Juan Cortés

Sampling-Based Path Planning on Configuration-Space Costmaps

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.

Manipulation Planning with Probabilistic Roadmaps

This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed 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. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems.

A random loop generator for planning the motions of closed kinematic chains using PRM methods

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.

Transition-based RRT for path planning in continuous cost spaces

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.

A manipulation planner for pick and place operations under continuous grasps and placements

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.

Disassembly Path Planning for Complex Articulated Objects

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.

Planning pick-and-place tasks with two-hand regrasping

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.

Probabilistic motion planning for parallel mechanisms

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 robots 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.

Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods

BackgroundObtaining atomic-scale information about large-amplitude conformational transitions in proteins is a challenging problem for both experimental and computational methods. Such information is, however, important for understanding the mechanisms of interaction of many proteins.MethodsThis paper presents a computationally efficient approach, combining methods originating from robotics and computational biophysics, to model protein conformational transitions. The ability of normal mode analysis to predict directions of collective, large-amplitude motions is applied to bias the conformational exploration performed by a motion planning algorithm. To reduce the dimension of the problem, normal modes are computed for a coarse-grained elastic network model built on short fragments of three residues. Nevertheless, the validity of intermediate conformations is checked using the all-atom model, which is accurately reconstructed from the coarse-grained one using closed-form inverse kinematics.ResultsTests on a set of ten proteins demonstrate the ability of the method to model conformational transitions of proteins within a few hours of computing time on a single processor. These results also show that the computing time scales linearly with the protein size, independently of the protein topology. Further experiments on adenylate kinase show that main features of the transition between the open and closed conformations of this protein are well captured in the computed path.ConclusionsThe proposed method enables the simulation of large-amplitude conformational transitions in proteins using very few computational resources. The resulting paths are a first approximation that can directly provide important information on the molecular mechanisms involved in the conformational transition. This approximation can be subsequently refined and analyzed using state-of-the-art energy models and molecular modeling methods.

Molecular Disassembly With Rrt-Like Algorithms

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.