We present a novel mission-planning strategy for heterogeneous multi-robot teams, taking into account the specific constraints and capabilities of each robot. Our approach employs hierarchical trees to systematically break down complex missions into manageable sub-tasks. We develop specialized APIs and tools, which are utilized by Large Language Models (LLMs) to efficiently construct these hierarchical trees. Once the hierarchical tree is generated, it is further decomposed to create optimized schedules for each robot, ensuring adherence to their individual constraints and capabilities. We demonstrate the effectiveness of our framework through detailed examples covering a wide range of missions, showcasing its flexibility and scalability.

Autonomous agents operating in adversarial scenarios face a fundamental challenge: while they may know their adversaries' high-level objectives, such as reaching specific destinations within time constraints, the exact policies these adversaries will employ remain unknown. Traditional approaches address this challenge by treating the adversary's state as a partially observable element, leading to a formulation as a Partially Observable Markov Decision Process (POMDP). However, the induced belief-space dynamics in a POMDP require knowledge of the system's transition dynamics, which, in this case, depend on the adversary's unknown policy. Our key observation is that while an adversary's exact policy is unknown, their behavior is necessarily constrained by their mission objectives and the physical environment, allowing us to characterize the space of possible behaviors without assuming specific policies. In this paper, we develop Task-Aware Behavior Fields (TAB-Fields), a representation that captures adversary state distributions over time by computing the most unbiased probability distribution consistent with known constraints. We construct TAB-Fields by solving a constrained optimization problem that minimizes additional assumptions about adversary behavior beyond mission and environmental requirements. We integrate TAB-Fields with standard planning algorithms by introducing TAB-conditioned POMCP, an adaptation of Partially Observable Monte Carlo Planning. Through experiments in simulation with underwater robots and hardware implementations with ground robots, we demonstrate that our approach achieves superior performance compared to baselines that either assume specific adversary policies or neglect mission constraints altogether. Evaluation videos and code are available at https://tab-fields.github.io.

Application of Unmanned Aerial Vehicles (UAVs) in search and rescue, emergency management, and law enforcement has gained traction with the advent of low-cost platforms and sensor payloads. The emergence of hybrid neural and symbolic AI approaches for complex reasoning is expected to further push the boundaries of these applications with decreasing levels of human intervention. However, current UAV simulation environments lack semantic context suited to this hybrid approach. To address this gap, HAMERITT (Hybrid Ai Mission Environment for RapId Training and Testing) provides a simulation-based autonomy software framework that supports the training, testing and assurance of neuro-symbolic algorithms for autonomous maneuver and perception reasoning. HAMERITT includes scenario generation capabilities that offer mission-relevant contextual symbolic information in addition to raw sensor data. Scenarios include symbolic descriptions for entities of interest and their relations to scene elements, as well as spatial-temporal constraints in the form of time-bounded areas of interest with prior probabilities and restricted zones within those areas. HAMERITT also features support for training distinct algorithm threads for maneuver vs. perception within an end-to-end mission run. Future work includes improving scenario realism and scaling symbolic context generation through automated workflow.

Heterogeneous multi-robot system deployment offers a For example, consider Figure 1 where two unmanned ground variety of advantages including improved versatility, scalability, vehicles (UGVs) and one unmanned aerial vehicle (UAV) are and adaptability over homogeneous systems. As robotic exploring an environment and may discover tasks at undisclosed technology has advanced over the last few decades making locations. During disconnection, the UAV maintains robots smaller, more capable, and affordable, demand for a set of possible (belief) states for UGV 1 and UGV 2 and multi-robot research has grown. Appropriate coordination of also a set of (empathy) states that UGV 1 and UGV 2 might these heterogeneous systems can improve the effectiveness of believe about the UAV. The UAV finds a task that requires safety critical missions such as surveillance, exploration, and a UGV and plans to communicate with UGV 2. After the rescue operations by incorporating the capabilities of each UAV travels to UGV 2's first belief state, it finds that UGV robot. However, the complexity of the solution for a heterogeneous 2 is not present. So, the UAV reasons that UGV 2 might be system can exponentially expand over long periods at the second belief state, successfully communicates, and of disconnectivity, especially in uncertain environments.

The ability to efficiently plan and execute automated and precise search missions using unmanned aerial vehicles (UAVs) during emergency response situations is imperative. Precise navigation between obstacles and time-efficient searching of 3D structures and buildings are essential for locating survivors and people in need in emergency response missions. In this work we address this challenging problem by proposing a unified search planning framework that automates the process of UAV-based search planning in 3D environments. Specifically, we propose a novel search planning framework which enables automated planning and execution of collision-free search trajectories in 3D by taking into account low-level mission constrains (e.g., the UAV dynamical and sensing model), mission objectives (e.g., the mission execution time and the UAV energy efficiency) and user-defined mission specifications (e.g., the 3D structures to be searched and minimum detection probability constraints). The capabilities and performance of the proposed approach are demonstrated through extensive simulated 3D search scenarios.

When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task. Prior research into learning from demonstrations (LfD) has failed to capture this notion of the acceptability of a task's execution; meanwhile, temporal logics provide a flexible language for expressing task specifications. Inspired by this, we present Bayesian specification inference, a probabilistic model for inferring task specification as a temporal logic formula. We incorporate methods from probabilistic programming to define our priors, along with a domain-independent likelihood function to enable sampling-based inference. We demonstrate the efficacy of our model for inferring specifications, with over 90% similarity observed between the inferred specification and the ground truth, both within a synthetic domain and during a real-world table setting task.

On 1 March Roboteam announced the launch of its Artificial Intelligence Control Unit (AI-CU) software to provide autonomous and artificial intelligence capabilities to the warfighters. The AI-CU software will provide intuitive control and operation of unmanned systems and payloads according to the company. This will include unique features such as autonomous navigation, facial recognition and other artificial intelligence-enabled capabilities for operators of unmanned systems. The AI-CU allows operators to control numerous platforms and payloads simultaneously, based on multi-robot operator control unit (MOCU) compliant, open source software. Roboteam said that with the use of AI, an operator can control semi-autonomous platforms with voice commands while receiving real-time facial recognition data in the field.

Many have used Kalman filter techniques based on the equations of vehicle motion; these techniques most accurately predict shortterm motion. With intelligent path prediction, the long-term mission objective of the vehicle is being predicted in addition to the short-term motion. Thus, when applied to predicting the motion of a car, an intelligent predictor will attempt to predict the final destination--say, for example, the vehicle appears to be going to the post office or the art museum--in addition to predicting which streets will be used. The theory is also applicable to predicting air vehicle travel, so that for a military application, the target (from a set of plausible targets) and the threat-avoidance policy (from a set of plausible policies), in addition to the route, can be predicted. The first investigation is to develop a method for identifying a decisionmaking strategy that seemingly explains the vehicle's motion.

The problem of predicting the motion of a vehicle has been investigated by several researchers. Many have used Kalman filter techniques based on the equations of vehicle motion; these techniques most accurately predict shortterm motion. In contrast, my dissertation (Krozel 1992)1 presents a methodology for intelligent path prediction, where predicting the motion of an observed vehicle is performed by reasoning about the decision-making strategy of the vehicle's operator.