Path Planning for Vehicle-borne System Consisting of Multi Air–ground Robots

Abstract

SummaryThis paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of a ground mobile robot and two aerial flying robots. The ground mobile robot, usually unmanned ground vehicle (UGV), as a carrier, is able to deploy and harvest the aerial flying robots, and each aerial flying robot, usually unmanned aerial vehicles (UAVs), takes off from and lands on the carrier. At the same time, owing to the limited duration in the air in one flight, UAVs should return to the ground mobile robot timely for its energy-saving and recharge. This work is motivated by cooperative search and reconnaissance missions in the field of heterogeneous robot system. Especially, some targets with given positions are assumed to be visited by any of the UAVs. For the cooperative path planning problem, this paper establishes a mathematical model to solve the path of two UAVs and UGV. Many real constraints including the maximum speed of two UAVs and UGV, the minimum charging time of two UAVs, the maximum hovering time of UAVs, and the dynamic constraints among UAVs and UGV are considered. The objective function is constructed by minimizing the time for completing the whole mission. Finally, the path planning problem of the robot system is transformed into a multi-constrained optimization problem, and then the particle swarm optimization algorithm is used to obtain the path planning results. Simulations and comparisons verify the feasibility and effectiveness of the proposed method.

Research on vehicle obstacle avoidance path planning based on APF-PSO

“…Particle swarm optimization algorithm (PSO) has experienced continuous development since the particle swarm optimization algorithm was proposed in the 20th century. 33 Suppose the size of the particle swarm is n and the dimension of the search area is D. x i =( x i1 , x i2 ,…, x iD ) is the position of the particle i in the search area. v i =( v i1 , v i2 ,…, v iD ) is the velocity of the particle i. p i =( p i1 , p i2 ,…, p iD ) is the optimal position of the particle i in the search area.…”

Multiple Carrier-Vehicle Travelling Salesman Problem

“…The non-linearity in constraints ( 7)-(10) makes the formulation complex to solve with current solvers. However, similar to what is done in [15], it is possible to rewrite constraints ( 7)- (10) as second order cone constraints, where THE equivalent logical expression…”

“…More recently, [10] extends the CV-TSP and proposes the case of 2 vehicles and one carrier. This extension of the original problem is motivated by cooperative search and reconnaissance missions in heterogeneous robot systems.…”

“…In the described scenario, the small vehicle can choose indistinctly between both carriers to land and be serviced. Equivalently to [10], this variant always provides a faster mission time that the original case. It should be noticed that this complementary extension remains interesting as it includes a whole new group of additional applications.…”

An inchworm-inspired motion strategy for robotic swarms

“…However, these approaches are subjected to a variety of constraints on formation as well as trajectory following. Other approaches to swarm motion planning have, for example, investigated methods for area coverage and monitoring [35,36], and collision-free paths in real time [37].…”