Abstract
In the study of collision-free navigation methods of multirobots, much attention has been paid to the constraints of external environment. However, most of the wheeled mobile robots are subjected to nonholonomic constraints. A collision between robots may occur if the nonholonomic constraints are neglected. This paper presents an improved approach to collision-free navigation for multi-nonholonomic robots. This approach combines the Optimal Reciprocal Collision Avoidance (ORCA) algorithm and Model Predictive Control (MPC) strategy. ORCA used a simple robot model, in which kinematics and dynamics are ignored. To cope with this problem, the MPC controller is introduced. In each ORCA step, the reference trajectory, reference control inputs, and “safe zones” are generated based on the new velocity. Consequently, the derived safe zone is transformed into the constraints of decision variables for a MPC controller. Finally, quadratic programming is used to solve the MPC problem by successive linearization of an error model of the mobile robot. Simulation results illustrate the effectiveness of the proposed method.
Highlights
Many classical approaches such as artificial potential field method [5], sampling-based algorithms [6,7,8,9], and dynamic window [10] are extended to dynamic environments [11]. ese approaches assume the observed obstacles to be static in a period of time [12,13,14] and compute an immediate action to avert collisions with the consideration of kinematics and dynamics in many cases
In order to test the performance of Discrete-Optimal Reciprocal Collision Avoidance (ORCA)-MPC, we carried on large-scale simulations to compare the mean computation times of our method with Priority-Based Noncooperative Distributed Model Predictive Control (PB-NC-DMPC) [44]
In the problem of collision avoidance of networked vehicles, PB-NC-DMPC showed better performance compared to NC-DMPC and Centralized MPC. e test scenario contains many robots whose goal is to move across a circle to the antipodal position. is scenario is the same as the published literature, Jur et al [31]
Summary
Motion planning/collision avoidance is concerned with computing a path or trajectory between two configurations embedded in cost field, while taking into account motion constraints, static obstacles, and moving obstacles [1]. e problem has been well studied for one robot avoiding static or moving obstacles [2,3,4].Many classical approaches such as artificial potential field method [5], sampling-based algorithms [6,7,8,9], and dynamic window [10] are extended to dynamic environments [11]. ese approaches assume the observed obstacles to be static in a period of time [12,13,14] and compute an immediate action to avert collisions with the consideration of kinematics and dynamics in many cases. E problem has been well studied for one robot avoiding static or moving obstacles [2,3,4] Many classical approaches such as artificial potential field method [5], sampling-based algorithms [6,7,8,9], and dynamic window [10] are extended to dynamic environments [11]. Many motionplanning methods of multiple point-mass robots have been developed [24,25,26] It means that applying these methods into practice directly is difficult because the mobility of the Mathematical Problems in Engineering robot is ignored. These methods relying on the communication between robots may increase the complexity of system
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