Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus

Abstract

Abstract-This paper presents a distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The robots are assumed to have a reduced communication and visibility radius and share information with their neighbors. Via distributed consensus the robots compute (a) the convex hull of the robot positions and (b) the largest convex region within free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation within this convex neighborhood of the robots. Reconfiguration is allowed, when required, by considering a set of target formations. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performs well in simulations with up to sixteen quadrotors. I. INTRODUCTIONMuti-robot teams can be employed for various tasks, such as surveillance, inspection, or automated factories. In these scenarios, robots may be required to navigate in formation, for example for maintaining a communication network, for collaboratively handling an object, for surveilling an area or to improve navigation.Multi-robot navigation in formation has received extensive attention in the past, with many works considering obstaclefree scenarios. In [1] we leveraged efficient optimization techniques, namely quadratic programming, semi-definite programming and (non-linear) sequential quadratic programming to devise a centralized method for local navigation in formation. These techniques provided good computational efficiency, local guarantees and generality, and were employed at different stages of the method for local motion planning in formation among static and dynamic obstacles, albeit centralized.In this work we combine the optimization concepts presented in [1] with distributed consensus and geometric reasoning to achieve similar results in a distributed schema, where robots are no more centrally controlled, but instead have a limited field of view, and communicate with their immediate neighbors.

Distributed multi-robot formation control in dynamic environments

“…Considering the extra routing control mechanisms that flooding would require make our solution a much better choice. Besides, the solution sending only the new constraints improves over our original approach in Alonso-Mora et al (2016), where on average for small teams the cost of our algorithm was much bigger.…”

“…This implies that, similarly to Algorithm 1, robots do not need to send all the constraints at each communication round, but only those that are new, and consequently more restrictive than in the previous round. In particular, robots send at each communication round the new linear constraints that have appeared after computing the intersection in Line 5 of Algorithm 3, instead of all the linear constraints at each round, as we originally considered in Alonso- Mora et al (2016). This modification leads to substantial communication savings in the Algorithm, specially when compared to a pure flooding approach.…”

Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems: Extended Abstract

“…Method for navigating a single formation using the leader-follower approach is shown in [18] and [22]. [23], [24] shows some promising results in aerial vehicle formation control. A slung payload transportation method is demonstrated in [25] where as [26] devised wheeled locomotion for payload carrying with modular robot.…”

Survey of convex optimization for aerospace applications

“…For multiple UAVs to travel in formation in environments with static or dynamic obstacles, a centralized algorithm based on SCP was proposed in Ref. [7] to compute an optimal formation, and a distributed version was also based on SCP [8]. In Ref.…”