Importantly, as the number of collaborating individual increases, the detrimental impact of these secondary behaviors on the performance of the main task increase as well. For example, when increasing the number of people attempting to exit a building through a single door, the number of individuals actually passing through the door per unit of time reduces. Similarly, in densely-packed robot swarms operating in confined regions, spatial interference – which manifests itself as a competition for physical space – forces robots to spend more time navigating around each other rather than performing the primary task.

This aim of this work is to develop a decentralized algorithm that enables individual robots to decide whether to continue participating in their current task and contribute to the overall mission, or vacate the region so as to reduce the negative effects that interference has on the overall efficiency of the swarm.

As shown in the video, this decentralized voluntary retreat process was applied to a distributed collection task. Here, a team of robots collect and deposit objects from one set of locations (pick-up points) to another (drop-off) in the elliptical region. Robots do not communicate and collect only binary information regarding the presence of other robots around them to make the decision to stay or retreat. Given the optimal number of required robots, 5 in this case, individuals decide to retreat when they estimate a higher-than-optimal participation in the task. The algorithm is capable of distributedly adjusting and maintaining the desired number of robots, even when new robots are added at a steady rate.

The solution to this problem provides useful insights on how limited resources can be optimally exploited by a collaborating community of individuals. Practical tasks that can potentially benefit from similar approaches span from distribution of goods, environmental cleaning tasks, and transportation.

**Investigators:**

- Siddharth Mayya
- Pietro Pierpaoli
- Magnus Egerstedt

**Related Publications:**

Siddharth Mayya, Pietro Pierpaoli, Magnus Egerstedt, “Voluntary Retreat for Decentralized Interference Reduction in Robot Swarms”, Under Review, *IEEE International Conference on Robotics and Automation (ICRA) 2018.*

This project investigates how a team of mobile robots with qualitatively different sensing capabilities should be organized in order to effectively cover an area. Given that each of the sensing modalities measures a particular type of event or feature in the domain, in general, it is no longer true that a single density function effectively encodes the importance of a point for all the robots in the team. Instead, we consider a different density function for each of the sensing modalities present in the team, so that each robot can calculate the importance of a point in the domain by composing the density functions associated with each of its sensors.

A coverage control algorithm for teams with heterogeneous sensing capabilities has been published in the IEEE Robotics and Automation Letters and will be presented at the 2018 IEEE International Conference on Robotics and Automation (ICRA) in Brisbane, Australia. Below is the spotlight video for the ICRA presentation:

**Investigators:**

- María Santos
- Yancy Diaz-Mercado
- Magnus Egerstedt

**Related Publications:**

M. Santos, Y. Diaz-Mercado and M. Egerstedt, “Coverage Control for Multirobot Teams With Heterogeneous Sensing Capabilities,” in IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 919-925, April 2018.

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Paper Submitted to CDC 2017.

Download the video.

]]>Communication is not only essential for sharing sensor measurements and performing diagnostics, it is often an integral part of the closed loop control mechanism. In fact, in many applications and scenarios such as extra-terrestrial exploration, high precision manufacturing, and multi-robot testbeds, the robots frequently rely on communicating with a centralized decision maker for their velocity or position commands. In such situations, a failure in the communication network can severely hinder the motion of the robots and performance of the algorithm. This is the premise behind this project, whereby the adverse effects caused by intermittently failing communication networks are mitigated.

In order to calculate the safe time horizon, we first compute the set of all possible locations that can be reached by a robot within a given time (i.e., the reachable set). This is followed by computing the time horizon for which each robot lies outside the reachable set of other robots. But, for the differential-drive robots considered here, performing such set-membership tests is computationally expensive owing to the non-convexity of the reachable set. Consequently, the reachable set is over-approximated by enclosing it within an ellipse whose convex structure allows for simpler set-membership tests and finite representation. By minimizing the area of the ellipse enclosing the convex hull of the reachable set, we obtain the best ellipsoidal over-approximation of the reachable set in terms of the accuracy and effectiveness of set-membership tests.

Taking advantage of this ellipsoidal approximation, the safe time horizon is defined as the longest time duration for which the robot lies outside the ellipsoidal reachable sets of other robots. Thus, the robot experiencing communication failure can execute its last received velocity command for the corresponding safe time horizon and remain safe. Beyond this, the robot stops moving.

**Investigators:**

- Siddharth Mayya
- Magnus Egerstedt

**Related Publications:**

Siddharth Mayya and Magnus Egerstedt. “Safe open-loop strategies for handling intermittent communications in multi-robot systems.” *IEEE International Conference on* *Robotics and Automation (ICRA), 2017 *. IEEE, 2017.

- Zak Costello
- Yancy Diaz-Mercado
- Rowland O’Flaherty
- Daniel Pickem
- Thiagarajan Ramachandran