Pian Yu

Yihao Yin, Pian Yu, Andrea Turrini et al.

Robots operate under significant uncertainty, from quantifiable noise to unquantifiable unknowns, and must account for strict operational constraints, such as limited resources. In this paper, we consider the problem of synthesizing robust strategies to guide a robot's actions in fulfilling a given task, while ensuring the system never exhausts its resources. To solve this problem, we first model the robotic system as a Consumption Markov Decision Process with Set-valued Transitions(CMDPST), a unified framework modelling nondeterministic actions, quantifiable and unquantifiable uncertainty, and resource consumption. Then, we combine the CMDPST with the task specification, expressed as a Linear Temporal Logic over finite traces (LTLf ) formula. Lastly, we address the resource constrained optimal robust strategy synthesis problem, which aims to synthesize a strategy that maximizes the probability of satisfying the LTLf objective without resource exhaustion. Our solution involves two techniques: a direct unrolling-based method and a more efficient, optimized approach that leverages state-space pruning for better performance. Experiments on a warehouse transportation network show the effectiveness of the proposed solutions.

Can Zhou, Yulong Gao, Pian Yu

Synthesising autonomous agents that can navigate uncertain environments while adhering to complex temporal constraints remains a fundamental challenge. While Linear Temporal Logic (LTL) provides a rigorous language for specifying such tasks, the inherent undecidability of qualitatively verifying LTL satisfaction in partially observable Markov decision processes renders quantitative synthesis difficult, especially when designing reliable reward signals for approximate solvers. In this paper, we bridge this gap with a novel, sound reward-shaping mechanism that dynamically generates belief-dependent rewards grounded in certified LTL satisfaction. By integrating this mechanism into an enhanced Monte Carlo Planning framework, we empower agents to navigate the `fog' of partial observability with a search process focused on maximising verifiable success. Our experiments demonstrate that this approach not only thrives in scenarios where existing solvers fail but also maintains effectiveness and scalability across diverse benchmark domains.

Pian Yu, Dimos V. Dimarogonas

This paper investigates the online motion coordination problem for a group of mobile robots moving in a shared workspace, each of which is assigned a linear temporal logic specification. Based on the realistic assumptions that each robot is subject to both state and input constraints and can have only local view and local information, a fully distributed multi-robot motion coordination strategy is proposed. For each robot, the motion coordination strategy consists of three layers. An offline layer pre-computes the braking area for each region in the workspace, the controlled transition system, and a so-called potential function. An initialization layer outputs an initially safely satisfying trajectory. An online coordination layer resolves conflicts when one occurs. The online coordination layer is further decomposed into three steps. Firstly, a conflict detection algorithm is implemented, which detects conflicts with neighboring robots. Whenever conflicts are detected, a rule is designed to assign dynamically a planning order to each pair of neighboring robots. Finally, a sampling-based algorithm is designed to generate local collision-free trajectories for the robot which at the same time guarantees the feasibility of the specification. Safety is proven to be guaranteed for all robots at any time. The effectiveness and the computational tractability of the resulting solution is verified numerically by two case studies.

Pian Yu, Dimos V. Dimarogonas

This paper investigates the online motion coordination problem for a group of mobile robots moving in a shared workspace. Based on the realistic assumptions that each robot is subject to both velocity and input constraints and can have only local view and local information, a fully distributed multi-robot motion coordination strategy is proposed. Building on top of a cell decomposition, a conflict detection algorithm is presented first. Then, a rule is proposed to assign dynamically a planning order to each pair of neighboring robots, which is deadlock-free. Finally, a two-step motion planning process that combines fixed-path planning and trajectory planning is designed. The effectiveness of the resulting solution is verified by a simulation example.

Pian Yu, Dimos V. Dimarogonas

The problem of time-constrained multi-agent task scheduling and control synthesis is addressed. We assume the existence of a high level plan which consists of a sequence of cooperative tasks, each of which is associated with a deadline and several Quality-of-Service levels. By taking into account the reward and cost of satisfying each task, a novel scheduling problem is formulated and a path synthesis algorithm is proposed. Based on the obtained plan, a distributed hybrid control law is further designed for each agent. Under the condition that only a subset of the agents are aware of the high level plan, it is shown that the proposed controller guarantees the satisfaction of time constraints for each task. A simulation example is given to verify the theoretical results.