With the promise of greater safety and adaptability, modular reconfigurable uncrewed air vehicles have been proposed as unique, versatile platforms holding the potential to replace multiple types of monolithic vehicles at once. State-of-the-art rigidly assembled modular vehicles are generally two-dimensional configurations in the shape of a “flight array”, where the rotors are coplanar. We introduce the Dodecacopter, a new type of modular rotorcraft where all modules take the shape of a regular dodecahedron, allowing the creation of richer sets of configurations beyond flight arrays. In particular, we show how the chosen module design can be used to create three-dimensional and fully actuated configurations. We justify the relevance of these types of configurations in terms of their structural and actuation properties with various performance indicators. Given the broad range of configurations and capabilities that can be achieved with our proposed design, we formulate tractable optimization programs to find optimal configurations given structural and actuation constraints. Finally, a prototype of such a vehicle is presented along with results of performed flights in multiple configurations.

Advanced machine learning algorithms require platforms that are extremely robust and equipped with rich sensory feedback to handle extensive trial-and-error learning without relying on overwhelming inductive biases. Traditional robotic designs, while well-suited for their specific use cases, are often fragile when used with these algorithms as they fail to address the intermediate sub-optimal posterior-based behavior these algorithms exhibit. To address this gap—and inspired by the vision of enabling curiosity-driven baby robots—we present a novel robotic limb designed from scratch. Our design features a semi-soft structure, a high degree of redundancy achieved through rich non-contact sensors (exclusively cameras), and strategically designed, easily replaceable failure points. Proof-of-concept experiments using two contemporary reinforcement learning algorithms on a physical prototype demonstrate that our design is able to succeed in a target-finding task even under simulated sensor failures, all with minimal human oversight during extended learning periods. Additional experiments on the robustness of the design show that it is able to withstand relatively large amounts of mechanical stress. We believe this design represents a concrete step toward more tailored robotic designs capable of supporting general-purpose, generally intelligent robots.

To create a self-repairing 3D printer, it must continue operating even after experiencing corruption. This work focuses on developing a method to effectively utilize a malfunctioning printer for reliable printing. This method can be applied by the printer itself for self-repair and enhance the reliability of commercial 3D printers. We achieve this by modeling the dynamics of the corrupted printer using a machine learning model that by observing one trajectory infers the corrupted printer dynamics to improve its accuracy. Our method is evaluated on a digital twin of the 3D printer, demonstrating its capability to enable the printer to operate reliably, even when encountering new corruptions not encountered during training. The scripts are public on https://github.com/piotrpiekos/adaptive-printer.

Aircraft trajectory is one of the most fundamental objects in air traffic management. Its optimization is essential to ensure efficient and sustainable aviation. This survey proposes to study all the phases of a flight, from its prediction several days before day of flight to the landing of the aircraft, including also the study of a possible emergency situation. Each phase of flight raises different issues and is subject to particular constraints. These guide the choice of potentially usable optimization methods. This study proposes, from the context, the issues, and existing studies, a methodology to identify the most appropriate solution algorithms for optimizing each phase of flight. This methodology is based on 5 evaluation criteria: optimality, computing time, adaptability, memory usage, and multi-trajectories. Finally, thanks to it, some methods are compared based on their consistency with solving problem associated to each phase of flight.

We analyze and model a belt transmission system with non-circular timing pulleys. Using a 3D printer as a proof-of-concept device, experiments consisting of tracking the pose data of a printer nozzle and its pulleys are conducted. A computational model from our previous work is validated with the experimental data and expanded to model more complex systems with multiple non-circular timing pulleys as well as slippage and non-ideal tensions. Finally, an example with two non-circular timing pulleys is presented and simulated utilizing the proposed method.

This paper presents a new design of aerial vehicles with tetrahedral geometry. We call this design the TetraQuad. The TetraQuad is a fractal modular aerial robot. A characteristic of fractals is that they have a geometric shape that can be assembled to generate the same geometry on a larger scale. Therefore multiple TetraQuad modules can be assembled to produce a larger scaled tetrahedral shaped aerial vehicle. The advantage is to have modular aerial robots that assemble in the vertical direction; this increases the rigidity of the structure, as well as reduces the wake interaction of the elevated propellers in the assembly. This work presents a design and analysis of the TetraQuad module as well as assemblies of multiple modules. A modular controller strategy is discussed. The functionality of the controller is illustrated using simulations. We validate our design with experimental flight tests.

This paper addresses the design of conflict free trajectories for sets of flights crossing areas subject to wind and meteorological hazards. After presenting the collaborative process based on first come first served principle, a dynamic Fast Marching Tree Star algorithm is proposed. The algorithm first computes initial avoidance trajectories and then updates them according to the new position of the hazard areas. Smart samplings are also proposed to reduce as much as possible the computing times. Indeed, the method generates in less than 1 second avoidance trajectories and guarantees safety. Then, a discussion on this method is conducted and shows its inequity. First simulations illustrate the proposed solution approach with promising results. However, to enhance equity between aircraft, the method is improved through a fairer collaborative process.

Inspired by Alan Turing's idea of a child machine, in this article, we introduce the formal definition of a robotic baby, an integrated system with minimal world knowledge at birth, capable of learning incrementally and interactively, and adapting to the world. Within the definition, fundamental capabilities and system characteristics of the robotic baby are identified and presented as the system-level requirements. As a minimal viable prototype, theBabyarchitecture is proposed with a systems engineering design approach to satisfy the system-level requirements, which has been verified and validated with simulations and experiments on a robotic system. We demonstrate the capabilities of the robotic baby in natural language acquisition and semantic parsing in English and Chinese, as well as in natural language grounding, natural language reinforcement learning, natural language programming, and system introspection for explainability. The education and evolution of the robotic baby are illustrated with real-world robotic demonstrations. Inspired by the genetic inheritance in human beings, knowledge inheritance in robotic babies and its benefits regarding evolution are discussed.

The direct connection of the jaw to the brain allows it to retain its motor and sensory capabilities even after severe spinal cord injuries. As such, it can be an accessible means of providing inputs for people with paralysis to manipulate their environment. This paper explores the potential for using the jaw, specifically the mandible, as an alternative input to human-machine interface systems. Two tests were developed to test the mandible’s ability to respond to visual stimuli. First, a visual response time test to measure the precision and accuracy of user input through a mandible-actuated button. Second, a choice response test to observe coordination between the mandible and a finger.Study results show that the mean response time of mandible inputs is 8.3% slower than the corresponding mean response time of performing the same task with a thumb. The delay in response after making a decision is statistically insignificant between the mandible-and finger-actuated inputs with the mandible being 2.67% slower.Based on these results, the increase in response time while using the mandibular input is minimal for new users. Coordination is feasible in tasks involving both the mandible and thumb. Extensive training with a made-to-fit device has the potential to enable a visual response time equivalent to the fingers in more complex tasks. The mandible is a viable option for accessible HMI for discreet inputs. Further testing into continuous input is needed to explore the mandible’s potential as an input for body augments.

The COVID-19 pandemic has profoundly affected the air transportation system, its structure, its reliability, and its dynamics. Passengers have in turn significantly adapted their behavior. Through a case study at Paris-Charles de Gaulle airport, the present paper examines the new traffic network, the fact that delays remain high despite a drop in flight volume, the significant decrease in aircraft load factors and the change in passenger behavior at the airport.

In this work, we show how controlled robustly forward invariant sets for systems with disturbances are efficiently identified via the application of the mixed monotonicity property. A mixed monotone system can be embedded in a related deterministic embedding system with twice as many states but for which the dynamics are monotone; one can then apply the powerful theory of monotone dynamical systems to the embedding system to conclude useful properties of the initial mixed monotone system. Using this technique, we present a method for verifying state-feedback controllers against safety (set invariance) constraints, and our approach involves evaluating a control barrier function type condition that requires the vector field of the embedding system to point into a certain southeast cone. This approach also facilitates the construction of runtime assurance mechanisms for controlled systems with disturbances, and we study system safety in the presence of state uncertainty as well. The results and findings of this work are demonstrated through two numerical examples where we study (i) the verification of a controlled spacecraft system against a safety constraint, and (ii) the formation of a runtime assurance mechanism that functions in the presence of uncertain state measurements.

Classical control of cyber-physical systems used to rely on basic linear controllers. These controllers provided a safe and robust behavior but lack the ability to perform more complex controls such as aggressive maneuvering or performing fuel-efficient controls. Another approach called optimal control is capable of computing such difficult trajectories but lacks the ability to adapt to dynamic changes in the environment. In both cases, the control was designed offline, relying on more or less complex algorithms to find the appropriate parameters. More recent kinds of approaches such as Linear Model-Predictive Control (MPC) rely on the online use of convex optimization to compute the best control at each sample time. In these settings optimization algorithms are specialized for the specific control problem and embed on the device. This paper proposes to revisit the code generation of an interior point method (IPM) algorithm, an efficient family of convex optimization, focusing on the proof of its implementation at code level. Our approach relies on the code specialization phase to produce additional annotations formalizing the intended specification of the algorithm. Deductive methods are then used to prove automatically the validity of these assertions. Since the algorithm is complex, additional lemmas are also produced, allowing the complete proof to be checked by SMT solvers only. This work is the first to address the effective formal proof of an IPM algorithm. The approach could also be generalized more systematically to code generation frameworks, producing proof certificate along the code, for numerical intensive software.

This paper describes the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-robot research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium and discusses the considerations one must take when making complex hardware remotely accessible. In particular, safety must be built into the system already at the design phase without overly constraining what coordinated control programs users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.

Robustness analyses play a major role in the synthesis and analysis of controllers. For control systems, robustness is a measure of the maximum tolerable model inaccuracies or perturbations that do not destabilize the system. Analyzing the robustness of a closed-loop system can be performed with multiple approaches: gain and phase margin computation for single-input single-output (SISO) linear systems, mu analysis, IQC computations, etc. However, none of these techniques consider the actual code in their analyses.

We consider an autonomous vehicle navigation problem, whereby a traveler aims at traversing an environment in which an adversary tries to set an ambush. Optimal strategies are computed as random path distributions, a realization of which is the path chosen by the traveler. Theoretical optimal policies are derived under assumptions from the Minimal Cut-Maximal Flow literature. Numerical approaches to compute such optimal strategies are proposed. These numerical approaches, which borrow from randomized path planning techniques, can be implemented for high-dimensional configuration spaces. The methodology developed allows for the application of ambush games on complex environments for realistic applications regarding vehicle routing in adversarial settings.

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes are coupled and interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. Collaborative decision-making has shown significant benefits at the airport level, both in the U.S. and in Europe. This paper examines how it could be extended to the multimodal network level, discusses the supporting evidence, and provides recommendations for implementation. A case study on the disruption management following the Asiana Crash at San Francisco International Airport is presented. The crash led to a large number of flight diversions to many airports, such as Oakland, Los Angeles, but also Seattle for instance, disrupting the journeys of thousands of passengers. Passenger reaccommodation varied greatly from airline to airline and airport to airport. First, a passenger-centric reaccommodation scheme is developed to balance costs and delays, for each diversion airport. Second, assuming better information sharing and collaborative decision-making, we show that there was enough capacity at the neighboring airports, Oakland and San Jose, to accommodate most of the diverted flights and reoptimize the allocation of flight diversions to the Bay Area airports. Based on this case study, recommendations for the adoption of multimodal CDM are elaborated. This paper paves the way for further data-driven research for increased resilience of passenger door-to-door journeys.

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes, whether air, road, or rail, are intrinsically coupled through passenger transfers and are interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. This paper provides a case report of the Asiana crash in San Francisco International Airport (SFO) on July 6, 2013, and its repercussions on the multimodal transportation network. It studies the resulting propagation of disturbances on the transportation infrastructure in the USA, particularly on the U.S. air transport network and the ground transportation in the Bay Area. The perturbation takes different forms and varies in scale and time frame: cancelations and delays snowball in the airspace, with up to 86% of cancelations in the U.S. due to the SFO crash; highway traffic near the airport is impacted by congestion in previously not congested locations, with low speed and high delays on US 101; and transit passenger demand exhibits unusual traffic peaks in between airports in the Bay Area, with up to 180 passengers more per hour between SFO and Oakland International Airport Bay Area Rapid Transit stations. This paper also investigated the effect of the crash on the social media Twitter. This paper, through a case study, aims at stressing the importance of further data-driven research on interdependent infrastructure networks. The end goal is to form the basis for optimization models behind providing more reliable passenger door-to-door journeys and improved transport network resilience.

This paper considers the challenges in the architectural design of cyber-physical systems (CPS). Cyber-physical systems are real-time control and coordination systems that rely on computational infrastructure. We help to elucidate these challenges by comparing cyber-physical system design to system-on-chip design. We then survey CPS architectures and identify several important challenges.

Airport departure operations are a source of airline delays and passenger frustration. Excessive surface traffic is a cause of increased controller and pilot workload. It is also a source of increased emissions and delays, and it does not yield improved runway throughput. Leveraging the extensive past research on airport departure management, this paper explores the environmental and safety benefits that improved surveillance technologies can bring in the context of gate- or spot-release strategies. This paper shows that improved surveillance technologies can yield a 4%-6% reduction of the average number of aircraft on the taxiway system during congested operations, and therefore emissions, in addition to the savings currently observed by implementing threshold-based metering strategies under evaluation at Boston's Logan Airport and other busy airports during congested periods. These calculated benefits contrast sharply with our previous work, which relied on simplified airport ramp areas with a single departure spot and where fewer environmental and economic benefits of advanced surface surveillance systems could be established. Our work is illustrated by its application to New York's LaGuardia and Seattle-Tacoma airports in Washington.

This paper proposes a compact configuration of aircraft flows at intersections. The goal is to achieve a higher capacity of the airspace, allowing more aircraft to safely fly through a fixed region. Intersections of aircraft flows can be considered as basic building blocks for air traffic networks, and traffic networks can be designed through finding optimal arrangements of intersections whose conflict zones do not overlap. A conflict zone is defined as a minimal circular area centered at the intersection of two flows, which allows aircraft approaching the intersections to resolve conflict completely within the conflict zone. This paper derives the relationship between the size of a conflict zone and the intersection angle of the two flows. Such a relationship guides the choice of the most compact configuration for intersecting aircraft flows. An example involving multiple converging flows of aircraft demonstrates the efficiency of the proposed configuration of intersections. The result of conflict resolution shows a greatly reduced traffic complexity. Therefore, our study provides a potential solution to increase airspace capacity.

Departure metering reduces congestion by reducing the number of aircraft present on the airport surface at any time while not starving the runway. Because some departing flights are held at gates, there is a possibility that arriving flights cannot access the gates and have to wait until the gates are cleared. This is called a gate conflict. Robust gate assignment is an assignment that minimizes gate conflicts by assigning gates to aircraft to maximize the time gap between two consecutive flights at the same gate; it makes gate assignment robust, but passengers may walk longer to transfer flights. In order to simulate the airport departure process, a queuing model is introduced. The model is calibrated and validated with actual data from New York's LaGuardia Airport (LGA) and a large U.S. hub airport. Then, the model simulates the airport departure process with the current gate assignment and a robust gate assignment to assess the impact of gate assignment on departure metering. The results show that the robust gate assignment reduces the number of gate conflicts caused by departure metering compared with the current gate assignment. Therefore, robust gate assignment can be combined with departure metering to improve operations at congested airports with limited gate resources.

We demonstrate a novel method to reliably generate collision avoidance advisories, in piloted simulations, by the widely used traffic alert and collision avoidance system (TCAS). The TCAS advisory issued to a pilot is highly sensitive to the trajectory of an intruder aircraft relative to the ownship flown by the pilot. In realistic piloted simulations, a prescripted intruder trajectory will not reliably result in the relative dynamics that lead to a desired TCAS advisory. Further, the complexity of the TCAS logic requires a novel method for mapping trajectories to the range of possible advisories. We propose to use a rapidly exploring random tree algorithm in large-scale fast-time simulations to establish the mapping between the space of relative trajectories and TCAS advisories. These trajectories are then created in piloted simulations through guidance algorithms. Results demonstrate the ease of use and robustness of this method, and its potential for pilot training and for research and development.

Embedded system control often relies on linear systems, which admit quadratic invariants. The parts of the code that host linear system implementations need dedicated analysis tools, since intervals or linear abstract domains will give imprecise results, if any at all, on these systems. Previous work by FERET proposes a specific abstraction for digital filters that addresses this issue on a specific class of controllers.

As airport surface surveillance technologies develop, aircraft ground position information becomes more easily available and accurate. This paper provides a better understanding of the value of future surface surveillance systems where departures, and more specifically pushback times, will be optimized. It analytically quantifies the potential benefits yielded by providing surveillance information to the agent or system that is entrusted with tactically optimizing pushback clearances under nominal conditions. A stochastic model of surface operations is developed for single-ramp surface operations and calibrated to emulate departure surface operations at LaGuardia Airport. Two levels of information are examined within a tactically optimized collaborative decision-making framework. For each level, emissions, number of taxiing aircraft, and runway utilization rate are analyzed and compared with a simple threshold policy to evaluate surface surveillance information. Safety benefits, however, are not considered in this paper. It is estimated that optimally controlling pushback clearances from a single-ramp area using detailed surface surveillance information does not provide significant benefits when compared with controlling pushback clearances using a gate-holding policy based on the number of aircraft currently taxiing. However, when the runway is functioning at intermediate capacity (50%-72% runway utilization rates), e.g., under adverse weather conditions, surveillance information may improve optimization of departure operations. In such case, emissions and the number of taxiing aircraft are reduced by up to 6% when compared with the gate-holding policy and by up to 3% when compared with the performance of an intelligent operator with limited information.

We investigate a state feedback Linear Quadratic Regulation problem with a constraint on the number of actuation signals that can be updated simultaneously. Such a constraint arises for example in networked and embedded control systems, due to limited communication and computation capabilities. Following recent results on the dual problem of scheduling Kalman filters, we first develop a bound on the achievable performance that can be computed efficiently by semidefinite programming. This bound can be approached arbitrarily closely by an analog periodic controller that can switch between control inputs arbitrarily fast. We then discuss implementation issues on digital platforms, i.e., the discretization of the analog controller in the presence of a relatively fast but finite sampling rate.

This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft.

Human factors constitute a core aspect of air traffic control concepts and must be addressed when dealing with traffic flow stability problems. This paper aims to evaluate how the usage of airspace depends on the degree of flexibility in pilot decision-making. Specifically, we study the airspace usage of two intersecting flows of aircraft when pilot maneuver preferences vary. The amount of airspace required for the two flows to cross without conflict can reveal certain aspects of traffic complexity of this specific traffic pattern. To simplify the analysis, we use models of instantaneous lateral and longitudinal position changes to approximate the heading-change and speed-change maneuvers of the aircraft, respectively. Pilot preferences, which are expressed as preferred heading or speed changes, are reflected by different penalty functions of position displacements that the pilots attempt to minimize during conflict resolution. Under the same pilot preferences, the aircraft flow stability is preserved using a decentralized conflict-resolution scheme. However, when the pilot preferences are allowed to vary so that individual aircraft have more control freedom in conflict resolution, the intersecting flows require a much larger fraction of airspace to ensure conflict-free and stable flows of aircraft.

Electronics systems for modern vehicles must be designed to meet stringent requirements on real-time performance, safety, power consumption, and security. Hardware/software codesign techniques allow system designers to create platforms that can both meet those requirements and evolve as components and system requirements evolve. Design methodologies have evolved that allow systems-of-systems to be built from subsystems that are themselves embedded computing systems. Software performance is a key metric in the design of these systems. A number of methods-of-methods for the analysis of worst case execution time have been developed. More recently, we have developed new methods for software performance analysis based on design of experiments. Formal methods can be used to verify system properties. Systems must be architected to maintain their integrity in the face of attacks from the Internet. All of these techniques build upon generic hardware/software codesign techniques but with significant adaptations to the technical and economic context of vehicle design.

Recent news illustrates the frequent occurrence of pileup crashes on highways. A predominant reason for the occurrence of such crashes is that current vehicles (including those equipped with an automatic cruise control system) do not provide drivers with advance information of events occurring far ahead of them. The use of intervehicular communication to provide advance warnings to enhance automotive safety is therefore being actively discussed in the research community. In this paper, we investigate scenarios wherein only a subset of the vehicles in a multivehicle stream is equipped with such advance-warning capabilities. These vehicles (which are equipped with the capability to receive far-ahead information) are arbitrarily distributed among other unequipped vehicles that are capable of receiving only local near-neighbor information. It is seen that there are conditions wherein even a partial equipage of the system can be beneficial to both equipped and unequipped vehicles in a mixed-vehicle stream. We demonstrate this through both simulations and a theoretical analysis. We also developed a prototype of an advance-warning system and conducted road tests to test the concept. These road tests have demonstrated the system's performance to be satisfactory, subject to good communication links, for the class of scenarios tested.

This paper aims to provide reliable indications of driver drowsiness based on the characteristics of driver-vehicle interaction. A test bed was built under a simulated driving environment, and a total of 12 subjects participated in two experiment sessions requiring different levels of sleep (partial sleep-deprivation versus no sleep-deprivation) before the experiment. The performance of the subjects was analyzed in a series of stimulus-response and routine driving tasks, which revealed the performance differences of drivers under different sleep-deprivation levels. The experiments further demonstrated that sleep deprivation had greater effect on rule-based than on skill-based cognitive functions: when drivers were sleep-deprived, their performance of responding to unexpected disturbances degraded, while they were robust enough to continue the routine driving tasks such as lane tracking, vehicle following, and lane changing. In addition, we presented both qualitative and quantitative guidelines for designing drowsy-driver detection systems in a probabilistic framework based on the paradigm of Bayesian networks. Temporal aspects of drowsiness and individual differences of subjects were addressed in the framework.

The introduction of new technologies and concepts of operation in the air transportation system is not possible unless they can be proven not to adversely affect the system operation under not only nominal but also degraded conditions. In extreme scenarios, degraded operations due to partial or complete technological failures should never endanger system safety. Many past system evolutions, whether ground-based or airborne, have been based on trial and error, and system safety was addressed only after a specific event yielded dramatic or near-dramatic consequences. Future system evolutions, however, must leverage available computation, prior knowledge, and abstract reasoning to anticipate all possible system degradations and prove that such degradations are graceful and safe. This paper is concerned with the graceful degradation of high-density structured arrival traffic against partial or complete surveillance failures. It is shown that for equal performance requirements, some traffic configurations might be easier to handle than others, thereby offering a quantitative perspective on these traffic configurations' ability to ldquogracefully degrade.rdquo To support our work, we also introduce a new conflict resolution algorithm aimed at solving conflicts involving many aircraft when aircraft position information is in the process of degrading.

This paper studies the conflict resolution for intersecting flows of mobile agents based on planar space partition. The idea of space partition is first demonstrated for two intersecting flows of mobile agents. Then, for three intersecting flows, where simple decentralized conflict avoidance rules may not handle all traffic scenarios, it is proved that certain periodic partitions of space are able to provide conflict resolution for any distribution of agents in the flows. A computational procedure based on mixed integer programming is further proposed to find optimal space partitions. The approach of space partition is not an online optimization algorithm. An online algorithm may find optimal resolution of conflict for a specific set of mobile agents but has to be rerun each time when new agents arrive, whereas a periodic partition of space provides a priori geometrical configuration for conflict avoidance regardless of the number and arriving patterns of the agents. Moreover, the offline nature of space partition does not imply a decrease of performance. As demonstrated in an example involving three symmetrically arranged agent flows, the optimal space partition has found a tight upper bound for the magnitude of any conflict-free maneuvers.

Understanding how humans assist each other in haptic interaction teams could lead to improved robotic aids to solo human dextrous manipulation. Inspired by experiments reported in Reed et al. (2004), which suggested two-person haptically interacting teams could achieve a lower movement time (MT) than individuals for discrete aiming movements of specified accuracy, we report that two-person teams (dyads) can also achieve lower MT for cyclical, continuous aiming movements. We propose a model, called endpoint compromise, for how the intended endpoints of both subjects' motion combine during haptic interaction; it predicts a ratio of /spl radic/2 between slopes of MT fits for individuals and dyads. This slope ratio prediction is supported by our data.

This paper analyzes two intersecting flows of aircraft that must avoid each other, utilizing maneuvers modeled by instantaneous heading changes. Sufficient conditions are derived for stable conflict avoidance, whereby one aircraft's conflict-avoidance action does not generate cascaded diverging conflict-avoidance behavior (known as the domino effect) in neighboring aircraft. The mathematics involved with the heading-change model are considerably more complex than those using the simpler offset-maneuver models utilized in the previous work. However, this additional effort is largely compensated by the improved realism of the maneuver model, as well as new insights in the inherent robustness of conflict-avoidance maneuver schemes based on heading changes against specified separation standards. The analytical results are compared with simulations.

In this paper, we introduce an approach for the efficient solution of motion-planning problems for time-invariant dynamical control systems with symmetries, such as mobile robots and autonomous vehicles, under a variety of differential and algebraic constraints on the state and on the control inputs. Motion plans are described as the concatenation of a number of well-defined motion primitives, selected from a finite library. Rules for the concatenation of primitives are given in the form of a regular language, defined through a finite-state machine called a Maneuver Automaton. We analyze the reachability properties of the language, and present algorithms for the solution of a class of motion-planning problems. In particular, it is shown that the solution of steering problems for nonlinear dynamical systems with symmetries and invariant constraints can be reduced to the solution of a sequence of kinematic inversion problems. A detailed example of the application of the proposed approach to motion planning for a small aerobatic helicopter is presented.

This paper considers the problem of solving conflicts arising among several aircraft that are assumed to move in a shared airspace. Aircraft can not get closer to each other than a given safety distance in order to avoid possible conflicts between different airplanes. For such system of multiple aircraft, we consider the path planning problem among given waypoints avoiding all possible conflicts. In particular we are interested in optimal paths, i.e., we want to minimize the total flight time. We propose two different formulations of the multiaircraft conflict avoidance problem as a mixed-integer linear program: in the first case only velocity changes are admissible maneuvers, in the second one only heading angle changes are allowed. Due to the linear formulation of the two problems, solutions may be obtained quickly with standard optimization software, allowing our approach to be implemented in real time.

This paper considers the problem of two intersecting aircraft flows under decentralized conflict resolution rules. Considering aircraft flowing through a fixed control volume, new air traffic control models and scenarios are defined that enable the study of long-term aircraft flow stability. For a class of two intersecting aircraft flows, this paper considers conflict scenarios involving arbitrary encounter angles. It is shown that aircraft flow stability, defined both in terms of safety and performance, is preserved under the decentralized conflict resolution algorithm considered. It is shown that the lateral deviations experienced by aircraft in each flow are bounded.