Browsing by Author "McLurkin, James"

Now showing 1 - 7 of 7
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    A Branch-and-Cut Method for Solving the Bilevel Clique Interdiction Problem
    (2015-04-20) Becker, Timothy J; Hicks, Illya; Zhang, Yin; McLurkin, James
    I introduce an algorithm to solve the current formulation of the bilevel clique interdiction problem. Interdiction, a military term, describes the removal of enemy resources. The single level clique interdiction problem describes the attempt of an attacker to interdict a maximum number of cliques. The bilevel form of the problem introduces a defender who attempts to minimize the number of cliques interdicted by the attacker. Previous authors have developed algorithms for the single level clique interdiction problem, as well as for bilevel formulations of other problems. However, an algorithm for the bilevel clique interdiction problem has not previously been creatd. The algorithm presented in this thesis uses a branch and cut approach to solve the proposed problem. This algorithm is expected to be usable on any social network, thereby improving the study of many network problems including terrorist cells or marketing strategies.
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    A Constraint and Sampling-Based Approach to Integrated Task and Motion Planning
    (2014-09-30) Prabhu, Sailesh Naveena; Chaudhuri, Swarat; Kavraki, Lydia E; McLurkin, James; Vardi, Moshe V
    This thesis tackles the Integrated Task and Motion Planning (ITMP) Problem. The ITMP problem extends classical task planning with actions that require a motion plan. The agent seeks a sequence of actions and the necessary motions to achieve the goal. The user partially specifies the task plan by providing the actions' known parameters. An SMT solver, then, discovers values for the unkown parameters that satisfies constraints requiring the task plan to achieve the goal. The SMT solver utilizes an annotated Probabilistic Roadmap (PRM) to query for motion planning information. A sampling algorithm generates the PRM's vertices to permit a mobile manipulator to grasp numerous object configurations. Each iteration samples several base configurations and adds a base configuration to the PRM that increases the object configurations grasped from its vertices. Our results indicate that increasing the samples per iteration improves the probability the SMT solver discovers a satisfying assignment without adversely affecting the resulting task plan.
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    Collective Transport of an Unknown Object by Multi Robots with Limited Sensing
    (2015-04-29) Habibi, Golnaz; McLurkin, James; Kavraki, Lydia; O'Malley, Marcia; Schwager, Mac
    This thesis presents a fully distributed approach to retrieve a large object from an unknown environment. The object is assumed to be located in an environment without GPS or Internet infrastructure. The object is too heavy to transport by one robot. The collective transport problem is broken into five major steps: 1) Exploring the unknown environment and finding the object. 2) Grasping the object. 3) Characterizing the object. 4) Planning a path to the desired location. 5) Transporting the object to the desired location. This thesis presents efficient distributed algorithms for robots with limited sensing to accomplish steps three to five. Object characterization includes centroid estimation and object dimension estimation. Two algorithms are developed for centroid estimation. In the first algorithm, each robot uses a communication tree to compute the sum of its children's positions. The second algorithm is based on pipelined consensus, which is an extension of pairwise gossip-based consensus. Two algorithms are presented to estimate object's dimensions. The first one is a distributed principal component analysis algorithm, and the second one is the distributed version of rotating calipers algorithm. A distributed path planning algorithm is presented. Robots have already been scattered across the terrain and collectively sample the obstacles in the environment. Robots use this sampling along with the estimated dimensions of the object, from above, to construct a configuration space of robots and the object. A variant of the distributed Bellman-Ford algorithm is then used to construct a shortest-path tree. A path navigation algorithm is presented to map each path segments to a distributed motion controller that can command the robots to transport the object. Four distributed motion controllers are designed including: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation. Finally, a distributed recovery algorithm is presented to recover the robots efficiently and safely after collective transport. This recovery method uses k-redundant maximum-leaf spanning trees that guarantee connectivity during the recovery. All algorithms are verified through simulation as well as hardware experiments. The results are promising, and the algorithms successfully transport convex or concave objects in simulation and hardware experiments. After robots transport the object, robots are successfully recovered at home location by using the recovery algorithm. All algorithms discussed in this thesis are fully distributed, efficient, and robust to object shape and network population changes.
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    Motion Planning with Uncertain Information in Robotic Tasks
    (2014-03-25) Grady, Devin; Kavraki, Lydia E.; McLurkin, James; Moll, Mark; O'Malley, Marcia K.
    In the real world, robots operate with imperfect sensors providing uncertain and incomplete information. We develop techniques to solve motion planning problems with imperfect information in order to accomplish a variety of robotic tasks including navigation, search-and-rescue, and exposure minimization. This thesis focuses on the challenge of creating robust policies for robots with imperfect actions and sensing. These policies map input observations to output actions. The tools that exist to solve these problems are typically Partially-Observable Markov Decision Processes (POMDPs), and can only handle small problem instances. This thesis proposes several techniques to expand the size of the problem instance that can be considered. Because executing a policy is simple once the offline computation is done, even inexpensive, computationally constrained robots can use these policies and solve the tasks mentioned. First we show that the solution of an abstracted action space can be used to bootstrap a complete solution for navigation. Generalizing this action space abstraction to both action and state spaces expands the set of problems that can be solved. Additionally, the concept of abstraction is applied to the workspace -- we develop a method to compute local solutions to a noisy navigation problem, then stitch them together into a global solution. Our proposed methods are run on large problem instances, and the output policies are compared against policies generated with existing techniques. Though these large tasks are often unsolvable with previous methods, abstraction allows us to find high quality policies. Our findings show that these techniques significantly increase the size of tasks involving planning with uncertain information for which solutions can be found. The techniques presented generally offer significant speed increases and often solution quality improvements as well. Additionally, this thesis includes work on two separate problems. First, we solve a task where several robots cooperate to quickly classify an observed object as one of several possible types using a camera. Then, we proceed to solve a task where a single robot navigates to a destination quickly, but the robot may need to allocate time towards obtaining information about a new object discovered along the way.
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    Myoelectric Control of a Robotic Exoskeleton for Rehabilitation
    (2015-04-21) Artz, Edward J; O'Malley, Marcia K.; Ghorbel, Fathi H; McLurkin, James
    A primary challenge in the design of human-robot interfaces for rehabilitation after neurological injury, such as stroke or spinal cord injury, is the detection of user intent, needed to maximize the efficacy of the therapy. Common approaches to rehabilitation robot interfaces, including the current implementation of the MAHI Exo-II upper extremity therapeutic exoskeleton at Rice University, rely on impedance control schemes. Another approach, surface electromyography (sEMG), is gaining attention. This interface is appealing as the recorded signal is related to the individual's desired torque about the joint the muscle actuates. In this thesis, an sEMG interface and associated control schemes are proposed and investigated for the MAHI Exo-II. A known drawback of sEMG interfaces are lengthy subject- and session-dependent calibration procedures to develop muscle-force mappings. In this thesis, a relaxed calibration procedure and various control schemes are proposed to enable practical integration into therapy protocols. Agonist-antagonist muscle groups were related following normalization based on sub-maximal isometric contraction in the exoskeleton. Pilot experiments were conducted on healthy subjects to assess the usability of the exoskeleton in the proposed control modes of operation given simple sEMG interface. The results of these experiments support the implementation of the proposed sEMG interface. Future experiments will focus on validation in impaired populations.
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    Temporal Logic Motion Planning in Partially Unknown Environments
    (2013-09-16) Maly, Matthew; Kavraki, Lydia E.; Vardi, Moshe Y.; McLurkin, James
    This thesis considers the problem of a robot with complex dynamics navigating a partially discovered environment to satisfy a temporal logic formula consisting of both a co-safety formula component and a safety formula component. We employ a multi-layered synergistic framework for planning motions to satisfy a temporal logic formula, and we combine with it an iterative replanning strategy to locally patch the robot's discretized internal representation of the workspace whenever a new obstacle is discovered. Furthermore, we introduce a notion of ``closeness'' of satisfaction of a linear temporal logic formula, defined by a metric over the states of the corresponding automaton. We employ this measure to maximize partial satisfaction of the co-safety component of the temporal logic formula when obstacles render it unsatisfiable. For the safety component of the specification, we do not allow partial satisfaction. This introduces a general division between ``soft'' and ``hard'' constraints in the temporal logic specification, a concept we illustrate in our discussion of future work. The novel contributions of this thesis include (1) the iterative replanning strategy, (2) the support for safety formulas in the temporal logic specification, (3) the method to locally patch the discretized workspace representation, and (4) support for partial satisfaction of unsatisfiable co-safety formulas. As our experimental results show, these methods allow us to quickly compute motion plans for robots with complex dynamics to satisfy rich temporal logic formulas in partially unknown environments.
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    Using Custom Integrated Force Sensing Mechanisms for Interaction Control in Rehabilitation Robots
    (2014-04-25) Erwin, Andrew; O'Malley, Marcia K.; Ghorbel, Fathi H.; McLurkin, James; Sergi, Fabrizio
    This thesis presents the implementation of interaction controllers on two custom integrated force sensing mechanisms and demonstrates their suitability for applications to the field of rehabilitation robotics. One condition in which interaction control is beneficial occurs when the robot's dynamics are significant and need to be compensated through force-feedback. To address this need, a grip force sensor that measures forces in three planes by using force sensing resistors was developed. The device readily integrates with most rehabilitation robots at the end effector. Additionally, if a robot is non-backdrivable, force measurement is required to render transparent environments during evaluation mode as well as for interaction control. Here, interaction controllers are implemented in a 1DOF MR-compatible actuation module. The MR-compatible device uses a non-backdrivable actuator with series elasticity for force sensing. Experimental implementation of interaction controllers on both devices demonstrates the advantages of closed-loop force control.