Multi-Target Coordinated Search Algorithm for Swarm Robotics Considering Practical Constraints

In order to deal with the multi-target search problems for swarm robots in unknown complex environments, a multi-target coordinated search algorithm for swarm robots considering practical constraints is proposed in this paper. Firstly, according to the target detection situation of swarm robots, an ideal search algorithm framework combining the strategy of roaming search and coordinated search is established. Secondly, based on the framework of the multi-target search algorithm, a simplified virtual force model is combined, which effectively overcomes the real-time obstacle avoidance problem in the target search of swarm robots. Finally, in order to solve the distributed communication problem in the multi-target search of swarm robots, a distributed neighborhood communication mechanism based on a time-varying characteristic swarm with a restricted random line of sight is proposed, and which is combined with the multi-target search framework. For the swarm robot kinematics, obstacle avoidance, and communication constraints of swarm robots, the proposed multi-target search strategy is more stable, efficient, and practical than the previous methods. The effectiveness of this proposed method is verified by numerical simulations.

Grey Estimator-Based Tracking Controller Applied to Swarm Robot Formation

Mobile robots are widely used in many applications, while various types of mobile robots and their control researches have been proposed in literature. Since swarm robots have higher flexibility and capacity for teamwork, this paper presents a grey estimator-based tracking controller for formation trajectory tracking of swarm robots. First, wheel-type mobile robots are used and modeled for the controller design. Then, a grey dynamic estimator is developed to estimate the environmental disturbance and model uncertainty for linear feedback compensation. As a result, the asymptotic trajectory tracking is assured, so that the application on the swarm robot formation is achieved for a multi-agent system. The computational complexity is slightly reduced by the design. Finally, in order to verify the reliability of swarm robot formation, several types of formation are maintained by the grey estimator-based feedback linearization controller.

Secure and secret cooperation in robot swarms

The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as a Merkle tree. With this method, operators can provide the “blueprint” of the swarm’s mission without disclosing its raw data. In other words, data verification can be separated from data itself. We propose a system where robots in a swarm, to cooperate toward mission completion, have to “prove” their integrity to their peers by exchanging cryptographic proofs. We show the implications of this approach for two different swarm robotics missions: foraging and maze formation. In both missions, swarm robots were able to cooperate and carry out sequential tasks without having explicit knowledge about the mission’s high-level objectives. The results presented in this work demonstrate the feasibility of using Merkle trees as a cooperation mechanism for swarm robotics systems in both simulation and real-robot experiments, which has implications for future decentralized robotics applications where security plays a crucial role.

Swarm robots in science fiction

In both science and science fiction, robot swarms are out of control.

Generating Legible and Glanceable Swarm Robot Motion through Trajectory, Collective Behavior, and Pre-attentive Processing Features

As swarm robots begin to share the same space with people, it is critical to design legible swarm robot motion that clearly and rapidly communicates the intent of the robots to nearby users. To address this, we apply concepts from intent-expressive robotics, swarm intelligence, and vision science. Specifically, we leverage the trajectory, collective behavior, and density of swarm robots to generate motion that implicitly guides people’s attention toward the goal of the robots. Through online evaluations, we compared different types of intent-expressive motions both in terms of legibility as well as glanceability, a measure we introduce to gauge an observer’s ability to predict robots’ intent pre-attentively. The results show that the collective behavior-based motion has the best legibility performance overall, whereas, for glanceability, trajectory-based legible motion is most effective. These results suggest that the optimal solution may involve a combination of these legibility cues based on the scenario and the desired properties of the motion.