Decoupled multiagent path planning via incremental sequential convex programming

Abstract

Abstract-This paper presents a multiagent path planning algorithm based on sequential convex programming (SCP) that finds locally optimal trajectories. Previous work using SCP efficiently computes motion plans in convex spaces with no static obstacles. In many scenarios where the spaces are non-convex, previous SCP-based algorithms failed to find feasible solutions because the convex approximation of collision constraints leads to forming a sequence of infeasible optimization problems. This paper addresses this problem by tightening collision constraints incrementally, thus forming a sequence of more relaxed, feasible intermediate optimization problems. We show that the proposed algorithm increases the probability of finding feasible trajectories by 33% for teams of more than three vehicles in non-convex environments. Further, we show that decoupling the multiagent optimization problem to a number of single-agent optimization problems leads to significant improvement in computational tractability. We develop a decoupled implementation of the proposed algorithm, abbreviated dec-iSCP. We show that deciSCP runs 14% faster and finds feasible trajectories with higher probability than a decoupled implementation of previous SCP-based algorithms. The proposed algorithm is real-time implementable and is validated through hardware experiments on a team of quadrotors.

Distributed multi-robot formation control in dynamic environments

“…Our goal is to have realtime capability for online computation. Online sequential convex programming has been employed by Augugliaro et al (2012) and Chen et al (2015) to compute collision-free trajectories for multiple Micro Air Vehicles (MAVs), but without considering formations. The assignment of robots to the target positions in the formation is another optimization problem that was solved with a centralized algorithm by Turpin et al (2014) or with a distributed algorithm, albeit in environments without obstacles, by Montijano and Mosteo (2014) and Morgan et al (2016).…”

Survey of convex optimization for aerospace applications

“…Nevertheless, this problem can be efficiently solved by sequential convex programming (SCP) after the nonconvex constraints are approximated by convex ones [5]. To reduce the probability of infeasibility resulting from convex approximations of the collision-avoidance constraints, an incremental SCP method was proposed to tighten the constraints incrementally [6]. For multiple UAVs to travel in formation in environments with static or dynamic obstacles, a centralized algorithm based on SCP was proposed in Ref.…”

Measurable Augmented Reality for Prototyping Cyberphysical Systems: A Robotics Platform to Aid the Hardware Prototyping and Performance Testing of Algorithms

“…A multiagent sequential convex path-planning algorithm has also been implemented in MAR-CPS [33]. Planned paths for a team of two physical and two virtual quadrotors were visualized using the projection system, with the vehicles' states being displayed and updated in real time ( Figure 6).…”