Robustness analysis of evolutionary controller tuning using real systems

A genetic algorithm (GA) presents an excellent method for controller parameter tuning. In our work, we evolved the heading as well as the altitude controller for a small lightweight helicopter. We use the real flying robot to evaluate the GA's individuals rather than an artificially consistent simulator. By doing so we avoid the ldquoreality gaprdquo, taking the controller from the simulator to the real world. In this paper we analyze the evolutionary aspects of this technique and discuss the issues that need to be considered for it to perform well and result in robust controllers

Real-time evolution of an embedded controller for an autonomous helicopter

In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it, thus avoiding the ldquoreality gaprdquo. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GApsilas individuals converge towards more robust solutions

smARTflight: An Environmentally-Aware Adaptive Real-Time Flight Management System

Multi-rotor drones require real-time sensor data processing and control to maintain flight stability, which is made more challenging by external disturbances such as wind. In this paper we introduce smARTflight: an environmentally-aware adaptive real-time flight management system. smARTflight adapts the execution frequencies of flight control tasks according to timing and safety-critical constraints, in response to transient fluctuations of a drone’s attitude. In contrast to current state-of-the-art methods, smARTflight’s criticality-aware scheduler reduces the latency to return to a steady-state target attitude. The system also improves the overall control accuracy and lowers the frequency of adjustments to motor speeds to conserve power. A comparative case-study with a well-known autopilot shows that smARTflight reduces unnecessary control loop executions under stable conditions, while reducing response time latency by as much as 60% in a given axis of rotation when subjected to a 15° step attitude disturbance.https://www.cs.bu.edu/fac/richwest/papers/smARTflight-ecrts20.pdfhttps://drops.dagstuhl.de/opus/volltexte/2020/12387/pdf/LIPIcs-ECRTS-2020-24.pdfPublished versio

Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration

This paper introduces a novel approach to detour management in Urban Air Traffic Management (UATM) using knowledge representation and reasoning. It aims to understand the complexities and requirements of UAM detours, enabling a method that quickly identifies safe and efficient routes in a carefully sampled environment. This method implemented in Answer Set Programming uses non-monotonic reasoning and a two-phase conversation between a human manager and the UATM system, considering factors like safety and potential impacts. The robustness and efficacy of the proposed method were validated through several queries from two simulation scenarios, contributing to the symbiosis of human knowledge and advanced AI techniques. The paper provides an introduction, citing relevant studies, problem formulation, solution, discussions, and concluding comments.Comment: 18 pages, 2 figures, accepted to the Advances in Artificial Intelligence and Machine Learning (AAIML) journa

Field Programmable Gate Array (FPGA) for Bio-Inspired Visuo-Motor Control Systems Applied to Micro-Air Vehicles

International audienc