Fahad Islam

RRT*-SMART: A Rapid Convergence Implementation of RRT*

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 completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been prposed to overcome the limitaions of RRT*. The goal of the proposecd method is to accelerate the rate of convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*–Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*-Smart algorithm.

Potential guided directional-RRT* for accelerated motion planning in cluttered environments

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 obstacles geometry in a given environment. However, the drawback of this technique is a slow processing rate. This paper presents our proposed Potential Guided Directional-RRT* which addresses this problem and provides accelerated processing rate by incorporating Artificial Potential Fields Algorithm into RRT*. Artificial Potential Field algorithm directs the random samples toward the goal which leads to an increase in the speed of RRT*. We have presented simulation results of our technique and their comparison with results of RRT* under different environmental conditions to demonstrate apace execution rate of our novel idea.

A*-Connect: Bounded suboptimal bidirectional heuristic search

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 A*. The systematic nature of search-based algorithms, which often leads to consistent and high-quality paths, also enforces strict conditions for the connection of forward and backward searches. Admissible heuristics for the connection of forward and backward searches have been developed, but their computational complexity is a deterrent. In this work, we leverage recent advances in search with inadmissible heuristics to develop an algorithm called A*-Connect, much in the spirit of RRT-Connect. A*-Connect uses a fast approximation of the classic front-to-front heuristic from literature to lead the forward and backward searches towards each other, while retaining theoretical guarantees on completeness and bounded suboptimality. We validate A*-Connect on manipulation as well as navigation domains, comparing with popular sampling-based methods as well as state-of-the-art bidirectional search algorithms. Our results indicate that A*-Connect can provide several times speedup over unidirectional search while maintaining high solution quality.

Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration

Whole-body motion planning for humanoid robots with heuristic search

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Designing of motions for humanoid goal keeper robots

, which is used to bias the planner to go through

Dynamic Multi-Heuristic A*

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A Single-Planner Approach to Multi-Modal Humanoid Mobility

. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics.

Safe-radius based motion planning of hexapod using RRT-connect

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, sampling-based algorithms are employed for this task since they perform admirably well in high-dimensional spaces. As an alternative, search-based planners offer improvements in terms of optimality and consistency of the solution. However, they are normally considered impractical for high-dimensional motion planning. In this paper, we present a heuristic search-based motion planning framework for humanoid robots that circumvents the drawbacks traditionally associated with search-based planners while catering to the specific requirements of humanoid motion planning. This is achieved primarily through a combination of informative yet computationally inexpensive heuristics, carefully crafted motion primitives as atomic actions, and a whole body inverse kinematics solver for achieving desired end effector orientations. The experimental results show the ability of our framework to perform complex motion planning tasks quickly and efficiently.

Adaptive Rapidly-Exploring-Random-Tree-Star (RRT*) -Smart: Algorithm Characteristics and Behavior Analysis in Complex Environments

A methodology is presented to develop motions for the purposes of a humanoid goal keeper robot during a match of soccer. These motions meet performance objectives as well as minimize damage to the humanoid robot that occurs during the execution of the motion. The methodology presented employs the use of a realistic simulator paired with controlled human influence. The resulting motions better meet performance criteria while resulting in relatively less damage to the humanoid robot.