Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Bio-inspired retinal optic flow perception in robotic navigation

This thesis concerns the bio-inspired visual perception of motion with emphasis on locomotion targeting robotic systems. By continuously registering moving visual features in the human retina, a sensation of a visual flow cue is created. An interpretation of visual flow cues forms a low-level motion perception more known as retinal optic flow. Retinal optic flow is often mentioned and credited in human locomotor research but only in theory and simulated environments so far. Reconstructing the retinal optic flow fields using existing methods of estimating optic flow and experimental data from naive test subjects provides further insight into how it interacts with intermittent control behavior and dynamic gazing. The retinal optic flow is successfully demonstrated during a vehicular steering task scenario and further supports the idea that humans may use such perception to aid their ability to correct their steering during navigation.To achieve the reconstruction and estimation of the retinal optic flow, a set of optic flow estimators were fairly and systematically evaluated on the criteria on run-time predictability and reliability, and performance accuracy. A formalized methodology using containerization technology for performing the benchmarking was developed to generate the results. Furthermore, the readiness in road vehicles for the adoption of modern robotic software and related software processes were investigated. This was done with special emphasis on real-time computing and introducing containerization and microservice design paradigm. By doing so, continuous integration, continuous deployment, and continuous experimentation were enabled in order to aid further development and research. With the method of estimating retinal optic flow and its interaction with intermittent control, a more complete vision-based bionic steering control model is to be proposed and tested in a live robotic system

Towards a design process for computer-aided biomimetics

Computer-Aided Biomimetics (CAB) tools aim to support the integration of relevant biological knowledge into biomimetic problem-solving processes. Specific steps of biomimetic processes that require support include the identification, selection and abstraction of relevant biological analogies. Existing CAB tools usually aim to support these steps by describing biological systems in terms of functions, although engineering functions do not map naturally to biological functions. Consequentially, the resulting static, functional view provides an incomplete understanding of biological processes, which are dynamic, cyclic and self-organizing. This paper proposes an alternative approach that revolves around the concept of trade-offs. The aim is to include the biological context, such as environmental characteristics, that may provide information crucial to the transfer of biological information to an engineering application. The proposed design process is exemplified by an illustrative case study

Advancing the Human Self

Do technologies advance our self-identities, as they do our bodies, cognitive skills, and the next developmental stage called postpersonal? Did we already manage to be fully human, before becoming posthuman? Are we doomed to disintegration and episodic selfhood? This book examines the impact of radical technopoiesis on our selves from a multidisciplinary perspective, including the health humanities, phenomenology, the life sciences and humanoid AI (artificial intelligence) ethics. Surprisingly, our body representations show more plasticity than scholarly concepts and sociocultural narratives. Our embodied selves can withstand transplants, bionic prostheses and radical somatechnics, but to remain autonomous and authentic, our agential potentials must be strengthened – and this is not through ‘psychosurgery’ and the brain–computer interface

MOTION CONTROL SIMULATION OF A HEXAPOD ROBOT

This thesis addresses hexapod robot motion control. Insect morphology and locomotion patterns inform the design of a robotic model, and motion control is achieved via trajectory planning and bio-inspired principles. Additionally, deep learning and multi-agent reinforcement learning are employed to train the robot motion control strategy with leg coordination achieves using a multi-agent deep reinforcement learning framework. The thesis makes the following contributions: First, research on legged robots is synthesized, with a focus on hexapod robot motion control. Insect anatomy analysis informs the hexagonal robot body and three-joint single robotic leg design, which is assembled using SolidWorks. Different gaits are studied and compared, and robot leg kinematics are derived and experimentally verified, culminating in a three-legged gait for motion control. Second, an animal-inspired approach employs a central pattern generator (CPG) control unit based on the Hopf oscillator, facilitating robot motion control in complex environments such as stable walking and climbing. The robot\u27s motion process is quantitatively evaluated in terms of displacement change and body pitch angle. Third, a value function decomposition algorithm, QPLEX, is applied to hexapod robot motion control. The QPLEX architecture treats each leg as a separate agent with local control modules, that are trained using reinforcement learning. QPLEX outperforms decentralized approaches, achieving coordinated rhythmic gaits and increased robustness on uneven terrain. The significant of terrain curriculum learning is assessed, with QPLEX demonstrating superior stability and faster consequence. The foot-end trajectory planning method enables robot motion control through inverse kinematic solutions but has limited generalization capabilities for diverse terrains. The animal-inspired CPG-based method offers a versatile control strategy but is constrained to core aspects. In contrast, the multi-agent deep reinforcement learning-based approach affords adaptable motion strategy adjustments, rendering it a superior control policy. These methods can be combined to develop a customized robot motion control policy for specific scenarios

Proceedings of the COST Action TU1403 : Adaptive Facades Network Final Conference

info:eu-repo/semantics/publishedVersio

Design, Control, and Perception of Bionic Legs and Exoskeletons

Bionic systems---wearable robots designed to replace, augment, or interact with the human body---have the potential to meaningfully impact quality of life; in particular, lower-limb prostheses and exoskeletons can help people walk faster, better, and safer. From a technical standpoint, there is a high barrier-to-entry to conduct research with bionic systems, limiting the quantity of research done; additionally, the constraints introduced by bionic systems often prohibit accurate measurement of the robot's output dynamics, limiting the quality of research done. From a scientific standpoint, we have begun to understand how people regulate lower-limb joint impedance (stiffness and damping), but not how they sense and perceive changes in joint impedance. To address these issues, I first present an open-source bionic leg prosthesis; I describe the design and testing process, and demonstrate patients meeting clinical ambulation goals in a rehabilitation hospital. Second, I develop tools to characterize open-loop impedance control systems and show how to achieve accurate impedance control without a torque feedback signal; additionally, I evaluate the efficiency of multiple bionic systems. Finally, I investigate how well people can perceive changes in the damping properties of a robot, similar to an exoskeleton. With this dissertation, I provide technical and scientific advances aimed at accelerating the field of bionics, with the ultimate goal of enabling meaningful impact with bionic systems.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163108/1/afazocar_1.pd