This paper presents a novel terrain-adaptive local trajectory planner designed for the autonomous operation of off-road vehicles on deformable terrains. State-of-the-art solutions either do not account for deformable terrains, or do not offer sufficient robustness or computational speed. To bridge this research gap, the paper introduces a novel model predictive control (MPC) formulation. In contrast to the prevailing state-of-the-art approaches that rely exclusively on hard or soft constraints for obstacle avoidance, the present formulation enhances robustness by incorporating both types of constraints. The effectiveness and robustness of the formulation are evaluated through extensive simulations, encompassing a wide range of randomized scenarios, and compared against state-of-the-art methods. Subsequently, the formulation is augmented with an optimal-control-oriented terramechanics model from the literature, explicitly addressing terrain deformation. Additionally, a terrain estimator employing the unscented Kalman filter is utilized to dynamically adjust the sinkage exponent online, resulting in a terrain-adaptive formulation. This formulation is tested on a physical vehicle in real world experiments against a rigid-terrain formulation as the benchmark. The results showcase the superior safety and performance achieved by the proposed formulation, underscoring the critical significance of integrating terramechanics knowledge into the planning process. Specifically, the proposed terrain-adaptive formulation achieves reduced mean absolute sideslip angle, decreased mean absolute yaw rate, shorter time to goal, and a higher success rate, primarily attributed to its enhanced understanding of terramechanics within the planner.

A novel hierarchical global trajectory planner is presented to allow highly dynamical nonholonomic off-road autonomous vehicles to achieve high mobility on 3D terrains. On complex terrains with uneven topology, designing safe and feasible vehicle trajectories often demands an understanding of the vehicle's dynamical and nonholonomic constraints. Prior research, however, treats the global planning problem as a path planning problem without effectively accounting for topology or dynamical constraints. To address this gap, this paper presents a three-phase trajectory planning algorithm composed of an A*, a rapidly exploring random tree (RRT), and a local trajectory refining (LTR) phase to incorporate dynamical and nonholonomic constraints on uneven terrain. The algorithm is tested in scenarios with randomized terrain fields and obstacles to demonstrate the necessity for all three phases. The algorithm is shown to have lower cost, higher success rate, and higher computational efficiency compared to state-of-the-art methods. The algorithm is then tested by controlling a simulated MRZR vehicle on a 3D terrain along with a local controller, with comparisons to state-of-the-art algorithms. It is demonstrated that the new algorithm is capable of planning dynamically feasible trajectories with lower cost where the state-of-the-art algorithms fail to perform due to neglecting dynamical vehicle limitations.

In operations of multi-agent teams ranging from homogeneous robot swarms to heterogeneous human-autonomy teams, unexpected events might occur. While efficiency of operation for multi-agent task allocation problems is the primary objective, it is essential that the decision-making framework is intelligent enough to manage unexpected task load with limited resources. Otherwise, operation effectiveness would drastically plummet with overloaded agents facing unforeseen risks. In this work, we present a decision-making framework for multiagent teams to learn task allocation with the consideration of load management through decentralized reinforcement learning, where idling is encouraged and unnecessary resource usage is avoided. We illustrate the effect of load management on team performance and explore agent behaviors in example scenarios. Furthermore, a measure of agent importance in collaboration is developed to infer team resilience when facing handling potential overload situations.

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

The platooning of electrified vehicles can improve vehicle performance by enhancing energy efficiency and safety. Nevertheless, studies focusing on control strategies for platoons usually ignore or over-simplify the powertrain in the vehicle dynamics model. Advanced powertrain control strategies for electrified vehicles show that efficient operation of the powertrain depends on the current vehicle status, such as the torque demand, vehicle speed, and battery state of charge. In addition, the operation of individual powertrains can affect platoon-level performance, and the relationship between powertrain operation and vehicle status can be highly nonlinear. In studies of platoons, the evaluation of vehicle performance usually requires the vehicle to converge to a static operating point or to follow a simple operating pattern. Ideal cases can help in the understanding and improvement of vehicle performance but may be less beneficial for applications in realistic cases that are more complex, e.g. in military operations. In this study, a platooning optimization problem is formulated for optimal performance between two locations, given the terrain grade and soil properties along the path. A high-fidelity powertrain model is constructed and embedded in a detailed platoon model. The drive schedule is specifically designed for a platoon of vehicles with an innovative hybrid electric powertrain to achieve minimum total energy consumption. Results show that the approach is able to reduce energy consumption and headway keeping errors.

Study of traffic flow dynamics has a long tradition. However, predicting traffic jams before they occur is still a challenge. In this paper, we introduce recently developed tools of tipping point forecasting in complex systems, namely early warning indicators and bifurcation forecasting methods, and investigate their application to predict traffic jams on a circular road. The main advantage of the proposed methods is that they are model-free. The methods are based on exploiting the phenomenon of critical slowing down which occurs in dynamical systems near certain types of bifurcations, such as traffic jams. One can forecast the onset of traffic jams and the dynamics of the traffic after the bifurcation by using a few traffic measurements before the tipping point occurs. The measurements required for forecasting are recorded dynamical features of the system such as headways between cars in traffic, velocity or accelerations of each car. Forecasting approaches are applied to several simulated and experimental traffic flow conditions. Results show that one can successfully predict the onset of traffic jams and the traffic dynamics after this critical point using the proposed approaches while no model of the system is required.

The value proposition for ground vehicle modularity in the U.S. Army and other services has been a topic of continuing debate. Studies to date have largely focused on individual system elements such as manufacturing or maintenance, lacking a holistic perspective of the implications of modularity for the entire fleet operation and life-cycle. The U.S. Army Science and Technology community has demonstrated the technical feasibility of large-scale, transformative ground vehicle modularity, but the business case for modularity remains incomplete. There are multiple criteria tradeoffs between modular and mission-specific (conventional) vehicle platforms, such as total life-cycle cost, mission utility, personnel requirements, and fleet adaptability. This paper presents a system-of-systems framework to address these tradeoffs to support high-level decisions on the strategic feasibility of ground vehicle modularity. We demonstrate this framework with a notional example and an application to the Joint Tactical Transport System (JTTS), a U.S. Army Tank Automotive Research, Development and Engineering Center demonstrator program. Under certain modeling assumptions with regards to the operation of a modular fleet, results for the JTTS study indicate that modularity can lead to significant cost savings at the expense of increased personnel requirements.

Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict.Their dynamics might appear intractable.However, new statistical approaches-rooted in dynamical systems and the theory of stochastic processes-have yielded insight into the dynamics of emerging and re-emerging pathogens.We argue that these approaches may lead to new methods for predicting epidemics.This perspective views pathogen emergence and re-emergence as a "critical transition," and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition.Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals.Specifically, the motion of perturbations slows as the system approaches the transition. Anticipating epidemic transitionsOutbreaks of re-emerging pathogens are among the most unpredictable threats to public health and global security [1].In recent years, epidemics of measles, mumps, polio, whooping cough and other vaccine-preventable diseases have caused death and disease [2, 3], captured headlines and focused political attention, and prompted substantial investment in emergency planning and preparedness in both developed and developing countries.The causes of pathogen re-emergence (as well as the emergence of new pathogens) are variable and seemingly idiosyncratic [4].For this reason, predicting their eruption might seem intractable [5].Here we question this pessimism and suggest instead that data-driven methods based on the characteristic fluctuations of near-critical systems may provide model-independent measurements of the approach to disease criticality before a crisis occurs.The approach we propose anticipates disease re-emergence through a critical transition, i.e. when driving factors such as pathogen evolution, spatial movement, or (most central

Kinesins are nano-sized biological motors which walk by repeating a mechanochemical cycle. A single kinesin molecule is able to transport its cargo about 1 μm in the absence of external loads. However, kinesins perform much longer range transport in cells by working collectively. This long range of transport by a team of kinesins is surprising because the motion of the cargo in cells can be hindered by other particles. To reveal how the kinesins are able to accomplish their tasks of transport in harsh intracellular circumstances, stochastic studies on the kinesin motion are performed by considering the binding and unbinding of kinesins to microtubules and their dependence on the force acting on kinesin molecules. The unbinding probabilities corresponding to each mechanochemical state of kinesin are modeled. The statistical characterization of the instants and locations of binding are captured by computing the probability of unbound kinesin being at given locations. It is predicted that a group of kinesins has a more efficient transport than a single kinesin from the perspective of velocity and run length. Particularly, when large loads are applied, the leading kinesin remains bound to the microtubule for long time which increases the chances of the other kinesins to bind to the microtubule. To predict effects of this behavior of the leading kinesin under large loads on the collective transport, the motion of the cargo is studied when the cargo confronts obstacles. The result suggests that the behavior of kinesins under large loads prevents the early termination of the transport which can be caused by the interference with the static or moving obstacles.