BACKGROUND: The domain-linker-domain (DLD) architecture is commonly found in proteins, where flexible linkers connect consecutive domains and regulate their relative spatial positioning. Often, these linkers present partially structured elements that modulate inter-domain dynamics, directly influencing their function. From a protein design perspective, tuning the relative position and orientation of domains via the linker offers opportunities to modulate biological activity. Despite their relevance, analyzing conformational ensembles of DLD proteins remains a challenge, thereby limiting the structural insights that can be extracted. RESULTS: We present DL3D, a robotics-inspired visualization tool that enables intuitive analysis of the conformational space sampled by DLD proteins. DL3D discretizes the relative positions of the two domains at the linker ends and projects each conformation onto a 3D voxel map, where density is represented in grayscale to highlight the most probable configurations. In addition, quaternion-based operations allow the analysis of relative domain orientations. CONCLUSION: DL3D facilitates the structural investigation of highly flexible proteins composed of well-folded domains connected by flexible linkers. Beyond visualization, the tool supports downstream analyses such as low-dimensional conformational clustering. DL3D is implemented as a Python package and is available at: https://gitlab.laas.fr/moma/methods/analysis/dl3d/ . A Jupyter notebook with usage examples is also provided.

Taking into account the effects of parameter uncertainties in the robot model is crucial to the robustness of motion generation. One approach to address this issue is to compute ‘uncertainty tubes’ enveloping the robot state for any combination of parameters within a given range, and to use these tubes to robustly check for collisions within a motion planning algorithm. However, computing these tubes for complex dynamical systems can be too computationally expensive due to the need to solve and integrate potentially numerous nonlinear ordinary differential equations (ODEs) associated with robot dynamics. To overcome this limitation, we propose a GRU-based architecture that provides fast and accurate estimation of these uncertainty tubes. We demonstrate that GRUs achieve the best compromise between prediction accuracy, prediction time, and network size compared to basic RNNs and LSTMs, justifying our choice. Finally, we showcase the efficiency of the learning process within a motion planning framework for an aerial vehicle.

MOTIVATION: Characterizing the structure of flexible proteins, particularly within the realm of intrinsic disorder, presents a formidable challenge due to their high conformational variability. Currently, their structural representation relies on (possibly large) conformational ensembles derived from a combination of experimental and computational methods. The detailed structural analysis of these ensembles is a difficult task, for which existing tools have limited effectiveness. RESULTS: This study proposes an innovative extension of the concept of contact maps to the ensemble framework, incorporating the intrinsic probabilistic nature of disordered proteins. Within this framework, a conformational ensemble is characterized through a weighted family of contact maps. To achieve this, conformations are first described using a refined definition of contact that appropriately accounts for the geometry of the inter-residue interactions and the sequence context. Representative structural features of the ensemble naturally emerge from the subsequent clustering of the resulting contact-based descriptors. Importantly, transiently populated structural features are readily identified within large ensembles. The performance of the method is illustrated by several use cases and compared with other existing approaches, highlighting its superiority in capturing relevant structural features of highly flexible proteins. AVAILABILITY AND IMPLEMENTATION: An open-source implementation of the method is provided together with an easy-to-use Jupyter notebook, available at https://gitlab.laas.fr/moma/WARIO.

Closed-loop state sensitivity [1], [2] is a recently introduced notion that can be used to quantify deviations of the closed-loop trajectory of a robot/controller pair against variations of uncertain parameters in the robot model. While local optimization techniques are used in [1], [2] to generate reference trajectories minimizing a sensitivity-based cost, no global planning algorithm considering this metric to compute collision-free motions robust to parametric uncertainties has yet been proposed. The contribution of this paper is to propose a global control-aware motion planner for optimizing a state sensitivity metric and producing collision-free reference motions that are robust against parametric uncertainties for a large class of complex dynamical systems. Given the prohibitively high computational cost of directly minimizing the state sensitivity using asymptotically optimal sampling-based tree planners, the proposed RRT*-based SAMP planner uses an appropriate steering method to first compute a (near) time-optimal and kinodynamically feasible trajectory that is then locally deformed to improve robustness and decrease its sensitivity to uncertainties. The evaluation performed on planar/full-3D quadrotor UAV models shows that the SAMP method produces low sensitivity robust solutions with a much higher performance than a planner directly optimizing the sensitivity.

SUMMARY: MoMA-LoopSampler is a sampling method that globally explores the conformational space of flexible protein loops. It combines a large structural library of three-residue fragments and a novel reinforcement-learning-based approach to accelerate the sampling process while maintaining diversity. The method generates a set of statistically likely loop states satisfying geometric constraints, and its ability to sample experimentally observed conformations has been demonstrated. This paper presents a web user interface to MoMA-LoopSampler through the illustration of a typical use-case. AVAILABILITY AND IMPLEMENTATION: MoMA-LoopSampler is freely available at: https://moma.laas.fr/applications/LoopSampler/. We recommend users to create an account, but anonymous access is possible. In most cases, jobs are completed within a few minutes. The waiting time may increase depending on the server load, but it very rarely exceeds an hour. For users requiring more intensive use, binaries can be provided upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Poly-alanine (polyA) regions are protein stretches mostly composed of alanines. Despite their abundance in eukaryotic proteomes and their association to nine inherited human diseases, the structural and functional roles exerted by polyA stretches remain poorly understood. In this work we study how the amino acid context in which polyA regions are settled in proteins influences their structure and function. RESULTS: We identified glycine and proline as the most abundant amino acids within polyA and in the flanking regions of polyA tracts, in human proteins as well as in 17 additional eukaryotic species. Our analyses indicate that the non-structuring nature of these two amino acids influences the α-helical conformations predicted for polyA, suggesting a relevant role in reducing the inherent aggregation propensity of long polyA. Then, we show how polyA position in protein N-termini relates with their function as transit peptides. PolyA placed just after the initial methionine is often predicted as part of mitochondrial transit peptides, whereas when placed in downstream positions, polyA are part of signal peptides. A few examples from known structures suggest that short polyA can emerge by alanine substitutions in α-helices; but evolution by insertion is observed for longer polyA. Our results showcase the importance of studying the sequence context of homorepeats as a mechanism to shape their structure-function relationships. AVAILABILITY AND IMPLEMENTATION: The datasets used and/or analyzed during the current study are available from the corresponding author onreasonable request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Loop portions in proteins are involved in many molecular interaction processes. They often exhibit a high degree of flexibility, which can be essential for their function. However, molecular modeling approaches usually represent loops using a single conformation. Although this conformation may correspond to a (meta-)stable state, it does not always provide a realistic representation. RESULTS: In this paper, we propose a method to exhaustively sample the conformational space of protein loops. It exploits structural information encoded in a large library of three-residue fragments, and enforces loop-closure using a closed-form inverse kinematics solver. A novel reinforcement-learning-based approach is applied to accelerate sampling while preserving diversity. The performance of our method is showcased on benchmark datasets involving 9-, 12- and 15-residue loops. In addition, more detailed results presented for streptavidin illustrate the ability of the method to exhaustively sample the conformational space of loops presenting several meta-stable conformations. AVAILABILITY AND IMPLEMENTATION: We are developing a software package called MoMA (for Molecular Motion Algorithms), which includes modeling tools and algorithms to sample conformations and transition paths of biomolecules, including the application described in this work. The binaries can be provided upon request and a web application will also be implemented in the short future. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nowadays, factories are required to increase production flexibility in order to manufacture small-lot variants, rapidly adapting to customer demands. Furthermore, manufacturing may involve complex manipulation tasks, usually performed by human workers. In such a context, traditional robotic systems are not competitive due to the huge costs of installation, maintenance and adaptation. A new generation of robots, equipped with multiple arms, is appearing as an attractive alternative because of their potential versatility and ability to execute intricate manipulation tasks. To facilitate the integration of these robots in a work-cell and a rapid adaptation to different tasks, easy-to-use programming interfaces and a high degree of autonomy are mandatory. Autonomous task and motion planning are particularly relevant in this context. In this paper, we present our recent progress in this direction. Hardware and software developments are explained in the context of a pilot dual-arm robot station that is being integrated in the production line of a big airplane manufacturer. First experimental results are also presented.

The study of the conformational energy landscape of a molecule is essential for the understanding of its physicochemical properties . This requires the exploration of a continuous, high-dimensional space to identify the most probable conformations and the transition paths between them. The problem is computationally difficult, in particular for highly-flexible biomolecules such as Intrinsically Disordered Proteins (IDPs). In recent years, a robotics-inspired algorithm called Transition-based Rapidly-exploring Random Tree (TRRT) has been proposed to solve this problem, and has been shown to provide good results with small and middle-sized biomolecules. Aiming to treat larger systems, we propose a hybrid strategy for the efficient parallelization of a multi-tree variant of TRRT, called Multi-TRRT, enabling an efficient execution in (possibly large) computer clusters. The parallel algorithm uses OpenMP multi-threading for computation inside each multi-core processor and MPI to perform the communication between processors. Results show a near-linear speedup for a wide range of cluster configurations. Although the paper mainly deals with the application of the proposed parallel algorithm to the investigation of biomolecules, the explanations concerning the methods are general, aiming to inspire future work on the parallelization of related algorithms.

Sampling-based algorithms for path planning, such as the Rapidly-exploring Random Tree (RRT), have achieved great success, thanks to their ability to efficiently solve complex high-dimensional problems. However, standard versions of these algorithms cannot guarantee optimality or even high-quality for the produced paths. In recent years, variants of these methods, such as T-RRT, have been proposed to deal with cost spaces: by taking configuration-cost functions into account during the exploration process, they can produce high-quality (i.e., low-cost) paths. Other novel variants, such as RRT*, can deal with optimal path planning: they ensure convergence toward the optimal path, with respect to a given path-quality criterion. In this paper, we propose to solve a complex problem encompassing this two paradigms: optimal path planning in a cost space. For that, we develop two efficient sampling-based approaches that combine the underlying principles of RRT* and T-RRT. These algorithms, called T-RRT* and AT-RRT, offer the same asymptotic optimality guarantees as RRT*. Results presented on several classes of problems show that they converge faster than RRT* toward the optimal path, especially when the topology of the search space is complex and/or when its dimensionality is high.

The overall performance of sampling-based motion planning algorithms strongly depends on the use of suitable sampling and connection strategies, as well as on the accuracy of the distance metric considered to select neighbor states. Defining appropriate strategies and metrics is particularly hard when considering robot dynamics, which is required to treat constrained motion planning problems for quadrotors. This paper presents an accurate but computationally fast quasi-metric to determine the proximity of dynamic states of a quadrotor, and an incremental state-space sampling technique to avoid generating local trajectories that violate kinodynamic constraints. Results show that the integration of the proposed techniques in RRT-based and PRM-based algorithms can drastically decrease computing time, up to two orders of magnitude.

Obtaining accurate representations of energy landscapes of biomolecules such as proteins and peptides is central to structure-function studies. Peptides are particularly interesting, as they exploit structural flexibility to modulate their biological function. Despite their small size, peptide modeling remains challenging due to the complexity of the energy landscape of such highly-flexible dynamic systems. Currently, only sampling-based methods can efficiently explore the conformational space of a peptide. In this paper, we suggest to combine two such methods to obtain a full characterization of energy landscapes of small yet flexible peptides. First, we propose a simplified version of the classical Basin Hopping algorithm to quickly reveal the meta-stable structural states of a peptide and the corresponding low-energy basins in the landscape. Then, we present several variants of a robotics-inspired algorithm, the Transition-based Rapidly-exploring Random Tree, to quickly determine transition state and transition path ensembles, as well as transition probabilities between meta-stable states. We demonstrate this combined approach on the terminally-blocked alanine.

Planning physically realistic and easily controllable motions of flying robots requires considering dynamics. This paper presents a local trajectory planner, based on a simplified dynamic model of quadrotors, which fits the requirements to be integrated into a global motion planning approach. It relies on a closed-form solution to compute curves in the kinodynamic state space that tend to minimize the flying time. These curves have suitable continuity properties and guarantee respect of physical limits of the system (i.e. bounds for the time-derivatives of the pose coordinates). The paper explains how this local planner can be used within different motion planning approaches that enable the treatment of difficult problems in constrained environments.

The Transition-based RRT (T-RRT) is a variant of RRT developed for path planning on a continuous cost space, i.e. a configuration space featuring a continuous cost function. It has been used to solve complex, high-dimensional problems in robotics and structural biology. In this paper, we propose a multiple-tree variant of T-RRT, named Multi-T-RRT. It is especially useful to solve ordering-and-pathfinding problems, i.e. to compute a path going through several unordered way-points. Using the Multi-T-RRT, such problems can be solved from a purely geometrical perspective, without having to use a symbolic task planner. We evaluate the Multi-T-RRT on several path planning problems and compare it to other path planners. Finally, we apply the Multi-T-RRT to a concrete industrial inspection problem involving an aerial robot.

The Transition-based RRT (T-RRT) algorithm enables to solve motion planning problems involving configuration spaces over which cost functions are defined, or cost spaces for short. T-RRT has been successfully applied to diverse problems in robotics and structural biology. In this paper, we aim at enhancing T-RRT to solve ever more difficult problems involving larger and more complex cost spaces. We compare several variants of T-RRT by evaluating them on various motion planning problems involving different types of cost functions and different levels of geometrical complexity. First, we explain why applying as such classical extensions of RRT to T-RRT is not helpful, both in a mono-directional and in a bidirectional context. Then, we propose an efficient Bidirectional T-RRT, based on a bidirectional scheme tailored to cost spaces. Finally, we illustrate the new possibilities offered by the Bidirectional T-RRT on an industrial inspection problem.

This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the message passing interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion-planning problems and analyze the various factors influencing their performance.

This paper addresses the problem of improving the performance of the Rapidly-exploring Random Tree (RRT) algorithm by parallelizing it. For scalability reasons we do so on a distributed-memory architecture, using the message-passing paradigm. We present three parallel versions of RRT along with the technicalities involved in their implementation. We also evaluate the algorithms and study how they behave on different motion planning problems.

This paper presents a new method to compute enveloping grasps with a multi-fingered robotic hand. The method is guided by the idea that a good grasp should maximize the contact surface between the held object and the hand's palmar surface. Starting from a given hand pregrasp configuration, the proposed method finds the hand poses that maximize this surface similarity. We use a surface descriptor that is based on a geodesic measure and on a continuous representation of the surfaces, unlike previous shape matching methods that rely on the Euclidean distance and/or discrete representation (e.g. random point set). Using geodesic contours to describe local surfaces enables us to detect details such as a handle or a thin part. Once the surface matching returns a set of hand poses, sorted by similarity, a second step is performed to adjust the hand configuration with the purpose of eliminating penetration of the object. Lastly, the grasp stability is tested in order to definitely validate the candidate grasps.

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.

This paper builds on the combination of robotic path planning algorithms and molecular modeling methods for computing large-amplitude molecular motions, and introduces voxel maps as a computational tool to encode and to represent such motions. We investigate several applications and show results that illustrate the interest of such representation.

This paper proposes a planning framework to deal with the problem of computing the motion of a robot with dual arm/hand, during an object pick-and-place task. We consider the situation where the start and goal configurations of the object constrain the robot to grasp the object with one hand, to give it to the other hand, before placing it in its final configuration. To realize such a task, the proposed framework treats the grasp computation, for one or two multi-fingered hands, of an arbitrarily-shaped object, the exchange configuration and finally the motion of the robot arms and body. In order to improve the planner performance, a context-independent grasp list is computed offline for each hand and for the given object as well as computed offline roadmap that will be adapted according to the environment composition. Simulation results show the planner performance on a complex scenario.

This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path-quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed.

Understanding life at the atomic level requires the development of new methodologies, able to overcome the limitations of available experimental and computational techniques for the analysis of processes involving molecular motions. With this goal in mind, we develop new methods, combining robotic path planning algorithms and molecular modeling techniques, for computing large-amplitude motions. This paper builds on these new methods, and introduces voxel maps as a computational tool to encode and to represent such motions. Voxel maps can be used to represent relative motions of two molecules, as well as conformational changes in macromolecules. We investigate several applications and show results that illustrate the interest of such representation. In particular, voxel maps are used to display channels into proteins, to analyze protein-ligand specificity, and to represents protein loop and domain motions.

This paper presents a new method for planning motions of multi-arm systems in constrained workspaces, for which state-of-the-art planners behave poorly. The method is based on the decomposition of the system into parts. Compact roadmaps are first computed for each part, and then, a super-graph is constructed by the composition of elementary roadmaps. Results presented for a three-arm system and a model of the complex DLR's Justin robot show a significant performance gain of such a two-stage roadmap construction method with respect to single-stage methods applied to the whole system.

This paper presents a new method called Transition-based RRT (T-RRT) for path planning in continuous cost spaces. It combines the exploration strength of the RRT algorithm that rapidly grow random trees toward unexplored regions of the space, with the efficiency of stochastic optimization methods that use transition tests to accept or to reject a new potential state. This planner also relies on the notion of minimal work path that gives a quantitative way to compare path costs. The method also integrates self tuning of a parameter controlling its exploratory behavior. It yields to solution paths that efficiently follow low cost valleys and the saddle points of the cost space. Simulation results show that the method can be applied to a large set of applications including terrain costmap motions or planning low cost motions for free flying or articulated robots.

Sampling-based path planning algorithms are powerful tools for computing constrained disassembly motions. This paper presents a variant of the Rapidly-exploring Random Tree (RRT) algorithm particularly devised for the disassembly of objects with articulated parts. Configuration parameters generally play two different roles in this type of problems: some of them are essential for the disassembly task, while others only need to move if they hinder the progress of the disassembly process. The proposed method is based on such a partition of the configuration parameters. Results show a remarkable performance improvement as compared to standard path planning techniques. The paper also shows practical applications of the presented algorithm in robotics and structural bioinformatics.

This paper addresses the problem of computing pathways for a ligand to exit from the active site of a protein. Such problem can be formulated as a mechanical disassembly problem for two articulated objects. Its solution requires searching paths in a constrained high-dimensional configuration-space. Indeed, the ligand passageway inside the protein is often extremely cluttered so that current path planning techniques are unable to solve the disassembly problem in reasonable computing time. The techniques presented in this paper are based on the RRT algorithm. First we discuss some simple and general modifications of the basic algorithm that significantly improve its performance. Then we describe a new variant of the planner that treats ligand and protein degrees of freedom separately. This new algorithm outperforms the basic RRT, particularly for very constrained problems, and is able to handle models with hundreds of degrees of freedom. We analyze the effects of each RRT variant via several examples of different complexity. Although discussions and results of this paper focus on molecular models, the ideas behind the algorithms are general and can be applied to path planners for disassembling articulated mechanical parts.

This paper introduces box approximations as a new tool for path planning of closed-loop linkages. Box approximations are finite collections of rectangloids that tightly envelop the robot's free space at a desired resolution. They play a similar role to that of approximate cell decompositions for open-chain robots - they capture the free-space connectivity in a multi-resolutive fashion and yield rectangloid channels enclosing collision-free paths - but have the additional property of enforcing the satisfaction of loop closure constraints frequently arising in articulated linkages. We present an efficient technique to compute such approximations and show how resolution-complete path planners can be devised using them. To the authors' knowledge, this is the first space-decomposition approach to closed-loop linkage path planning proposed in the literature.

Despite the increasing interest in parallel mechanisms during the last years, few researchers have addressed the motion planning problem for such systems. The few existing techniques lie in a representation of the workspace of the mechanism (or its boundary). However, obtaining this representation is generally too difficult, only partial solutions exist for particular cases. In this paper we propose a general approach based on probabilistic motion planning techniques. This approach does not need any modeling of the robot's workspace. It combines random sampling techniques with simple but general geometric algorithms that guide the sampling toward feasible configurations satisfying the closure constraints of the parallel mechanism. The efficiency and the generality of the method are demonstrated onto several complex mechanisms mode up with serial or parallel associations of Stewart platforms, or created with several redundant robots manipulating an object.

Closed kinematic chains in mechanical systems represent a challenge for their motion analysis, and therefore, for path planning. Closed mechanisms appear in different areas where path planning algorithms are applied. We propose a method to handle them within probabilistic roadmap (PRM) techniques. This method is an extension of the approach proposed by Han et al. (2000). Our main contribution concerns the generation of random configurations. The structure of the mechanism is analyzed in a preprocessing step. Then, in the roadmap construction phase, an algorithm called the random loop generator uses data from this analysis. This algorithm increases the probability of randomly generating valid configurations of the closed mechanism. Experimental results demonstrate the efficiency of the approach.

This paper addresses the manipulation planning problem which deals with motion planning for robots manipulating movable objects among static obstacles. We propose a manipulation planner capable of handling continuous domains for modeling both the possible grasps and the stable placements of a single movable object, rather than discrete sets generally assumed by the existing planners. The algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. This property leads to reduce the problem by structuring the search-space. It allows us to devise a manipulation planner that directly captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. First experiments demonstrate the feasibility and the efficiency of the approach.

An important skill of autonomous robots is the ability to carry out manipulation tasks. The solution to a manipulation problem generally consists in a sequence of elementary paths where an object is moved by a robot or it stays at a stable placement while the robot performs a re-grasping motion. Most existing planners require a finite set of configurations to achieve this task decomposition. We recently proposed an approach to automatically compute such intermediate configurations from continuous sets of stable placements and possible grasps of the movable object. This paper describes an improved algorithm based on this approach. It also presents several complex manipulation problems that illustrate the efficiency of the planner.

Reports on our current effort for applying probabilistic path planning techniques to logistics and operation in huge industrial installations (e.g., power plants). We show how the specific domain constraints impose a dedicated software architecture to take advantage of the generality of probabilistic approaches. In addition, such an architecture should be compatible with existing CAD systems making critical the interface issues. We conclude with three study cases currently under development within the European project MOLOG.