Yasar Ayaz

Human-Like Approach to Footstep Planning Among Obstacles for Humanoid Robots

Unlike wheeled robots, humanoid robots are able to overcome obstacles in the environment by stepping over or upon them. Conventional 2D methods for robot navigation fail to exploit this unique ability of humanoids and thus design trajectories only by circumventing obstacles. Recently, globalized algorithms have been presented that take into account this feature of humanoids. However, due to high computational complexity, most of them are very time consuming. In this paper we present a new approach to footstep planning in obstacle cluttered environments that employs a human-like strategy to terrain traversal. Simulation results of its implementation on a model of Saika-3 humanoid robot are also presented. The algorithm, being one of reactive nature, refutes previous claims that reactive algorithms fail to find successful paths in complex obstacle cluttered environments

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.

HUMAN-LIKE APPROACH TO FOOTSTEP PLANNING AMONG OBSTACLES FOR HUMANOID ROBOTS

Unlike wheeled robots, humanoid robots are able to cross obstacles by stepping over or upon them. Conventional 2D methods for robot navigation fail to exploit this unique ability of humanoids and thus design trajectories only by circumventing obstacles. Recently, global algorithms have been presented that take into account this feature of humanoids. However, due to high computational complexity, most of them are very time consuming. In this paper, we present a new approach to footstep planning in obstacle cluttered environments that employs a human-like strategy to terrain traversal. Simulation results of its implementation on a model of the Saika-3 humanoid robot are also presented. The algorithm, being one of reactive nature, refutes previous claims that reactive algorithms fail to find successful paths in complex obstacle cluttered environments.

Turbine Blade Manufacturing Through Rapid Tooling (RT) Process and Its Quality Inspection

Rapid prototyping (RP) technologies have played vital role in product development and validation. Another aspect of RP is rapid tooling (RT). The development and manufacturing of conventional tools (die and molds) take considerable amount of time. RP technologies could be used to shorten the development time of these tools for shorten the time to production. This investigation focuses on the development of turbine blade through RT technique with quality inspection at three different stages, i.e., after manufacturing of master patterns, wax patterns, and casting in metal. Three different materials were considered for RT techniques, i.e., Room temperature vulcanization (RTV) silicon, polyurethane, and plaster of Paris. Master patterns were developed using stereolithography(SLA) and fused deposition modeling (FDM) process. Both master patterns were analyzed for surface roughness and dimensional accuracy. SLA pattern showed better results for surface finish and dimensional accuracy, and it was used for mold manufacturing. Wax patterns were produced from RTV silicon, polyurethane (PU), and plaster of Paris molds,and used for metal casting. Dimensional quality inspection was performed for both wax and metallic parts using noncontact three-dimensional (3D)digitizer. RTV silicon and SLA process were selected as the suitable mold material and process respectively for RT of turbine blade.

Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments

Abstract The sampling-based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT called RRT* has been proposed that ensures asymptotic optimality. Subsequently its bidirectional version has also been introduced in the literature known as Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT* and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed for complex cluttered environments. IB-RRT* utilizes the bidirectional trees approach and introduces intelligent sample insertion heuristic for fast convergence to the optimal path solution using uniform sampling heuristics. The proposed algorithm is evaluated theoretically and experimental results are presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental results demonstrate the superior efficiency of IB-RRT* in comparison with RRT* and B-RRT in complex cluttered environments.

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.

Footstep planning for humanoid robots among obstacles of various types

The unique ability of humanoid robots to step over or upon obstacles is left unexploited if ordinary mobile robot navigation strategies are used for humanoids as well. Recently presented path planning strategies that make use of this capability, however, are very time consuming due to high computational complexity. We have presented a novel approach to humanoid robot footstep planning in obstacle cluttered environments that employs a human-like approach to terrain traversal. This paper mainly centers on providing a proof of concept for the algorithm while also bringing about some improvements. Unlike previous statically stable simulation work, here we present footstep plans including dynamic walk together with practical results of HRP-2 humanoid robot navigating in various scenarios with different obstacle characteristics. These also include catering for body sway dynamics as well as planning and executing stepping over obstacles encountered at angles not perpendicular to the robots line of motion.

Potential functions based sampling heuristic for optimal path planning

Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.

Planning footsteps in obstacle cluttered environments

Humanoid robots posses the unique ability to cross obstacles by stepping over or upon them. However, conventional 2D methods for robot navigation fail to exploit it and thus design trajectories only by circumventing obstacles. Recently, global algorithms have been presented that take into account this feature of humanoids. However, due to high computational complexity, most of them are very time consuming. In this paper, we illustrate our novel approach to footstep planning in obstacle cluttered environments that employs a human-like strategy to terrain traversal and report on progress towards its implementation on humanoid robot hardware. Unlike existing hardware executions of footstep planning approaches, we consider obstacles of non-zero height while planning. Specific strategies are adopted to cater for these, both in dodging obstacles by circumvention as well as in design of stepping over trajectories. Experimental results of its implementaion on HRP-2 humanoid robot are also presented.

Humanoid robot motion generation for nailing task

In order to exert a large force on the environment, it is effective to apply impulsive force. We describe the motions that perform tasks by applying impulsive force as ldquoimpact motionrdquo. In this research, a nailing task is taken as an example of impact motion. This paper proposes a way to generate impact motions for humanoid robots to exert a large force and the feedback control method for driving a nail robustly. In order to validate the proposed scheme, experiments are carried out using life-sized humanoid robot HRP-2. The motion for nailing task generated by the proposed method is compared with the motion designed heuristically by a human. The driving depth is clearly increased by the proposed method.