Sasanka Nagavalli

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Robotic swarms are distributed systems whose members interact via local control laws to achieve a variety of behaviors, such as flocking. In many practical applications, human operators may need to change the current behavior of a swarm from the goal that the swarm was going towards into a new goal due to dynamic changes in mission objectives. There are two(More)
In this paper, we present both centralized and distributed algorithms for aligning coordinate frames in multi-robot systems based on inter-robot relative position measurements. Robot orientations are not measured, but are computed by our algorithms. Our algorithms are robust to measurement error and are useful in applications where a group of robots need to(More)
Robotic swarms are distributed systems whose members interact via local control laws to achieve different behaviors. Practical missions may require a combination of different swarm behaviors, where these behavioral combinations are not known a priori but could arise dynamically due to changes in mission goals. Therefore, human interaction with the swarm(More)
This paper presents asynchronous distributed algorithms for information leader selection in multi-robot systems based on local communication between each robot and its direct neighbours in the system's communication graph. In particular, the information leaders refer to a small subset of robots that are near the boundary of the swarm and suffice to(More)
In many multi-robot applications such as target search, environmental monitoring and reconnaissance, the multi-robot system operates semi-autonomously, but under the supervision of a remote human who monitors task progress. In these applications, each robot collects a large amount of task-specific data that must be sent to the human periodically to keep the(More)
Robotic swarms are distributed systems that exhibit global behaviors arising from local interactions between individual robots. Each robot can be programmed with several local control laws that can be activated depending on an operator's choice of global swarm behavior. While some simple behaviors (e.g. rendezvous) with guaranteed performance on known(More)
The theory and design of effective interfaces for human interaction with multi-robot systems has recently gained significant interest. Robotic swarms are multi-robot systems where local interactions between robots and neighbors within their spatial neighborhood generate emergent collective behaviors. Most prior work has studied interfaces for human(More)
Robotic swarms are multi-robot systems whose global behaviors emerge from local interactions between individual robots. Each robot obeys a local control law that can be activated depending on an operator’s choice of global swarm behavior. Real missions occur in uncontrolled environments with dynamically arising objectives and require combinations of(More)
In this thesis, we consider the general problem of moving a large number of networked robots toward a goal position through a cluttered environment under constraints on network connectivity and collision avoidance. In contrast to previous approaches that either plan complete paths for each individual robot in the highdimensional joint configuration space or(More)
The capability of using cached plans, whether in the form of expert provided demonstrations or previously generated paths from prior planner runs, is a useful addition to any search based planner. Its benefits range from faster planning times in large state spaces to generation of more predictable replans in case of partially known environments which robots(More)