Randomized planning of dynamic motions avoiding forward singularities

The final publication is available at link.springer.comForward singularities, also known as direct, or actuator singularities, cause many problems to the planning and control of robot motions. They yield position errors and rigidity losses of the robot, and generate unbounded actions in typical control laws. To circumvent these issues, this paper proposes a randomized kinodynamic planner for computing trajectories avoiding such singularities. Given initial and final states for the robot, the planner attempts to connect them by means of a dynamically-feasible, singularity-free trajectory that also respects the force limits of the actuators. The performance of the strategy is illustrated in simulation by means of a parallel robot performing a highly- dynamic task.Peer ReviewedPostprint (author's final draft

Collided path replanning in dynamic environments using RRT and Cell decomposition algorithms

The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness.Plánování pohybu robota je důležitou součástí modelování funkcí robotů. Plán řídí pohyby robota. V této práci se algoritmus rozkladu na buňky používá k nalezení cesty pracovní plochou a algoritmus prozkoumání náhodného stromu (RRT) k ověření cesty skutečným prostorem. Byly navrženy dvě metody ke zlepšení navigace všesměrové pohyblivého robota částečně změněnou pracovní plochou. Za prvé, plánovač vytvoří RRT strom a vychyluje jeho růst směrem k bodu na cestě. Plánovač snižuje pravděpodobnost výběru dalšího bodu, když je detekována kolize, což zase zvyšuje expanzi RRT na volném prostoru. Druhá metoda používá shodný plánovač pro napojení bodů cesty. Pokud je detekována kolize, plánovač upravuje RRT na obou stranách kolizního segmentu. Navrhované metody jsou porovnávány s dalšími používanými přístupy, přečemž simulace ukazuje lepší výsledky z hlediska účinnosti a úplnosti plánování cesty.The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness

Scheduling access to shared space in multi-robot systems

Through this study, we introduce the idea of applying scheduling techniques to allocate spatial resources that are shared among multiple robots moving in a static environment and having temporal constraints on the arrival time to destinations. To illustrate this idea, we present an exemplified algorithm that plans and assigns a motion path to each robot. The considered problem is particularly challenging because: (i) the robots share the same environment and thus the planner must take into account overlapping paths which cannot happen at the same time; (ii) there are time deadlines thus the planner must deal with temporal constraints; (iii) new requests arrive without a priori knowledge thus the planner must be able to add new paths online and adjust old plans; (iv) the robot motion is subject to noise thus the planner must be reactive to adapt to online changes. We showcase the functioning of the proposed algorithm through a set of agent-based simulations

FASTKIT: A Mobile Cable-Driven Parallel Robot for Logistics

International audienceThe subject of this paper is about the design, modeling, control and performance evaluation of a low cost and versatile robotic solution for logistics. The robot under study, named FASTKIT, is obtained from a combination of mobile robots and a Cable-Driven Parallel Robot (CDPR). FASTKIT addresses an industrial need for fast picking and kitting operations in existing storage facilities while being easy to install, keeping existing infrastructures and covering large areas. The FASTKIT prototype consists of two mobile bases that carry the exit points of the CDPR. The system can navigate autonomously to the area of interest. Once the desired position is attained, the system deploys the CDPR in such a way that its workspace corresponds to the current task specification. The system calculates the required mobile base position from the desired workspace and ensures the controllability of the platform during the deployment. Once the system is successfully deployed, the set of stabilizers are used to ensure the prototype structural stability. Then the prototype gripper is moved accurately by the CDPR at high velocity over a large area by controlling the cable tension

Multi-agent Poli-RRT* Optimal constrained RRT-based planning for multiple vehicles with feedback linearisable dynamics

Planning a trajectory that is optimal according to some performance criterion, collision-free, and feasible with respect to dynamic and actuation constraints is a key functionality of an autonomous vehicle. Poli-RRT* is a sample-based planning algorithm that serves this purpose for a single vehicle with feedback linearisable dynamics. This paper extends Poli-RRT* to a multi-agent cooperative setting where multiple vehicles share the same environment and need to avoid each other besides some static obstacles

Sampling-based Algorithms for Optimal Motion Planning

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.Comment: 76 pages, 26 figures, to appear in International Journal of Robotics Researc

Value Iteration for Simple Stochastic Games: Stopping Criterion and Learning Algorithm

Simple stochastic games can be solved by value iteration (VI), which yields a sequence of under-approximations of the value of the game. This sequence is guaranteed to converge to the value only in the limit. Since no stopping criterion is known, this technique does not provide any guarantees on its results. We provide the first stopping criterion for VI on simple stochastic games. It is achieved by additionally computing a convergent sequence of over-approximations of the value, relying on an analysis of the game graph. Consequently, VI becomes an anytime algorithm returning the approximation of the value and the current error bound. As another consequence, we can provide a simulation-based asynchronous VI algorithm, which yields the same guarantees, but without necessarily exploring the whole game graph.Comment: CAV201

Predicting a small molecule-kinase interaction map: A machine learning approach

<p>Abstract</p> <p>Background</p> <p>We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.</p> <p>Results</p> <p>A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided.</p> <p>Conclusions</p> <p>In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.</p