A Motion Planning Processor on Reconfigurable Hardware

Motion planning algorithms enable us to find feasible paths for moving objects. These algorithms utilize feasibility checks to differentiate valid paths from invalid ones. Unfortunately, the computationally expensive nature of such checks reduces the effectiveness of motion planning algorithms. However, by using hardware acceleration to speed up the feasibility checks, we can greatly enhance the performance of the motion planning algorithms. Of course, such acceleration is not limited to feasibility checks; other components of motion planning algorithms can also be accelerated using specially designed hardware. A Field Programmable Gate Array (FPGA) is a great platform to support such an acceleration. An FPGA is a collection of digital gates which can be reprogrammed at run time, i.e., it can be used as a CPU that reconfigures itself for a given task. In this paper, we study the feasibility of an FPGA based motion planning processor and evaluate its performance. In order to leverage its highly parallel nature and its modular structure, our processor utilizes the probabilistic roadmap method at its core. The modularity enables us to replace the feasibility criteria with other ones. The reconfigurability lets us run our processor in different roles, such as a motion planning co-processor, an autonomous motion planning processor or dedicated collision detection chip. Our experiments show that such a processor is not only feasible but also can greatly increase the performance of current algorithms

Higher-order CFD and Interface Tracking Methods on Highly-Parallel MPI and GPU systems

A computational investigation of the effects on parallel performance of higher-order accurate schemes was carried out on two different computational systems: a traditional CPU based MPI cluster and a system of four Graphics Processing Units (GPUs) controlled by a single quad-core CPU. The investigation was based on the solution of the level set equations for interface tracking using a High-Order Upstream Central (HOUC) scheme. Different variants of the HOUC scheme were employed together with a 3rd-order TVD Runge-Kutta time integration. An increase in performance of two orders of magnitude was seen when comparing a single CPU core to a single GPU with a greater increase at higher orders of accuracy and at lower precision

GPGPU: Hardware/Software Co-Design for the Masses

With the recent development of high-performance graphical processing units (GPUs), capable of performing general-purpose computation (GPGPU: general-purpose computation on the GPU), a new platform is emerging. It consists of a central processing unit (CPU), which is very fast in sequential execution, and a GPU, which exhibits high degree of parallelism and thus very high performance on certain types of computations. Optimally leveraging the advantages of this platform is challenging in practice. We spotlight the analogy between GPGPU and hardware/software co-design (HSCD), a more mature design paradigm, to derive a design process for GPGPU. This process, with appropriate tool support and automation, will ease GPGPU design significantly. Identifying the challenges associated with establishing this process can serve as a roadmap for the future development of the GPGPU field

Exploratory Aspects of Sensor Based Planning

In sensor based planning exploration is unavoidable. To understand this aspect of sensor based planning, the authors consider the problem of motion planning for a point with tactile sensors . In dimensions greater than two, this problem has been shown to be unsolvable given a certain mathematical framework. But, if the formulation of the problem is changed by taking the C-space to be discrete, then path planning with tactile sensors is possible. In this setting we give a resolution complete algorithm for planning the motion of a point in any dimension. Measuring the complexity of the problem by the number of discrete moves that the robot makes, we give an upper bound for the complexity of our algorithm that is linear in the surface area of the boundary of the C-space obstacles

Distributed Algorithms for Guiding Navigation across a Sensor Network

We develop distributed algorithms for self-reconfiguring sensor networks that respond to directing a target through a region. The sensor network models the danger levels sensed across its area and has the ability to adapt to changes. It represents the dangerous areas as obstacles. A protocol that combines the artificial potential field of the sensors with the goal location for the moving object guides the object incrementally across the network to the goal, while maintaining the safest distance to the danger areas. We report on hardware experiments using a physical sensor network consisting of Mote sensors

Discrete Search Leading Continuous Exploration for Kinodynamic Motion Planning

This paper presents the Discrete Search Leading continuous eXploration (DSLX) planner, a multi-resolution approach to motion planning that is suitable for challenging problems involving robots with kinodynamic constraints. Initially the method decomposes the workspace to build a graph that encodes the physical adjacency of the decomposed regions. This graph is searched to obtain leads, that is, sequences of regions that can be explored with sampling-based tree methods to generate solution trajectories. Instead of treating the discrete search of the adjacency graph and the exploration of the continuous state space as separate components, DSLX passes information from one to the other in innovative ways. Each lead suggests what regions to explore and the exploration feeds back information to the discrete search to improve the quality of future leads. Information is encoded in edge weights, which indicate the importance of including the regions associated with an edge in the next exploration step. Computation of weights, leads, and the actual exploration make the core loop of the algorithm. Extensive experimentation shows that DSLX is very versatile. The discrete search can drastically change the lead to reflect new information allowing DSLX to find solutions even when sampling-based tree planners get stuck. Experimental results on a variety of challenging kinodynamic motion planning problems show computational speedups of two orders of magnitude over other widely used motion planning methods

Evasión de obstáculos en tiempo real para robots móviles mediante redes neuronales

This paper reviews the theory of robot motion planning and elaborates on an application in which neural networks are used to make decisions regarding to the orientation of a robot in its search for a destination target with obstacle avoidance. The results of these concepts are implemented in an application developed with C++. This work can be used to develop games which mobile objects, that avoid obstacles, are involved or to implement obstacle detection systems for robots.En el presente artículo se realiza una revisión acerca de la planeación de trayectorias en robots móviles y específicamente la aplicación de una red neuronal que toma las decisiones con respecto a la trayectoria de un robot móvil en la evasión de obstáculos hasta llegar a un objetivo. El resultado de estos conceptos se muestra en una aplicación desarrollada en C++. Este trabajo puede ser tomado como base para realizar juegos en los que intervienen objetos móviles que evitan colisiones o para implementar sistemas de detección de obstáculos para robots