Edge-Weighted Consensus Based Formation Control Strategy With Collision Avoidance

In this paper, a consensus-based control strategy is presented to gather formation for a group of differential-wheeled robots. The for- mation shape and the avoidance of collisions between robots are ob- tained by exploiting the properties of weighted graphs. Since mobile robots are supposed to move in unknown environments, the presented approach to multi-robot coordination has been extended in order to include obstacle avoidance. The effectiveness of the proposed control strategy has been demonstrated by means of analytical proofs. More- over, results of simulations and experiments on real robots are provided for validation purposes

Automated image-based tracking and its application in ecology

The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers

Stochastic recruitment strategies for controlling artificial muscles

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-176).This thesis presents a new architecture for controlling active material actuators inspired by biological motor recruitment. An active material is broken down into many small fibers and grouped together to form one large actuator. Each of these fibers is held in a binary state, either relaxed or contracted, using a small local controller which responds to a broadcast input signal from a central controller. The output force and displacement of the actuator is a function of the number of contracted fibers at any point in time. This architecture enables the creation of large-scale, controllable actuators from highly non-linear active materials. The key innovation enabling the central controller to coordinate the behavior of very many small identical units is to randomize the behavior of each unit. This thesis explains how a collection of active material motor units responding in a random, uncorrelated fashion to broadcast commands will exhibit a predictable response that can be stabilized with feedback control and observed using a Kalman filter. Various control strategies will be presented and discussed, including open-loop plant behavior, linear feedback, optimal control, and model-based look-ahead control. Performance metrics such as accuracy and convergence time will be analyzed using dynamic programming and other control techniques. Parallels will also be discussed between this control problem and similar control problems in the field of swarm robotics.(cont.) The stochastic, recruitment-like actuator architecture is demonstrated in shape memory alloy actuators, each composed of 60 individual elements, having a displacement of over 20 mm and a peak force of over 100 N. Control of displacement, isometric force and stiffness are demonstrated using the observer-controller framework. Two actuators are used in an antagonistic fashion to control the stiffness and position of a 1-DOF arm joint.by Lael Ulam Odhner.Sc.D

Biologically Inspired Connected Advanced Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) have become commonplace in the automotive industry over the last few decades. Even with the advent of ADAS, however, there are still a significant number of accidents and fatalities. ADAS has in some instances been shown to significantly reduce the number and severity of accidents. Manufacturers are working to avoid ADAS plateauing for effectiveness, which has led the industry to pursue various avenues of investment to ascend the next mountain of challenges – vehicle autonomy, smart mobility, connectivity, and electrification – for reducing accidents and injuries. A number of studies pertaining to ADAS scrutinize a specific ADAS technology for its effectiveness at mitigating accidents and reducing injury severity. A few studies take holistic accounts of ADAS. There are a number of directions ADAS could be further progressed. Industry manufacturers are improving existing ADAS technologies through multiple avenues of technology advancement. A number of ADAS systems have already been improved from passive, alert or warning, systems to active systems which provide early warning and if no action is taken will control the vehicle to avoid a collision or reduce the impact of the collision. Studies about the individual ADAS technologies have found significant improvement for reduction in collisions, but when evaluating the actual vehicles driving the performance of ADAS has been fairly constant since 2015. At the same time, industry is looking at networking vehicle ADAS with fixed infrastructure or with other vehicles’ ADAS. The present literature surrounding connected ADAS be it with fixed systems or other vehicles with ADAS focuses on the why and the how information is passed between vehicles. The ultimate goal of ADAS and connected ADAS is the development of autonomous vehicles. Biologically inspired systems provide an intriguing avenue for examination by applying self-organization found in biological communities to connecting ADAS among vehicles and fixed systems. Biological systems developed over millions of years to become highly organized and efficient. Biological inspiration has been used with much success in several engineering and science disciplines to optimize processes and designs. Applying movement patterns found in nature to automotive transportation is a rational progression. This work strategizes how to further the effectiveness of ADAS through the connection of ADAS with supporting assets both fixed systems and other vehicles with ADAS based on biological inspiration. The connection priorities will be refined by the relative positioning of the assets interacting with a particular vehicle’s ADAS. Then based on the relative positioning data distribution among systems will be stratified based on level of relevance. This will reduce the processing time for incorporating the external data into the ADAS actions. This dissertation contributes to the present understanding of ADAS effectiveness in real-world situations and set forth a method for how to optimally connect local ADAS vehicles following from biological inspiration. Also, there will be a better understanding of how ADAS reduces accidents and injury severity. The method for how to structure an ADAS network will provide a framework for auto-manufacturers for the development of their proprietary networked ADAS. This method will lead to a new horizon for reducing accidents and injury severity through the design of connecting ADAS equipped vehicles.Ph.D