This paper presents an iterative approach for heterogeneous multi-agent route planning in environments with unknown resource distributions. We focus on a team of robots with diverse capabilities tasked with executing missions specified using Capability Temporal Logic (CaTL), a formal framework built on Signal Temporal Logic to handle spatial, temporal, capability, and resource constraints. The key challenge arises from the uncertainty in the initial distribution and quantity of resources in the environment. To address this, we introduce an iterative algorithm that dynamically balances exploration and task fulfillment. Robots are guided to explore the environment, identifying resource locations and quantities while progressively refining their understanding of the resource landscape. At the same time, they aim to maximally satisfy the mission objectives based on the current information, adapting their strategies as new data is uncovered. This approach provides a robust solution for planning in dynamic, resource-constrained environments, enabling efficient coordination of heterogeneous teams even under conditions of uncertainty. Our method's effectiveness and performance are demonstrated through simulated case studies.

This paper introduces an iterative approach to multi-agent route planning under chance constraints. A heterogeneous team of agents with various capabilities is tasked with a Capability Temporal Logic (CaTL) mission, a fragment of Signal Temporal Logic. The agents’ motion is modeled as a finite weighted graph, where the weights represent travel durations. Given the probability distribution over the durations of each edge’s traversal, we want to find paths for all agents such that (a) the specification robustness is maximized, (b) travel time is minimized, and (c) the success probability is maximized. We tackle the problem using an iterative approach. In each stage, it selects edges’ traversal duration and success probabilities and then solves a multi-agent route planning problem. We use an efficient Mixed-Integer Linear Programming (MILP) encoding for the latter. Our method provides a framework for agents to make informed decisions in choosing the most suitable edge attributes (travel durations and success probabilities) that consider agents’ capabilities to perform tasks in the environment. The proposed iterative method leverages graph structure to generate a more efficient search space. The effectiveness of our method is demonstrated through simulated case studies where obtaining the optimal solution would otherwise be computationally expensive. Our approach efficiently explores the solution space, generating better solutions and improving the performance of multi-agent route planning with uncertain travel durations.

This work presents an optimization-based control synthesis approach for an extension of Signal Temporal Logic (STL) called weighted Signal Temporal Logic (wSTL). wSTL was proposed to accommodate user preferences for importance and priorities over concurrent and sequential tasks as well as satisfaction times denoted by weights over the logical and temporal operators, respectively. We propose a Mixed Integer Linear Programming (MILP) based approach for synthesis with wSTL specifications. These specifications have the same qualitative semantics as STL but differ in their quantitative semantics, which is recursively modulated with weights. Additionally, we extend the formal definition of wSTL to include the semantics for until and release temporal operators and present an efficient encoding for these operators in the MILP formulation. As opposed to the original implementation of wSTL, where the arithmetic-geometric mean robustness was used with gradient-based methods prone to local optima, our encoding allows the use of a weighted version of traditional robustness and efficient global MILP solvers. We demonstrate the operational performance of the proposed formulation using multiple case studies, showcasing the distinct functionalities over Boolean and temporal operators. Moreover, we elaborate on multiple case studies for synthesizing controllers for an agent navigating a non-convex environment under different constraints highlighting the difference in synthesized control plans for STL and wSTL. Finally, we compare the time and complexity performance of encodings for STL and wSTL.

This paper presents an agent-agnostic framework to control swarms of robots tasked with temporal and logical missions expressed as Metric Temporal Logic (MTL) formulas. We consider agents that can receive global commands from a high-level planner, but no inter-agent communication. Moreover, agents are grouped into sub-swarms whose number can vary over the mission time horizon due to splitting and merging. However, a strict upper bound on the maximum number of sub-swarms is imposed to ensure their safe operation in the environment. We propose a two-phase approach. In the first phase, we compute the trajectories of the sub-swarms, splitting, and merging actions using a Mixed Integer Linear Programming approach that ensures the satisfaction of the MTL specification with minimal swarm division over the mission time horizon. Moreover, it enforces the upper bound on the number of sub-swarms. In the second phase, splitting fractions for sub-swarms resulting from splitting actions are computed. A distributed randomized protocol with no interagent communication ensures agent assignments matching the splitting fractions. Finally, we show the operation and performance of the approach in simulations with multiple tasks that require swarm splitting or merging.

We tackle the problem of multiple quadrotors transporting a cable-suspended point-mass load. The quadrotors are treated as a virtual leader-follower algorithm, where a multi-layer graph encapsulates the communication and physical interaction. On the one hand, the communication stands for the approach of following the reference trajectory of a virtual leader. On the other hand, the load exerts a distributed tension force on each cable which is modeled as the well-known spring-damping system to each quadrotor establishing an interconnected dynamic. We assume cables are stretchable and have neglectable mass. Both objectives are accomplished through a Model Reference Adaptive Control approach with a robust modification that treats uncertainties and perturbations given by error in parameters, noise in the signal, and the wind drag forces. We prove stability based on Lyapunov approach and the results are shown through simulation.

Transporting objects using quadrotors with cables has been widely studied in the literature. However, most of those approaches assume that the cables are previously attached to the load by human intervention. In tasks where multiple objects need to be moved, the efficiency of the robotic system is constrained by the requirement of manual labor. Our approach uses a non-stretchable cable connected to two quadrotors, which we call the catenary robot, that fully automates the transportation task. Using the cable, we can roll and drag the cuboid object (box) on planar surfaces. Depending on the surface type, we choose the proper action, dragging for low friction, and rolling for high friction. Therefore, the transportation process does not require any human intervention as we use the cable to interact with the box without requiring fastening. We validate our control design in simulation and with actual robots, where we show them rolling and dragging boxes to track desired trajectories.

We develop an event-triggered control strategy for a weighted-unbalanced directed homogeneous robot network to reach a dynamic consensus in this work. We present some guarantees for synchronizing a robot network when all robots have access to the reference and when a limited number of robots have access. The proposed event-triggered control can reduce and avoid the periodic updating of the signals. Unlike some current control methods, we prove stability by making use of a logarithmic norm, which extends the possibilities of the control law to be applied to a wide range of directed graphs, in contrast to other works where the event-triggered control can be only implemented over strongly connected and weight-balanced digraphs. We test the performance of our algorithm by carrying out experiments both in simulation and in a real team of robots.