Forward speed control of a pulsed-jet soft-bodied underwater vehicle

This paper reports on the development of the control for a new class of soft underwater vehicles. These vehicles exploit their soft-bodied nature to produce thrust by cyclically ingesting and expelling ambient fluid. A forward speed control based on the linearised dynamics of the robot is design. The control succeeds at dealing with the discontinuous thrust by accounting for the shape-change driven actuation

Adaptive longitudinal control of an autonomous vehicle with an approximate knowledge of its parameters

This paper explores the longitudinal control problem of an autonomous car in legal speed range. The goal is to develop a longitudinal controller that does not rely on vehicle identification parameters, while being capable of tracking the speed profile with comfort acceleration. A modification of a Model Reference Adaptive Control (MRAC) technique found in literature has been deeply studied and implemented, by setting the proper initial conditions for the target application. The proposed architecture is capable of controlling a vehicle whose parameters are known approximately. A CarSim-Simulink joint simulation verifies the feasibility of the proposed strategy and evaluates performances of vehicles at low and high dynamics conditions

A Locomotion Strategy for an Octopus-Bioinspired Robot

In this paper a locomotion strategy for a six-limb robot inspired by the octopus is shown. A tight relationship between the muscular system and the nervous systems exists in the octopus. At a high level of abstraction, the same relationship between the mechanical structure and the control of the robot is presented here. The control board sends up to six signals to the limbs, which mechanically perform a stereotypical rhythmical movement. The results show how by coordinating only two limbs an effective locomotion is achieved

Dynamic Model of a Multibending Soft Robot Arm Driven by Cables

The new and promising field of soft robotics has many open areas of research such as the development of an exhaustive theoretical and methodological approach to dynamic modeling. To help contribute to this area of research, this paper develops a dynamic model of a continuum soft robot arm driven by cables and based upon a rigorous geometrically exact approach. The model fully investigates both dynamic interaction with a dense medium and the coupled tendon condition. The model was experimentally validated with satisfactory results, using a soft robot arm working prototype inspired by the octopus arm and capable of multibending. Experimental validation was performed for the octopus most characteristic movements: bending, reaching, and fetching. The present model can be used in the design phase as a dynamic simulation platform and to design the control strategy of a continuum robot arm moving in a dense medium

A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

Research on autonomous cars, early intensified in the 1990s, is becoming one of the main research paths in automotive industry. Recent works use Rapidly-exploring Random Trees to explore the state space along a given reference path, and to compute the minimum time collision-free path in real time. Those methods do not require good approximations of the reference path, they are able to cope with discontinuous routes, they are capable of navigating in realistic traffic scenarios, and they derive their power from an extensive computational effort directed to improve the quality of the trajectory from step to step. In this paper, we focus on re-engineering an existing state-of-the-art sequential algorithm to obtain a CUDA-based GPGPU (General Purpose Graphics Processing Units) implementation. To do that, we show how to partition the original algorithm among several working threads running on the GPU, how to propagate information among threads, and how to synchronize those threads. We also give detailed evidence on how to organize memory transfers between the CPU and the GPU (and among different CUDA kernels) such that planning times are optimized and the available memory is not exceeded while storing massive amounts of fuse data. To sum up, in our application the GPU is used for all main operations, the entire application is developed in the CUDA language, and specific attention is paid to concurrency, synchronization, and data communication. We run experiments on several real scenarios, comparing the GPU implementation with the CPU one in terms of the quality of the generated paths and in terms of computation (wall-clock) times. The results of our experiments show that embedded GPUs can be used as an enabler for real-time applications of computationally expensive planning approaches

Neural Network and Jacobian Method for Solving the Inverse Statics of a Cable-Driven Soft Arm With Nonconstant Curvature

The solution of the inverse kinematics problem of soft manipulators is essential to generate paths in the task space. The inverse kinematics problem of constant curvature or piecewise constant curvature manipulators has already been solved by using different methods, which include closed-form analytical approaches and iterative methods based on the Jacobian method. On the other hand, the inverse kinematics problem of nonconstant curvature manipulators remains unsolved. This study represents one of the first attempts in this direction. It presents both a model-based method and a supervised learning method to solve the inverse statics of nonconstant curvature soft manipulators. In particular, a Jacobian-based method and a feedforward neural network are chosen and tested experimentally. A comparative analysis has been conducted in terms of accuracy and computational time