Fahad Islam

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Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT*), claims to achieve convergence towards the optimal solution but has been proven to take an(More)
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic(More)
Recently proposed Rapidly Exploring Random Tree Star (RRT*) algorithm which is an extension of Rapidly Exploring Random Tree (RRT) provides collision free asymptotically optimal path regardless of obstacle's geometry in a given environment. However, the drawback of this technique is a slow processing rate. This paper presents our proposed Potential Guided(More)
Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the configuration space has several `narrow passages'.(More)
The task of whole-body motion planning for humanoid robots is challenging due to its high-DOF nature, stability constraints, and the need for obstacle avoidance and movements that are efficient. Over the years, various approaches have been adopted to solve this problem such as bounding-box models and jacobian-based techniques. More commonly though,(More)
The benefits of bidirectional planning over the unidirectional version are well established for motion planning in high-dimensional configuration spaces. While bidirectional approaches have been employed with great success in the context of sampling-based planners such as in RRT-Connect, they have not enjoyed popularity amongst search-based methods such as(More)
Rapidly exploring Random Trees (RRT), a sampling based algorithm, efficiently computes a path between a start and a goal configuration. RRT-Connect, is a variant of RRT that works by incrementally building two RRTs rooted at the start and the goal configurations. Significant amount of research has been done on the motion planning of six-legged robots. We(More)