Embedded harmonic control for dynamic trajectory planning on FPGA

International audienceThis paper presents a parallel hardware implementation of a well-known navigation control method on reconfigurable digital circuits. Trajectories are estimated after an iterated computation of the harmonic functions, given the goal and obstacle positions of the navigation problem. The proposed massively distributed implementation locally computes the direction to choose to get to the goal position at any point of the environment. Changes in this environment may be immediately taken into account, for example when obstacles are discovered during an on-line exploration. The implementation results show that the proposed architecture simultaneously improves speed, power consumption, precision, and environment size

Embedded harmonic control for dynamic trajectory planning on

This paper presents a parallel hardware implementation of a well-known navigation control method on reconfigurable digital circuits. Trajectories are estimated after an iterated computation of the harmonic functions, given the goal and obstacle positions of the navigation problem. The proposed massively distributed implementation locally computes the direction to choose to get to the goal position at any point of the environment. Changes in this environment may be immediately taken into account, for example when obstacles are discovered during an on-line exploration. The implementation results show that the proposed architecture simultaneously improves speed, power consumption, precision, and environment size.

Embedded harmonic control for trajectory planning in large environments

International audienceThis paper presents an embedded FPGA­based architecture to compute navigation trajectories along a harmonic potential. The goals and obstacles may be changed during computation. Large environments are split into blocks. This approach, together with the use of an increasing precision, enables an optimization of the overall computation time that is theoretically and experimentally studied. Implementation results confirm outstanding speedup factors

Block-synchronous Harmonic Control for Scalable Trajectory Planning

ISBN : 978-953-7619-20-6Trajectory planning consists in finding a way to get from a starting position to a goal position while avoiding obstacles within a given environment or navigation space. Harmonic functions may be used as potential fields for trajectory planning. Such functions do not have local extrema, so that control algorithms may reduce to locally descend the potential field until reaching a minimum, when obstacles correspond to maxima of the potential and goals correspond to minima. This chapter presents a parallel hardware implementation of this navigation method on reconfigurable digital circuits. Trajectories are estimated after the iterated computation of the harmonic function, given the goal and obstacle positions of the navigation problem. The proposed massively distributed implementation locally computes the direction to choose to get to the goal position at any point of the environment. Changes in this environment may be immediately taken into account, for example when obstacles are discovered during an on-line exploration. To fit real-world applications, our implementation has been designed to deal with very large navigation environments while optimizing computation time

A Biomimetic, Energy-Harvesting, Obstacle-Avoiding, Path-Planning Algorithm for UAVs

This dissertation presents two new approaches to energy harvesting for Unmanned Aerial Vehicles (UAV). One method is based on the Potential Flow Method (PFM); the other method seeds a wind-field map based on updraft peak analysis and then applies a variant of the Bellman-Ford algorithm to find the minimum-cost path. Both methods are enhanced by taking into account the performance characteristics of the aircraft using advanced performance theory. The combined approach yields five possible trajectories from which the one with the minimum energy cost is selected. The dissertation concludes by using the developed theory and modeling tools to simulate the flight paths of two small Unmanned Aerial Vehicles (sUAV) in the 500 kg and 250 kg class. The results show that, in mountainous regions, substantial energy can be recovered, depending on topography and wind characteristics. For the examples presented, as much as 50% of the energy was recovered for a complex, multi-heading, multi-altitude, 170 km mission in an average wind speed of 9 m/s. The algorithms constitute a Generic Intelligent Control Algorithm (GICA) for autonomous unmanned aerial vehicles that enables an extraction of atmospheric energy while completing a mission trajectory. At the same time, the algorithm automatically adjusts the flight path in order to avoid obstacles, in a fashion not unlike what one would expect from living organisms, such as birds and insects. This multi-disciplinary approach renders the approach biomimetic, i.e. it constitutes a synthetic system that “mimics the formation and function of biological mechanisms and processes.