Navigation of carlike robots in an extended dynamic environment with swarm avoidance

Abstract

Summary In this paper, we propose a new solution to the motion planning and control problem for a team of carlike mobile robots traversing in an extended dynamic environment. Motivated by the emerging necessity to avoid or defend against a swarm of autonomous robots, the wide array of obstacles in this dynamic environment for the first time includes a swarm of boids governed separately by a system of ordinary differential equations. The swarm exhibits collective emergent behaviors, whereas the carlike mobile robots safely navigate to designated targets. We present a set of nonlinear continuous controllers for obstacle, collision, and swarm avoidance. The controllers provide a collision‐free trajectory within a constrained workspace cluttered with various fixed and moving obstacles while satisfying the nonholonomic and kinodynamic constraints associated with the vehicular robotic system. An advantage of the proposed method is the ease in deriving the acceleration‐based control laws from the Lyapunov‐based control scheme. The effectiveness of the control laws is demonstrated via computer simulations. The novelty of this paper lies in the simplicity of the controllers and the ease in the treatment of an extended dynamic environment, which includes swarm avoidance.

Collision Avoidance of 3D Rectangular Planes by Multiple Cooperating Autonomous Agents

“…In addition, we only consider the wall closest to the point-mass robot en route to its target. Subsequently, our obstacle avoidance scheme is more straightforward contrasted with, for instance, the avoidance schemes used in the artificial potential strategies where all of the obstacles are considered in parallel [4,17]. (4) Stability analysis pertaining to the kinodynamic system.…”

“…Typically, MPC algorithms are applied to systems with fully fixed geometric and kinematic features, while the system design in robotics takes into account robustness, stiffness, workspace volume, obstacle avoidance schemes, and other performance features. e principle goal for any MPC problem is to find the most optimum design to optimise the motion between given configurations [2][3][4][5][6][7]. In an MPC problem, multiple robots are favoured as they are able to cooperate for faster and more efficient results [4,5,[8][9][10], including other fields where multiagent operations are always preferred [11].…”

“…Path planning or MPC algorithms for mobile robots operating in an environment cluttered with obstacles are usually grouped according to the methodologies used to generate the geometric path, namely, the road map techniques, cell decomposition algorithms, and artificial potential field (APF) methods [4,12]. ese path planning algorithms have a common objective, which is to find the shortest and most optimal geometric path taking into account the moving objects and obstacles in the workspace [13][14][15].…”

“…In this research article, we use the Lyapunov controllers, constructed via the Lyapunov-based Control Scheme (LbCS), essentially an APF method, for the control and stability of a system point-mass mobile robots that, in theory, can take on reasonably high velocities. e LbCS has been employed to warrant point and posture stabilities in the sense of Lyapunov for MPC for various robotic systems, such as car-like mobile robotic systems [4], mobile manipulators [16], tractor-trailer systems [12,17], and swarming [18]. We utilise the control scheme to derive and extract centralised velocity-based control laws for pointmass mobile robots.…”

Novel Lyapunov-Based Autonomous Controllers for Quadrotors

“…In our case, our controllers address the latter issue. Also, our approach is different from other motion planning and control schemes from literature which do not specifically take into account system constraints as components of the controllers [21], [32], [33].…”