Marco Morales

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In this dissertation we study how motion induced through the interaction of inertia, gravity, and elastic oscillations can be utilized to improve the performance of legged robots. Different ‘gaits’ for which such natural dynamics help to optimize efficiency and locomotion speed are synthesized and analyzed for two types of conceptual models: for(More)
There are many sampling-based motion planning methods that model the connectivity of a robot's configuration space (C-space) with a graph whose nodes are valid configurations and whose edges represent valid transitions between nodes. One of the biggest challenges faced by users of these methods is selecting the right planner for their problem. While(More)
There are many randomized motion planning techniques, but it is often difficult to determine what planning method to apply to best solve a problem. Planners have their own strengths and weaknesses, and each one is best suited to a specific type of problem. In previous work, we proposed a meta-planner that, through analysis of the problem features,(More)
Although there are many motion planning techniques, there is no single one that performs optimally in every environment for every movable object. Rather, each technique has different strengths and weaknesses which makes it best-suited for particular types of situations. Also, since a given environment can consist of vastly different regions, there may not(More)
Probabilistic roadmap methods (prms) have been highly successful in solving many high degree of freedom motion planning problems arising in diverse application domains such as traditional robotics, computer-aided design, and computational biology and chemistry. One important practical issue with prms is that they do not provide an automated mechanism to(More)
The brain has extraordinary computational power to represent and interpret complex natural environments is essentially determined by the topology and geometry of the brain’s architectures. We present a framework to construct cortical networks which borrows from probabilistic roadmap methods developed for robotic motion planning. We abstract the network as a(More)
In this paper we introduce the Partition Task problem class along with a complexity measure to evaluate its instances and a performance measure to quantify the ability of a system to solve them. We explore, via simulations, some potential applications of these concepts and present some results as examples that highlight their usefulness in policy design(More)
With the success of randomized sampling-based motion planners such as probabilistic roadmap methods, much work has been done to design new sampling techniques and distributions. To date, there is no sampling technique that outperforms all other techniques for all motion planning problems. Instead, each proposed technique has different strengths and(More)
Sampling based motion planning methods have been highly successful in solving many high degree of freedom motion planning problems arising in diverse application domains such as traditional robotics, computer-aided design, and computational biology and chemistry. Recent work in metrics for sampling based planners provide tools to analyze the model building(More)