39 research outputs found
Coordination of Multirobot Systems Under Temporal Constraints
Multirobot systems have great potential to change our lives by increasing efficiency or decreasing costs in many applications, ranging from warehouse logistics to construction. They can also replace humans in dangerous scenarios, for example in a nuclear disaster cleanup mission. However, teleoperating robots in these scenarios would severely limit their capabilities due to communication and reaction delays. Furthermore, ensuring that the overall behavior of the system is safe and correct for a large number of robots is challenging without a principled solution approach. Ideally, multirobot systems should be able to plan and execute autonomously. Moreover, these systems should be robust to certain external factors, such as failing robots and synchronization errors and be able to scale to large numbers, as the effectiveness of particular tasks might depend directly on these criteria. This thesis introduces methods to achieve safe and correct autonomous behavior for multirobot systems.
Firstly, we introduce a novel logic family, called counting logics, to describe the high-level behavior of multirobot systems. Counting logics capture constraints that arise naturally in many applications where the identity of the robot is not important for the task to be completed. We further introduce a notion of robust satisfaction to analyze the effects of synchronization errors on the overall behavior and provide complexity analysis for a fragment of this logic.
Secondly, we propose an optimization-based algorithm to generate a collection of robot paths to satisfy the specifications given in counting logics. We assume that the robots are perfectly synchronized and use a mixed-integer linear programming formulation to take advantage of the recent advances in this field. We show that this approach is complete under the perfect synchronization assumption. Furthermore, we propose alternative encodings that render more efficient solutions under certain conditions. We also provide numerical results that showcase the scalability of our approach, showing that it scales to hundreds of robots.
Thirdly, we relax the perfect synchronization assumption and show how to generate paths that are robust to bounded synchronization errors, without requiring run-time communication. However, the complexity of such an approach is shown to depend on the error bound, which might be limiting. To overcome this issue, we propose a hierarchical method whose complexity does not depend on this bound. We show that, under mild conditions, solutions generated by the hierarchical method can be executed safely, even if such a bound is not known.
Finally, we propose a distributed algorithm to execute multirobot paths while avoiding collisions and deadlocks that might occur due to synchronization errors. We recast this problem as a conflict resolution problem and characterize conditions under which existing solutions to the well-known drinking philosophers problem can be used to design control policies that prevents collisions and deadlocks. We further provide improvements to this naive approach to increase the amount of concurrency in the system. We demonstrate the effectiveness of our approach by comparing it to the naive approach and to the state-of-the-art.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162921/1/ysahin_1.pd
Intermittent Connectivity for Exploration in Communication-Constrained Multi-Agent Systems
Motivated by exploration of communication-constrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of information-consistency to handle situations where the plan is not initially known by all agents, and suggest an integer linear program for synthesizing information-consistent plans that also achieve auxiliary goals. Furthermore, inspired by network flow problems we propose a novel way to pose connectivity constraints that scales much better than previous methods. In the second part of the paper we apply these results in an exploration setting, and propose a clustering method that separates a large exploration problem into smaller problems that can be solved independently. We demonstrate how the resulting exploration algorithm is able to coordinate a team of ten agents to explore a large environment
Multi-Agent Robust Control Synthesis from Global Temporal Logic Tasks
This paper focuses on the heterogeneous multi-agent control problem under
global temporal logic tasks. We define a specification language, called
extended capacity temporal logic (ECaTL), to describe the required global
tasks, including the number of times that a local or coupled signal temporal
logic (STL) task needs to be satisfied and the synchronous requirements on task
satisfaction. The robustness measure for ECaTL is formally designed. In
particular, the robustness for synchronous tasks is evaluated from both the
temporal and spatial perspectives. Mixed-integer linear constraints are
designed to encode ECaTL specifications, and a two-step optimization framework
is further proposed to realize task-satisfied motion planning with high spatial
robustness and synchronicity. Simulations are conducted to demonstrate the
expressivity of ECaTL and the efficiency of the proposed control synthesis
approach.Comment: 7 pages, 3 figure
Robust Multi-Agent Coordination from CaTL+ Specifications
We consider the problem of controlling a heterogeneous multi-agent system
required to satisfy temporal logic requirements. Capability Temporal Logic
(CaTL) was recently proposed to formalize such specifications for deploying a
team of autonomous agents with different capabilities and cooperation
requirements. In this paper, we extend CaTL to a new logic CaTL+, which is more
expressive than CaTL and has semantics over a continuous workspace shared by
all agents. We define two novel robustness metrics for CaTL+: the traditional
robustness and the exponential robustness. The latter is sound, differentiable
almost everywhere and eliminates masking, which is one of the main limitations
of the traditional robustness metric. We formulate a control synthesis problem
to maximize CaTL+ robustness and propose a two-step optimization method to
solve this problem. Simulation results are included to illustrate the increased
expressivity of CaTL+ and the efficacy of the proposed control synthesis
approach.Comment: Submitted to ACC 202
Neural Network-based Control for Multi-Agent Systems from Spatio-Temporal Specifications
We propose a framework for solving control synthesis problems for multi-agent
networked systems required to satisfy spatio-temporal specifications. We use
Spatio-Temporal Reach and Escape Logic (STREL) as a specification language. For
this logic, we define smooth quantitative semantics, which captures the degree
of satisfaction of a formula by a multi-agent team. We use the novel
quantitative semantics to map control synthesis problems with STREL
specifications to optimization problems and propose a combination of heuristic
and gradient-based methods to solve such problems. As this method might not
meet the requirements of a real-time implementation, we develop a machine
learning technique that uses the results of the off-line optimizations to train
a neural network that gives the control inputs at current states. We illustrate
the effectiveness of the proposed framework by applying it to a model of a
robotic team required to satisfy a spatial-temporal specification under
communication constraints.Comment: 8 pages. Submitted to the CDC 202
Formal Synthesis of Controllers for Safety-Critical Autonomous Systems: Developments and Challenges
In recent years, formal methods have been extensively used in the design of
autonomous systems. By employing mathematically rigorous techniques, formal
methods can provide fully automated reasoning processes with provable safety
guarantees for complex dynamic systems with intricate interactions between
continuous dynamics and discrete logics. This paper provides a comprehensive
review of formal controller synthesis techniques for safety-critical autonomous
systems. Specifically, we categorize the formal control synthesis problem based
on diverse system models, encompassing deterministic, non-deterministic, and
stochastic, and various formal safety-critical specifications involving logic,
real-time, and real-valued domains. The review covers fundamental formal
control synthesis techniques, including abstraction-based approaches and
abstraction-free methods. We explore the integration of data-driven synthesis
approaches in formal control synthesis. Furthermore, we review formal
techniques tailored for multi-agent systems (MAS), with a specific focus on
various approaches to address the scalability challenges in large-scale
systems. Finally, we discuss some recent trends and highlight research
challenges in this area
A framework for simultaneous task allocation and planning under uncertainty
We present novel techniques for simultaneous task allocation and planning in multi-robot systems operating under uncertainty. By performing task allocation and planning simultaneously, allocations are informed by individual robot behaviour, creating more efficient team behaviour. We go beyond existing work by planning for task reallocation across the team given a model of partial task satisfaction under potential robot failures and uncertain action outcomes. We model the problem using Markov decision processes, with tasks encoded in co-safe linear temporal logic, and optimise for the expected number of tasks completed by the team. To avoid the inherent complexity of joint models, we propose an alternative model that simultaneously considers task allocation and planning, but in a sequential fashion. We then build a joint policy from the sequential policy obtained from our model, thus allowing for concurrent policy execution. Furthermore, to enable adaptation in the case of robot failures, we consider replanning from failure states and propose an approach to preemptively replan in an anytime fashion, replanning for more probable failure states first. Our method also allows us to quantify the performance of the team by providing an analysis of properties such as the expected number of completed tasks under concurrent policy execution. We implement and extensively evaluate our approach on a range of scenarios. We compare its performance to a state-of-the-art baseline in decoupled task allocation and planning: sequential single-item auctions. Our approach outperforms the baseline in terms of computation time and the number of times replanning is required on robot failure
Using Knowledge Awareness to improve Safety of Autonomous Driving
We present a method, which incorporates knowledge awareness into the symbolic
computation of discrete controllers for reactive cyber physical systems, to
improve decision making about the unknown operating environment under
uncertain/incomplete inputs. Assuming an abstract model of the system and the
environment, we translate the knowledge awareness of the operating context into
linear temporal logic formulas and incorporate them into the system
specifications to synthesize a controller. The knowledge base is built upon an
ontology model of the environment objects and behavioural rules, which includes
also symbolic models of partial input features. The resulting symbolic
controller support smoother, early reactions, which improves the security of
the system over existing approaches based on incremental symbolic perception. A
motion planning case study for an autonomous vehicle has been implemented to
validate the approach, and presented results show significant improvements with
respect to safety of state-of-the-art symbolic controllers for reactive
systems
Time Minimization and Online Synchronization for Multi-agent Systems under Collaborative Temporal Tasks
Multi-agent systems can be extremely efficient when solving a team-wide task
in a concurrent manner. However, without proper synchronization, the
correctness of the combined behavior is hard to guarantee, such as to follow a
specific ordering of sub-tasks or to perform a simultaneous collaboration. This
work addresses the minimum-time task planning problem for multi-agent systems
under complex global tasks stated as Linear Temporal Logic (LTL) formulas.
These tasks include the temporal and spatial requirements on both independent
local actions and direct sub-team collaborations. The proposed solution is an
anytime algorithm that combines the partial-ordering analysis of the underlying
task automaton for task decomposition, and the branch and bound (BnB) search
method for task assignment. Analyses of its soundness, completeness and
optimality as the minimal completion time are provided. It is also shown that a
feasible and near-optimal solution is quickly reached while the search
continues within the time budget. Furthermore, to handle fluctuations in task
duration and agent failures during online execution, an adaptation algorithm is
proposed to synchronize execution status and re-assign unfinished subtasks
dynamically to maintain correctness and optimality. Both algorithms are
validated rigorously over large-scale systems via numerical simulations and
hardware experiments, against several strong baselines.Comment: 17 pages, 14 figure