In multiagent coordination tasks, motion trajectories are required to satisfy a range of constraints that present significant implementation challenges due to the limited onboard sensing and communication capacities. This article introduces distributed control laws that integrate nonholonomic motion constraints into bearing-based designs to enable time-varying formation tracking with minimal onboard resources. Unlike state-of-the-art formation control solutions, this approach maintains formation shape through relative bearing feedback and orientation alignment rather than tracking global target locations or regulating interagent relative positions and velocities. This distributed controller design has been validated in two deployment scenarios: 1) leaderless nonhierarchical formations and 2) leader-follower hierarchical formations. In hierarchical formations, follower agents employ a speed estimator within the orientation alignment framework to reach velocity consensus with the leader agent. The proposed controllers guarantee accurate tracking of time-varying reference trajectories, preserve the desired formation structures, and achieve velocity consensus for both nonhierarchical and hierarchical formations, as established by analysis and validated through simulations and experiments.

In this article, we propose a method to replan coverage paths for a robot operating in an environment with initially unknown static obstacles. Existing coverage approaches reduce coverage time by covering along the minimum number of coverage lines (straight-line paths). However, recomputing such paths online can be computationally expensive resulting in robot stoppages that increase coverage time. A naive alternative isgreedy detourreplanning, i.e., replanning with minimum deviation from the initial path, which is efficient to compute but may result in unnecessary detours. In this work, we propose an anytime coverage replanning approach namedOARP-Replanthat performs near-optimal replans to an interrupted coverage path within a given time budget. We do this by solving linear relaxations of integer linear programs to identify sections of the interrupted path that can be optimally replanned within the time budget. We validate OARP-Replan in simulation and perform comparisons against a greedy detour replanner and other state-of-the-art coverage planners. We also demonstrate OARP-Replan in experiments using an industrial-level autonomous robot.

This paper proposes a method to eliminate dynamic feature points in robot motion estimation for visual-inertial odometry (VIO) via a geometric feature matching confidence checking procedure utilizing the inertial measurement unit (IMU) data. The IMU motion model expressed in the camera frame of reference is used to estimate the fundamental matrix in this procedure. Thereafter, the estimated fundamental matrix is used to calculate the distance of the matched features to the epipolar line. Similarly the same distance is calculated using the fundamental matrix that is obtained by visual structure from motion. Then the two distances are compared to produce a feature-matching confidence measure that is used to decide whether the matched features are static or dynamic. Finally, we provide odometry simulation test results based on a real world dataset to show the effectiveness of the proposed method.

Vehicle-trailer systems have various unstable modes including trailer snaking, jack-knifing, and roll-over, which should be considered in their stability control. For stability control design purposes, various techniques have been proposed to estimate vehicle-trailer system states and parameters. Some of these techniques rely on vehicle kinematic/dynamic models while others are data-driven and do not require a model. This review paper provides a comprehensive overview of different model-based and non-model-based techniques/algorithms developed for estimating vehicle-trailer system states and parameters. The main features, limitations, and assumptions for each estimation method are discussed. The trailer parameter estimation feasibility is also investigated for different possible vehicle-trailer on-board sensor settings. This paper can be used as a review and reference resource for engineers working in vehicle with semi-trailer state estimation and safety systems.

In this paper, ultra-sonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four ultra-sonic sensors. An angle estimate is generated for each of the six possible sensor-pair combinations, and these six estimates are passed through a voting algorithm to produce a single estimate. As the second and third approaches, kinematic and dynamic models of the tractor-trailer system are used to develop least-squares and Kalman filtering based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type box trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Based on the validity of the assumptions, the proposed scheme can estimate the hitch angle in both low-speed using the kinematic model, and high-speed using the dynamic model of the tractor-trailer system. Experimental results corroborate the algorithms in estimating the hitch angle estimates in various cases conducted in this study.

Tire-road friction coefficient is an essential parameter in vehicle safety control systems. In particular, friction information is required by antilock braking systems (ABS) during deceleration and by traction control systems (TCS) during acceleration. The characteristic of the force acting on the tires has an extremum, which is dependent in the road condition. This paper develops a recursive least squares (RLS) based extremum seeking algorithm that estimates the optimum slip ratio on-line to produce maximum deceleration/acceleration. Results of simulation studies in both Matlab and CarSim environments are presented to illustrate the effectiveness of the developed algorithm and numerically compare with gradient based estimation.

A novel cyber physical method is proposed and experimentally verified for reliable distributed estimation of vehicle longitudinal velocity, robustly to road friction condition variations. In this method, the vehicle speed estimated at each of the four corners of the vehicle, using a linear parameter-varying observer in the physical layer, and speed data measured by a conventional low-cost GPS are incorporated in a distributed structure (in the cyber layer) to enhance the reliability of the estimate. The method minimizes a cost function quantizing the effect of disturbances on each corner's estimation and adversaries due to occasional GPS signal drops. A fault-tolerant estimation policy is integrated to deal with large deviations in corner estimations, which have unexpectedly high levels of confidence. The main advantages of the proposed method are increased reliability on various road surface conditions and robustness to faults, as confirmed by road tests. Several experimental tests, including lane change and low-excitation maneuvers, with various powertrain configurations on dry and slippery roads demonstrate the efficiency of the algorithm.

Reliable estimation of vehicle speed is an active topic of research in the automotive industry and academia due to its technical challenges as well as applications to vehicle traction and stability control. In this direction, the emergence of new generations of communication technologies has brought new perspectives to traditional studies on vehicle speed estimation and control. To this end, this paper introduces a cooperative vehicle speed fault diagnosis and correction algorithm. The distributed part of the algorithm is based on a distributed function calculation algorithm for vehicle networks. The introduced algorithm enables each vehicle to gather some information from other vehicles in the network in a distributed manner and is robust to communication failures. A procedure to use such information for a single vehicle to diagnose and correct a possible fault in its own speed estimation/measurement is discussed. The functionality and performance of the proposed algorithms are verified via illustrative examples and simulation results.

Limited battery capacity and long charging time resulting in what is known as range anxiety have been major obstacles to the widespread adoption of electric vehicles. In addition to running out of battery power, some drivers are also concerned about the amount of time required to recharge their batteries (i.e., time anxiety). This paper focuses on these problems, proposing a Multi-Module Range Anxiety Reduction Scheme. The proposed scheme takes into account traffic density on roadways to provide an accurate computation of energy consumption to charging stations in order to overcome the driver's concern of being stranded en-route. Furthermore, it addresses the driver's concern about completing the recharging process in either minimum energy or time. Simulations are conducted to test and validate the proposed scheme.

An opinion dynamics approach is proposed to enhance the reliability of the vehicle velocity estimators, which are required for autonomous driving as well as advanced vehicle active safety systems, such as traction and stability control. The corners' estimates of a velocity observer, which is formed by combining the kinematic and model-based estimation schemes, are used as opinions with different levels of confidence in the developed algorithm. This is to find more reliable estimates robust to disturbances and time delay via solving a convex optimization problem. To bypass the effect of failure in velocity estimation, a fault rejection policy is used concurrently with the opinion dynamics. Road tests confirm the validity and robustness of the algorithm on slippery and dry roads independent of the powertrain configuration in different driving scenarios, especially for combined-slip and low-excitation maneuvers, which are demanding for the current vehicle state estimators.

In this paper, we present the cooperative driving system developed by the Chalmers car team for the grand cooperative driving challenge 2016. This paper gives an overview of the system architecture and describes in detail the communication, signal processing, and decision-making sub-systems. Experimental results demonstrate the system's performance and operation according to the rules and requirements of the competition.

Platooning has recently been the focus of many researchers from academia and industry due to the positive impacts it has shown on the highway traffic, such as road capacity improvement. However, the impacts of platoon formation in urban areas, especially the effects on idling time near signalized intersections has not been addressed in detail. This paper investigates the effects of autonomous vehicle platooning on average idling time around signalized intersections. Speed optimization is proposed to allow vehicles to decide in a decentralized manner whether to be part of the platoon or not such that the average idling time and number of stops are minimized. Simulation is conducted and analysis results are provided, investigating the performance of the proposed scheme in terms of average idling time and average number of stops at a signalized intersection.

A distributed estimation approach based on opinion dynamics is proposed to enhance the reliability of vehicle corners' velocity estimates. The corners' estimates, which are obtained from a Kalman filter, is formed by integrating the model-based and kinematic-based velocity estimation approaches. These estimates are utilized as opinions with different levels of confidence in the developed algorithm. More reliable estimates robust to disturbances and time delay are achieved via solving a convex optimization problem. Vehicle tests with various driveline configurations are performed to verify the estimator performance under different surfaces friction conditions in pure and combined-slip (combination of longitudinal/lateral) maneuvers, which are arduous for the current vehicle state estimators.

Model predictive control (MFC) approach is prone to loss of feasibility due to the limited prediction horizon for decision making. For autonomous vehicle motion planning, many of detected obstacles, which are beyond the prediction horizon, cannot be considered in the instantaneous decisions, and late consideration of them may cause infeasibility. The conditions that guarantee persistent feasibility of a model predictive motion planning scheme are studied in this paper. Maintaining the system's states in a control invariant set of the system guarantees the persistent feasibility of the corresponding MPC scheme. Therefore, the persistent feasibility concern can be expressed as the problem of computing an effective control invariant set of the system and maintaining the system states inside it. In this paper, two approaches are presented to compute control invariant sets for the motion planning problem, the linearization-convexification approach and the brute-force search approach. The control invariant sets calculated via these two approaches are numerically analyzed and compared.

We consider a graph-theoretic approach to the performance and robustness of a platoon of vehicles, in which each vehicle communicates with its k-nearest neighbors. In particular, we quantify the platoon's stability margin, robustness to disturbances (in terms of system H∞ norm), and maximum delay tolerance via graph-theoretic notions, such as nodal degrees and (grounded) Laplacian matrix eigenvalues. The results show that there is a trade-off between robustness to time delay and robustness to disturbances. Both lurst-order dynamics (reference velocity tracking) and second-order dynamics (controlling inter-vehicular distance) are analyzed in this direction. Theoretical contributions are conlurmed via simulation results.

This paper proposes a model predictive collision avoidance scheme for use in autonomous driving, based on cooperative on-line estimation of unknown and time varying road conditions. The autonomous vehicle is linearly modelled with constraints dependent on the road condition parameter. The proposed model predictive controller (MPC) is designed to be adaptive to this parameter. To accommodate this adaptive design, a particular method is developed for estimating the road friction coefficient cooperatively, by disseminating individual estimates in a vehicular network and using a consensus algorithm to converge these estimates to the maximum likelihood value. Presented simulation results demonstrate that the cooperative consensus scheme improves estimation significantly, and accordingly, the adaptive MPC incorporates road condition properly in collision avoidance planning.

This paper presents a model predictive control (MPC) based approach to improve a recently developed class of collaborative adaptive cruise control (CACC) schemes. The PID structure used previously is replaced with MPC, which is able to accommodate actuator limits and parameter estimation. In addition to the regular CACC functionalities, rear end collision control is also incorporated. This approach is able to avoid rear end collisions with the following car, as long as it can still maintain the safe distance with the preceding vehicle. Simulation results are presented which demonstrate the validity of the approach.

Graph theory has been used to characterize the solvability of the sensor network localization problem. If sensors correspond to vertices and edges correspond to sensor pairs between which the distance is known, a significant result in the theory of range-based sensor network localization is that if the graph underlying the sensor network is generically globally rigid and there is a suitable set of anchors at known positions, then the network can be localized, i.e., a unique set of sensor positions can be determined that is consistent with the data. In particular, for planar problems, provided the sensor network has three or more noncollinear anchors at known points, all sensors are located at generic points, and the intersensor distances corresponding to the graph edges are precisely known rather than being subject to measurement noise, generic global rigidity of the graph is necessary and sufficient for the network to be localizable (in the absence of any further information). In practice, however, distance measurements will never be exact, and the equations whose solutions deliver sensor positions in the noiseless case in general no longer have a solution. This paper then argues that if the distance measurement errors are not too great and otherwise the associated graph is generically globally rigid and there are three or more noncollinear anchors, the network will be approximately localizable, in the sense that estimates can be found for the sensor positions which are near the correct values; in particular, a bound on the position errors can be found in terms of a bound on the distance errors. The sensor positions in this case can be found by minimizing a cost function which, although nonconvex, does have a global minimum.

Erroneous local geometric realizations in some parts of the network due to their sensitivity to certain distance measurement errors is a major problem in wireless sensor network localization. This may in turn affect the localization of either the entire network or a large portion of it. This phenomenon is well-described using the notion of "flip ambiguity" in rigid graph theory. In this paper we analytically derive an expression for the flip ambiguity probabilities of arbitrary neighborhoods in two dimensional sensor networks. This probability can be used to mitigate flip ambiguities in two ways: 1) If an unknown sensor finds the probability of flip ambiguity on its location estimate larger than a predefined threshold, it may choose not to localize itself 2) Every known neighbor can be assigned with a confidence factor to its estimated location, reflecting the probability of flip ambiguity; a sensor with an initially unknown location can then choose only those known neighbors with a confidence factor greater than a predefined threshold. A recent study by co-authors have shown that the performance of sequential and cluster based localization schemes in the literature can be significantly improved by correctly identifying and removing neighborhoods with possible flip ambiguities from the localization process. One motivation of this paper is to enhance the performance of the robustness criterion presented in that study by accurately identifying the flip ambiguity probabilities of arbitrary neighborhoods. The various simulations done in this study show that our analytical calculations of the probability of flip ambiguity matches with the simulated detection of the probability very accurately.

A major problem in wireless sensor network localization is erroneous local geometric realizations in some parts of the network due to the sensitivity to certain distance measurement errors, which may in turn affect the reliability of the localization of the whole or a major portion of the sensor network. This phenomenon is well-described using the notion of "flip ambiguity" in rigid graph theory. In a recent study by the coauthors, an initial formal geometric analysis of flip ambiguity problems has been provided. The ultimate aim of that study was to quantify the likelihood of flip ambiguities in arbitrary sensor neighborhood geometries. In this paper we propose a more general robustness criterion to detect flip ambiguities in arbitrary sensor neighborhood geometries in planar sensor networks. This criterion enhances the recent study by the coauthors by removing the assumptions of accurately knowing some inter- sensor distances. The established robustness criterion is found to be useful in two aspects: (a) Analyzing the effects of flip ambiguity and (b) Enhancing the reliability of the location estimates of the prevailing localization algorithms by incorporating this robustness criterion to eliminate neighborhoods with flip ambiguity from being included in the localization process.

This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise performance. Instead given a set of sensors/anchors of known positions and measured distances of the source/sensor to be localized from them, we propose a potentially nonconvex weighted cost function whose global minimum estimates the location of the source/sensor one seeks. The contribution of this paper is to provide nontrivial ellipsoidal and polytopic regions surrounding these sensors/anchors of known positions, such that if the object to be localized is in this region localization occurs by globally convergent gradient descent. This has implication to the deployment of sensors/anchors to achieve a desired level of geographical coverage.

The path loss exponent (PLE) is a parameter indicating the rate at which the received signal strength (RSS) decreases with distance, and its value depends on the specific propagation environment. Path loss exponent estimation plays an important role in distance-based wireless sensor network localization, where distance is estimated from the RSS measurements. Path loss exponent estimation is also useful for other purposes like sensor network dimensioning. Existing techniques on PLE estimation rely on both RSS measurements and distance measurements in the same environment to calibrate the PLE. However distance measurements can be difficult and expensive to obtain in some environments. In this paper we propose a novel technique for online calibration of the path loss exponent in wireless sensor networks without using distance measurements. The major contribution of this paper is to demonstrate that it is possible to estimate the PLE using only power measurements and the geometric constraints associated with planarity in a sensor network. This may have a significant impact on wireless sensor network localization.