Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments

Abstract: We present controllers that enable mobile robots to persistently monitor or sweep a changing environment. The changing environment is modeled as a field which grows in locations that are not within range of a robot, and decreases in locations that are within range of a robot. We assume that the robots travel on given closed paths. The speed of each robot along its path is controlled to prevent the field from growing unbounded at any location. We consider the space of speed controllers that can be parametrized by a finite set of basis functions. For a single robot, we develop a linear program that is guaranteed to compute a speed controller in this space to keep the field bounded, if such a controller exists. Another linear program is then derived whose solution is the speed controller that minimizes the maximum field value over the environment. We extend our linear program formulation to develop a multi-robot controller that keeps the field bounded. The multi-robot controller has the unique feature that it does not require communication among the robots. Simulation studies demonstrate the robustness of the controllers to modeling errors, and to stochasticity in the environment.

• The paper presents a framework and methods for designing robot speed controllers that enable persistent monitoring and sweeping tasks in dynamic environments.
• The approach employs linear programming for controller synthesis in both single and multi-robot scenarios, notably allowing multi-robot control without inter-robot communication.
• Simulation studies demonstrate the practical applicability and resilience of the proposed speed controllers, showing their effectiveness in various dynamic conditions.

Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments

The paper "Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments" presents a framework for designing speed controllers to enable mobile robots to perform continuous monitoring or sweeping tasks in dynamic environments. The environments are modeled as time-varying fields where the data or material accumulation increases at locations not serviced by a robot and decreases at those that are. This model is relevant to applications like environmental monitoring, cleaning, or surveillance where persistent operation is essential due to the ongoing dynamism of the environment.

The authors specifically address the challenge of robots constrained to move on pre-specified closed paths. Their work facilitates controlling the speed of these robots to maintain or reduce the accumulation field across the environment. They explore both single and multi-robot scenarios within this context, and importantly, the multi-robot speed controllers operate without requiring communication between robots.

Key Contributions and Methodology

1. Modeling and Control Problem Definition: The authors formally introduce the concept of persistent tasks and propose a mathematical model for these in terms of field accumulation. They provide necessary conditions for the existence of a controller that can stabilize the field accumulation for a given set of robots and paths.
2. Single Robot Controller Synthesis: They present an approach using linear programming to compute a speed controller for a single robot. The goal is to ensure the field does not grow unbounded, essentially keeping the accumulation below a given threshold. If feasible, the LP formulation also allows for determining an optimal speed controller which minimizes the maximum steady-state field value.
3. Multi-Robot Controller Synthesis: For multiple robots, the approach essentially extends the single-robot formulation. They present a linear program that achieves a field-stabilizing controller without requiring inter-robot communications. This aspect is particularly significant in real-world deployment where communication may be unreliable or undesirable.
4. Robustness to Errors: The paper also highlights the robustness of these controllers against modeling errors and uncertainties like errors in field production rate estimation, stochastic variations, and even tracking imperfections in the robot's motion.
5. Simulation Studies: Through comprehensive simulation studies, the authors demonstrate the real-world applicability of their methods and the resilience of the controllers under varying conditions. These studies underscore the effectiveness of the proposed control algorithms and validate the theoretical models.

Implications and Future Directions

The work effectively bridges theoretical insights with practical demands of persistent tasks in robotic applications. It underscores the importance of path-speed decoupling as a viable strategy for trajectory planning in complex environments. The methodology provides a way forward for deploying autonomous systems in persistent monitoring and sweeping applications without continuous human intervention or full autonomy.

The paper opens several future research avenues. These include extending the approach to handle unknown or varying field production rates through adaptive control techniques, considering full trajectory planning instead of fixed paths, and exploring distributed approaches that allow robots to dynamically negotiate path coverage.

In summary, this research provides a structured mathematical framework and a practical solution approach to a class of problems that are frequently encountered in the deployment of autonomous robotic systems in dynamic environments. The insights and solutions proposed could have meaningful impacts on the areas of environmental monitoring, resource management, and autonomous surveillance.