A path conditioning method with trap avoidance

This work presents a sliding-mode method for robotic path conditioning. The proposal includes a trap avoidance algorithm in order to escape from trap situations, which are analogous to local minima in potential field-based approaches. The sliding-mode algorithm activates when the desired path is about to violate the robot workspace constraints, modifying it as much as necessary in order to fulfill all the constraints and reaching their limit surface at low speed. The proposed path conditioning algorithm can be used on-line, since it does not require a priori knowledge of the desired path, and improves the conventional conservative potential field-based approach in the sense that it fully exploits the robot workspace. The proposed approach can be easily added as an auxiliary supervisory loop to conventional robotic planning algorithms and its implementation is very easy in a few program lines of a microprocessor. The proposed path conditioning is compared through simulation with the conventional potential field-based approach in order to show the benefits of the method. Moreover, the effectiveness of the proposed trap avoidance algorithm is evaluated by simulation for various trap situations.This research is partially supported by DISICOM project PROM-ETEO 2008/088 of Generalitat Valenciana (Spain), research project DPI2011-27845-C02-01 of the Spanish Government (Spain), research project PAID-05-11-2640 of the Technical University of Valencia (Spain), and the Argentinian Government (UNLP 11I127, CONICET PIP 112-200801-0, ANPCyT PICT 2007 00535).Gracia Calandin, LI.; Sala, A.; Garelli, F. (2012). A path conditioning method with trap avoidance. Robotics and Autonomous Systems. 60(6):862-873. https://doi.org/10.1016/j.robot.2012.01.009S86287360

Reactive Sliding-Mode Algorithm for Collision Avoidance in Robotic Systems

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).This brief presents a reactive reference conditioning algorithm for robot collision avoidance based on geometric invariance and sliding-mode (SM) ideas. First, constraints are defined in terms of the measurements given by the robot s sensors in order to guarantee that collisions will not occur. Then, a supervisory loop ensures the fulfillment of the constraints modifying the reference trajectory as much as necessary by means of a discontinuous control law. The proposed algorithm activates only when the constraints are about to be violated and, thus, in contrast to conventional SM approaches, there exists no reaching mode to the limit surface of the constraints (sliding surface). The validity and effectiveness of the proposed approach is substantiated by simulation and experimental results using a mobile robot equipped with infrared sensors.This work was supported in part by research under Project DPI2011-27845-C02-01 (Spanish Government), Projects PAID-05-11-2640, and PAID-00-12-SP20120159, (Universitat Politecnica de Valencia, Spain), and Projects ANPCyT PICT-2011-0888, CONICET PIP 112-2011-00361, and UNLP I164 in Argentina. Recommended by Associate Editor M. Zefran.Gracia Calandin, LI.; Garelli, F.; Sala, A. (2013). Reactive Sliding-Mode Algorithm for Collision Avoidance in Robotic Systems. IEEE Transactions on Control Systems Technology. 21(6):2391-2399. https://doi.org/10.1109/TCST.2012.2231866S2391239921

Integrated sliding-mode algorithms in robot tracking applications

An integrated solution based on sliding mode ideas is proposed for robotic trajectory tracking. The proposal includes three sliding-mode algorithms for speed auto-regulation, path conditioning and redundancy resolution in order to fulfill velocity, workspace and C-space constraints, respectively. The proposed method only requires a few program lines and simplifies the robot user interface since it directly deals with the fulfillment of the constraints to find a feasible solution for the robot trajectory tracking in a short computation time. The proposed approach is evaluated in simulation on the freely accessible 6R robot model PUMA-560, for which the main features of the method are illustrated.This research is partially supported by research project DPI2011-27845-C02-01 of the Spanish Government (Spain), research projects PAID-05-11-2640 and PAID-00-12-SP20120159 of the Universitat Polit'ecnica de Val'encia (Spain), and research projects ANPCyT PICT-2011-0888, CONICET PIP 112-2011-00361, and UNLP 1164 (Argentina).Gracia Calandin, LI.; Garelli, F.; Sala Piqueras, A. (2013). Integrated sliding-mode algorithms in robot tracking applications. Robotics and Computer-Integrated Manufacturing. 29(1):53-62. https://doi.org/10.1016/j.rcim.2012.07.007S536229

Solving the potential field local minimum problem using internal agent states

We propose a new, extended artificial potential field method, which uses dynamic internal agent states. The internal states are modelled as a dynamical system of coupled first order differential equations that manipulate the potential field in which the agent is situated. The internal state dynamics are forced by the interaction of the agent with the external environment. Local equilibria in the potential field are then manipulated by the internal states and transformed from stable equilibria to unstable equilibria, allowiong escape from local minima in the potential field. This new methodology successfully solves reactive path planning problems, such as a complex maze with multiple local minima, which cannot be solved using conventional static potential fields

Biological learning and artificial intelligence

It was once taken for granted that learning in animals and man could be explained with a simple set of general learning rules, but over the last hundred years, a substantial amount of evidence has been accumulated that points in a quite different direction. In animal learning theory, the laws of learning are no longer considered general. Instead, it has been necessary to explain behaviour in terms of a large set of interacting learning mechanisms and innate behaviours. Artificial intelligence is now on the edge of making the transition from general theories to a view of intelligence that is based on anamalgamate of interacting systems. In the light of the evidence from animal learning theory, such a transition is to be highly desired

Survival asymptotics for branching random walks in IID environments

We first study a model, introduced recently in \cite{ES}, of a critical branching random walk in an IID random environment on the $d$-dimensional integer lattice. The walker performs critical (0-2) branching at a lattice point if and only if there is no `obstacle' placed there. The obstacles appear at each site with probability $p\in [0,1)$ independently of each other. We also consider a similar model, where the offspring distribution is subcritical. Let $S_n$ be the event of survival up to time $n$. We show that on a set of full $\mathbb P_p$-measure, as $n\to\infty$, (i) Critical case: P^{\omega}(S_n)\sim\frac{2}{qn}; (ii) Subcritical case: P^{\omega}(S_n)= \exp\left[\left( -C_{d,q}\cdot \frac{n}{(\log n)^{2/d}} \right)(1+o(1))\right], where $C_{d,q}>0$ does not depend on the branching law. Hence, the model exhibits `self-averaging' in the critical case but not in the subcritical one. I.e., in (i) the asymptotic tail behavior is the same as in a "toy model" where space is removed, while in (ii) the spatial survival probability is larger than in the corresponding toy model, suggesting spatial strategies. We utilize a spine decomposition of the branching process as well as some known results on random walks.Comment: 2 figure

Robot coordination using task-priority and sliding-mode techniques

In this work, an approach based on task-priority redundancy resolution and sliding mode ideas is proposed for robot coordination. In particular, equality and inequality constraints representing the coordination of the multi-robot system are considered as mandatory (for instance, rigid-body manipulation constraints to distance between the end-effectors of several robot arms, or other inequality constraints guaranteeing safe operation of a robotic swarm or confining the robot's workspace to avoid collision and joint limits). Besides the mandatory constraints, other constraints with lower priority are considered for the tracking of the workspace reference and to achieve secondary goals. Thus, lower-priority constraints are satisfied only in the null space of the higherpriority ones. The fulfillment of the constraints is achieved using geometric invariance and sliding mode control theory. The validity and effectiveness of the proposed approach is substantiated by 2D and 3D simulation results using two 3R planar robots and two 6R PUMA-762 robots, respectively.This work was supported in part by research under Project DPI2011-27845-C02-01 of the Spanish Government and Projects ANPCyT PICT-2011-0888, CONICET PIP 112-2011-00361 and UNLP 1164 in Argentina.Gracia Calandin, LI.; Sala Piqueras, A.; Garelli, F. (2014). Robot coordination using task-priority and sliding-mode techniques. Robotics and Computer-Integrated Manufacturing. 30(1):74-89. https://doi.org/10.1016/j.rcim.2013.08.003S748930

Social learning and its effect on conservation efforts in the Barrens Topminnow: an evaluation of conditioning and social learning as a viable longterm solution to evolutionary traps

The invasive Western Mosquitofish (WMF), Gambusia affinis, has facilitated the extinction and endangerment of multiple freshwater fish species, including the Barrens Topminnow (BTM), Fundulus julisia. In my study, I investigated if BTMs are capable of socially learning conditioned predator recognition, and I hypothesized that BTMs are capable of social learning and that such learning might improve survival of BTMs. To explore the role of conditioning and subsequent learning in the conservation of BTMs, I conducted a series of experiments in which I 1) attempted to condition BTMs to exhibit antipredator behavior when faced with WMF, and 2) created a situation in which naive BTMs could potentially learn from conditioned individuals. I found no evidence of conditioning or of social learning, and there were no significant differences in behavior, body condition, or survival among my treatment groups