Multi-robot Motion Planning with Nets-within-Nets Modeling and Simulation

Konsep Inti

Designing motion plans for heterogeneous robot teams using a novel High-Level robot team Petri Net system.

Abstrak

This paper introduces a framework for multi-robot motion planning using High-Level robot team Petri Nets. The robots cooperate to fulfill a global mission in an environment with regions of interest. The proposed model integrates the specification and robot models into the Nets-within-Nets paradigm. A Global Enabling Function ensures synchronization of transitions to prevent specification violations. Simulations are conducted to find solutions based on Linear Temporal Logic missions, showcasing the framework's effectiveness.

Statistik

Copyright may be transferred without notice after submission to IEEE.
Partially supported by CICYT-FEDER project PID2021-125514NB-I00 in Spain.
Execution time per simulation was 18 milliseconds on average with a standard deviation of 12 milliseconds.

Kutipan

"Introducing a general framework called the High-Level robot team Petri Net system (HLPN) for path planning." - Authors
"Incorporating scalability and adaptability properties in the proposed model to address flexible numbers of agents and spatial constraints." - Authors
"Designing a synchronization function (Global Enabling Function) between the nets in the model that verifies logical Boolean formulas." - Authors

Wawasan Utama Disaring Dari

Multi-robot Motion Planning based on Nets-within-Nets Modeling and Simulation

by Sofia Hustiu... pada arxiv.org 03-15-2024

https://arxiv.org/pdf/2304.08772.pdf

Pertanyaan yang Lebih Dalam

How does computational tractability scale as the number of robots increases?

As the number of robots in a system increases, the computational tractability can be impacted in several ways. One key factor is the exponential growth in complexity that occurs with an increase in the number of agents. The state space for planning and coordination expands rapidly, leading to higher computational demands. This exponential growth can make it challenging to find optimal solutions within a reasonable timeframe as more robots are added to the system.
Moreover, with a larger number of robots, there is an increase in interactions and dependencies among them. Coordinating these interactions becomes more complex, requiring sophisticated algorithms and efficient data structures to manage communication and synchronization effectively. As such, scalability issues may arise when trying to handle numerous agents simultaneously.
To address these challenges and improve computational tractability as the number of robots increases, researchers often explore parallel computing techniques or distributed algorithms that leverage multiple processing units concurrently. By distributing computation across different nodes or cores, tasks can be executed in parallel, reducing overall processing time and enhancing scalability.
Furthermore, optimizing algorithms for specific hardware architectures or utilizing cloud computing resources can also help improve performance and scalability when dealing with large numbers of robots. Overall, managing computational tractability with an increasing number of agents requires careful consideration of algorithm design, resource allocation strategies, and optimization techniques tailored to multi-robot systems.

Can time constraints be effectively encapsulated under high-level Petri nets formalism?

Time constraints can indeed be effectively encapsulated under high-level Petri nets formalism by incorporating temporal logic specifications into the modeling framework. High-level Petri nets provide a structured approach for representing complex systems where timing requirements play a crucial role in determining system behavior.
Temporal logic formulas such as Linear Temporal Logic (LTL) or Signal Temporal Logic (STL) can be integrated into Petri net models to capture temporal properties related to task deadlines, synchronization requirements between actions performed by different agents over time intervals or sequences.
By defining transitions based on temporal logic predicates that specify timing constraints (e.g., "eventually," "until," "always"), high-level Petri nets enable precise representation of temporal relationships within robotic systems. These transitions are labeled with logical formulas that dictate when they should fire based on specific timing conditions specified by the temporal logic expressions.
Additionally, tools like Renew offer support for simulating high-level Petri net models containing time-related elements through their graphical interfaces and simulation capabilities. This allows users to visualize how time constraints affect system behavior dynamically during simulations while ensuring correctness according to specified temporal properties encoded within the model.
In summary, leveraging high-level Petri nets along with temporal logic enables effective encapsulation of time constraints within robotic systems' modeling frameworks by providing a formalized structure for representing intricate timing requirements essential for coordinating multi-agent activities efficiently.

How can collaborative task assignments be integrated into the framework for robotic systems?

Collaborative task assignments play a vital role in maximizing efficiency and achieving collective goals within robotic systems operating as teams. Integrating collaborative task assignments into frameworks for robotic systems involves designing mechanisms that facilitate coordinated decision-making processes among multiple agents towards accomplishing shared objectives.
One approach is to incorporate task allocation algorithms that consider factors such as agent capabilities,
task requirements,
and communication costs
to assign tasks optimally among team members.
These algorithms utilize optimization techniques like combinatorial auctions,
market-based approaches,
or consensus protocols
to distribute tasks efficiently while balancing workload distribution across all robots.
Another strategy involves implementing coordination protocols based on negotiation strategies,
where agents communicate their preferences
and negotiate task allocations collaboratively.
This fosters adaptive assignment schemes where robot roles may change dynamically based on real-time conditions
and evolving mission priorities.
Furthermore,
the framework could include monitoring mechanisms using feedback loops
to track progress,
resolve conflicts,
and adaptively reassign tasks if needed during mission execution.
By integrating collaborative task assignments into robust frameworks supported by advanced planning tools like High-Level robot team Petri Nets (HLPN),
robust decision-making processes are enabled at both individual agent levels
as well as at higher levels governing team dynamics.
Overall,
this integration enhances overall system performance through optimized utilization of resources
while promoting synergy among autonomous entities working towards common objectives
in dynamic environments characterized by uncertainty
and changing operational conditions