem. With kinematic constraints, the probability of obtaining a valid configuration by sampling in the joint ambient space is not low but null, which complicates the direct application of sampling-based path planners. This paper presents the AtlasRRT algorithm, which is a planner especially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize a manifold and to coordinate the charts, forming an atlas that fully covers it. AtlasRRT simultaneously builds an atlas and a bidirectional rapidly exploring random tree (RRT), using the atlas to sample configurations and to grow the branches of the RRTs, and the RRTs to devise directions of expansion for the atlas. The efficiency of AtlasRRT is evaluated in several benchmarks involving high-dimensional manifolds embedded in large ambient spaces. The results show that the combined use of the atlas and the RRTs produces a more rapid exploration of the configuration space manifolds than existing approaches.

Randomized path planning on manifolds based on higher-dimensional continuation

Despite the significant advances in path planning methods, highly constrained problems are still challenging. In some situations, the presence of constraints defines a configuration space that is a non-parametrizable manifold embedded in a high-dimensional ambient space. In these cases, the use of sampling-based path planners is cumbersome since samples in the ambient space have low probability to lay on the configuration space manifold. In this paper, we present a new path planning algorithm specially tailored for highly constrained systems. The proposed planner builds on recently developed tools for higher-dimensional continuation, which provide numerical procedures to describe an implicitly defined manifold using a set of local charts. We propose to extend these methods focusing the generation of charts on the path between the two configurations to connect and randomizing the process to find alternative paths in the presence of obstacles. The advantage of this planner comes from the fact that it directly operates into the configuration space and not into the higher-dimensional ambient space, as most of the existing methods do.

The CUIK suite: analyzing the motion closed-chain multibody systems

Many situations in robotics require the analysis of the motions of complex multibody systems. These are sets of articulated bodies arising in a variety of devices, including parallel manipulators, multifingered hands, or reconfigurable mechanisms, but they appear in other domains too as mechanical models of molecular compounds or nanostructures. Closed kinematic chains arise frequently in such systems, either due to their morphology or due to geometric or contact constraints to fulfill during operation, giving rise to configuration spaces of an intricate structure. Despite appearing very often in practice, there is a lack of general software tools to analyze and represent such configuration spaces. Existing packages are oriented either to open-chain systems or to specific robot types, which hinders the analysis and development of innovative manipulators. This article describes the CUIK suite, a software toolbox for the kinematic analysis of general multibody systems. The implemented tools can isolate the valid configurations, determine the motion range of the whole multibody system or of some of its parts, detect singular configurations leading to control or dexterity issues, or find collision- and singularity-free paths between configurations. The toolbox has applications in robot design and programming and is the result of several years of research and development within the Kinematics and Robot Design group at IRI, Barcelona. It is available under GPLv3 license from http://www.iri.upc.edu/cuik.

Path planning with loop closure constraints using an atlas-based RRT

In many relevant path planning problems, loop closure constraints reduce the configuration space to a manifold embedded in the higher-dimensional joint ambient space. Whereas many progresses have been done to solve path planning problems in the presence of obstacles, only few work consider loop closure constraints. In this paper we present the AtlasRRT algorithm, a planner specially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize manifolds and to coordinate them forming an atlas. AtlasRRT simultaneously builds an atlas and a Rapidly-Exploring Random Tree (RRT), using the atlas to sample relevant configurations for the RRT, and the RRT to devise directions of expansion for the atlas. The new planner is advantageous since samples obtained from the atlas allow a more efficient extension of the RRT than state of the art approaches, where samples are generated in the joint ambient space.

EG-RRT: Environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles

Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance.

Asymptotically-optimal path planning on manifolds

This paper presents an approach for optimal path planning on implicitly-defined configuration spaces such as those arising, for instance, when manipulating an object with two arms or with a multifingered hand. In this kind of situations, the kinematic and contact constraints induce configuration spaces that are manifolds embedded in higher dimensional ambient spaces. Existing sampling-based approaches for path planning on manifolds focus on finding a feasible solution, but they do not optimize the quality of the path in any sense. Thus, the returned paths are usually not suitable for direct execution. Recently, RRT* and other similar asymptotically-optimal path planners have been proposed to generate high-quality paths in the case of globally parametrizable configuration spaces. In this paper, we propose to use higher dimensional continuation tools to extend RRT* to the case of implicitly-defined configuration spaces. Experiments in different problems validate the proposed approach.

Path Planning on Manifolds Using Randomized Higher-Dimensional Continuation

Despite the significant advances in path planning methods, problems involving highly constrained spaces are still challenging. In particular, in many situations the configuration space is a non-parametrizable variety implicitly defined by constraints, which complicates the successful generalization of sampling-based path planners. In this paper, we present a new path planning algorithm specially tailored for highly constrained systems. It builds on recently developed tools for Higher-dimensional Continuation, which provide numerical procedures to describe an implicitly defined variety using a set of local charts. We propose to extend these methods to obtain an efficient path planner on varieties, handling highly constrained problems. The advantage of this planner comes from that it directly operates into the configuration space and not into the higher-dimensional ambient space, as most of the existing methods do.

Efficient asymptotically-optimal path planning on manifolds

This paper presents an e cient approach for asymptotically-optimal path planning on implicitly-defined configuration spaces. Recently, several asymptotically-optimal path planners have been introduced, but they typically exhibit slow convergence rates. Moreover, these planners can not operate on the configuration spaces that appear in the presence of kinematic or contact constraints, such as when manipulating an object with two arms or with a multifingered hand. In these cases, the configuration space usually becomes an implicit manifold embedded in a higher-dimensional joint ambient space. Existing sampling-based path planners on manifolds focus on finding a feasible solution, but they do not optimize the quality of the path in any sense and, thus, the returned solution is usually not adequate for direct execution. In this paper, we adapt several techniques to accelerate the convergence of the asymptotically-optimal planners and we use higher-dimensional continuation tools to deal with the case of implicitly-defined configuration spaces. The performance of the proposed approach is evaluated through various experiments.