Performance-based reactive navigation for non-holonomic mobile robots

Abstract

This paper presents an architecture for the navigation of an autonomous mobile robot evolving in environments with obstacles. Instead of addressing motion planning and control in different contexts, these issues are described in connected modules with performance requirement considerations. The planning problem is formulated as a constrained receding horizon planning problem and is solved in real time with an efficient computational method that combines nonlinear control theory, B-spline basis function and nonlinear programming. An integral sliding mode controller is used for trajectory tracking. Closed-loop stability of the tracking errors is guaranteed in spite of unknown disturbances. It is also shown that this strategy is particularly useful if integral sliding mode control is combined with other methods to further robustify against perturbations. The effectiveness, perfect performance of obstacle avoidance, real time and high robustness properties are demonstrated by experimental results.

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

“…However, it is useful for robust nonlinear MPC (see, e.g., [186]), and problems where only partial state information is available (see, e.g., [243]). Also, any approach that includes path following with bounded deviation (see, e.g., [60]) is somewhat equivalent to tube MPC.…”

“…In terms of vehicle navigation problems, examples of MPC that apply unicycle kinematics while having disturbance present have been proposed, see, e.g., [59,60,110,311]. Similar methods guarantee convergence using passivity-based arguments.…”

Integral sliding mode control of an extended Heisenberg system

“…It should be noted that since the system (4) is flat (see [36]), a collision-free optimal reference trajectory can be generated on-line (see for instance [37]). …”

Local path planning for mobile robots based on intermediate objectives

“…Due to those constraints, the algorithms mentioned above cannot be applied to this type of mobile robots, since these constraints might render many unfeasible spatial paths. Some algorithms have been proposed for this kind of robot [21,22,23], and the algorithm proposed in [24] describes the path planning problem as a nonlinear optimization problem with constraints, which guarantees the navigation of the robot in unknown environments. However, they either compute the path not in an optimal way, or simply represent the obstacles as circles, and there are at least two drawbacks for these algorithms: firstly only the circular obstacles are taken into account, and secondly local minima cannot be avoided when robots getting close to complex obstacles.…”