Small UAS Detect and Avoid Requirements Necessary for Limited Beyond Visual Line of Sight (BVLOS) Operations

Potential small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) operational scenarios/use cases and Detect And Avoid (DAA) approaches were collected through a number of industry wide data calls. Every 333 Exemption holder was solicited for this same information. Summary information from more than 5,000 exemption holders is documented, and the information received had varied level of detail but has given relevant experiential information to generalize use cases. A plan was developed and testing completed to assess Radio Line Of Sight (RLOS), a potential key limiting factors for safe BVLOS ops. Details of the equipment used, flight test area, test payload, and fixtures for testing at different altitudes is presented and the resulting comparison of a simplified mathematical model, an online modeling tool, and flight data are provided. An Operational Framework that defines the environment, conditions, constraints, and limitations under which the recommended requirements will enable sUAS operations BVLOS is presented. The framework includes strategies that can build upon Federal Aviation Administration (FAA) and industry actions that should result in an increase in BVLOS flights in the near term. Evaluating approaches to sUAS DAA was accomplished through five subtasks: literature review of pilot and ground observer see and avoid performance, survey of DAA criteria and recommended baseline performance, survey of existing/developing DAA technologies and performance, assessment of risks of selected DAA approaches, and flight testing. Pilot and ground observer see and avoid performance were evaluated through a literature review. Development of DAA criteria—the emphasis here being well clear— was accomplished through working with the Science And Research Panel (SARP) and through simulations of manned and unmanned aircraft interactions. Information regarding sUAS DAA approaches was collected through a literature review, requests for information, and direct interactions. These were analyzed through delineation of system type and definition of metrics and metric values. Risks associated with sUAS DAA systems were assessed by focusing on the Safety Risk Management (SRM) pillar of the SMS (Safety Management System) process. This effort (1) identified hazards related to the operation of sUAS in BVLOS, (2) offered a preliminary risk assessment considering existing controls, and (3) recommended additional controls and mitigations to further reduce risk to the lowest practical level. Finally, flight tests were conducted to collect preliminary data regarding well clear and DAA system hazards

Effects of Feedback Mapping on Human Control of Robotic Systems in Individual and Cooperative Tasks

In this chapter, we discuss how humans learn to interact with robots with different types of feedback. Specifically, we examine human-robot interaction during reversed control situations and how two humans can jointly control a single robot. In learning to work with unmanned aerial systems, endoscopic surgery tools, or industrial robots, one of the many challenges to humans is mapping the secondary control rapidly and accurately. Three of the studies included in this chapter extend what is currently known about cooperative human-human robot control and individual human-robot control. We focus on control of a randomly moving object, cooperative dyads working via separate master robots to cooperatively control a single robot, and humans having her/his own pair of robotic arms attached to opposite sides of an object when doing a cooperative task. Depending on the interaction and the number of humans in control, the controls can have a one-to-one correspondence, be partially reversed (remotely controlled plane flying toward the operator) or have the fulcrum effect where all motions are reversed. The combined discussion of these research areas reveals the effect of different types of feedback and suggests extensions of current methods for testing feedback conditions with respect to theory in engineering and human factors. This chapter also discusses how the forces are affected, how humans are able to mediate their interactions through a haptic device, and how performance time is affected. The results of these studies help inform how humans use feedback to adapt to the controls required in many types of robot systems and add additional information on human limitations during adaptation and learning

Effects of Feedback Mapping on Human Control of Robotic Systems in Individual and Cooperative Tasks

In this chapter, we discuss how humans learn to interact with robots with different types of feedback. Specifically, we examine human-robot interaction during reversed control situations and how two humans can jointly control a single robot. In learning to work with unmanned aerial systems, endoscopic surgery tools, or industrial robots, one of the many challenges to humans is mapping the secondary control rapidly and accurately. Three of the studies included in this chapter extend what is currently known about cooperative human-human robot control and individual human-robot control. We focus on control of a randomly moving object, cooperative dyads working via separate master robots to cooperatively control a single robot, and humans having her/his own pair of robotic arms attached to opposite sides of an object when doing a cooperative task. Depending on the interaction and the number of humans in control, the controls can have a one-to-one correspondence, be partially reversed (remotely controlled plane flying toward the operator) or have the fulcrum effect where all motions are reversed. The combined discussion of these research areas reveals the effect of different types of feedback and suggests extensions of current methods for testing feedback conditions with respect to theory in engineering and human factors. This chapter also discusses how the forces are affected, how humans are able to mediate their interactions through a haptic device, and how performance time is affected. The results of these studies help inform how humans use feedback to adapt to the controls required in many types of robot systems and add additional information on human limitations during adaptation and learning