An obstacle avoidance model predictive control scheme: A sum-of-squares approach (original) (raw)
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An obstacle avoidance receding horizon control scheme for autonomous vehicles
2013 American Control Conference, 2013
The paper addresses the obstacle avoidance motion planning problem for ground vehicles operating in uncertain environments. By resorting to set-theoretic ideas, a receding horizon control algorithm is proposed for robots modelled by linear time-invariant (LTI) systems subject to input and state constraints and disturbance effects. Sequences of inner ellipsoidal approximations of the exact one-step controllable sets are pre-computed for all the possible obstacle scenarios and then on-line exploited to determine the more adequate control action to be applied to the robot in a receding horizon fashion. The resulting framework guarantees Uniformly Ultimate Boundedness and constraints fulfilment regardless of any obstacle scenario occurrence.
Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths
Springer Tracts in Advanced Robotics, 2010
As mobile robots venture into more difficult environments, more complex state-space paths are required to move safely and efficiently. The difference between mission success and failure can be determined by a mobile robots capacity to effectively navigate such paths in the presence of disturbances. This paper describes a technique for mobile robot model predictive control that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved solution. The contribution, a receding horizon model-predictive control (RHMPC) technique, specifically addresses the problem of path following and obstacle avoidance through geometric singularities and discontinuities such as cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to relax parameterized control inputs initialized from a regional motion planner to navigate safely through the environment. Experimental results are presented for a six-wheeled skid-steered field robot in natural terrain.
Model Predictive Control for path tracking and obstacle avoidance of autonomous vehicle
The concept of autonomous vehicles has been widely explored lately by, among others, automotive companies as a way to for example improve fuel efficiency or to gain access to environments which pose a danger to human operators. Model Predictive Control (MPC) has traditionally been used to control systems with slower dynamics but with the emergence of more powerful computers it is now being used in systems with considerably faster dynamics as well. One of the main strengths of MPC is its ability to handle constraints which are present in all physical systems. The aim of this thesis was to develop a single layer linear controller for path tracking and obstacle avoidance of an autonomous car. Its ability to minimize the deviations to the reference path while clearing static obstacles was evaluated. Focus was placed on the tracking problem hence no trajectory planning system was implemented. Instead a predefined path was used. Simulations were developed in MATLAB based on the kinematic ...
2014
This thesis is concerned with trajectory generation for robots in dynamic environments with relatively narrow passages. In particular, this thesis aims at developing motion planning schemes using receding horizon control (RHC) and mixed-integer linear programming (MILP). The thesis is constructed of two phases. In the first phase, a general nonlinear RHC framework is developed utilizing existing algorithms for motion planning of a robot with uncertain states. In this phase, the motion planning problem in the presence of arbitrary shaped obstacles is tackled using nonlinear system and measurement equations. This method is then adopted to solve the problem
Nonlinear Model Predictive Path Following Controller with Obstacle Avoidance
Journal of Intelligent and Robotic Systems, 2021
In the control systems community, path-following refers to the problem of tracking an output reference curve. This work presents a novel model predictive path-following control formulation for nonlinear systems with constraints, extended with an obstacle avoidance strategy. The method proposed in this work simultaneously provides an optimizing solution for both, path-following and obstacle avoidance tasks in a single optimization problem, using Nonlinear Model Predictive Control (NMPC). The main idea consists in extending the existing NMPC controllers by the introduction of an additional auxiliary trajectory that maintains the feasibility of the successive optimization problems even when the reference curve is unfeasible, possibly discontinuous, relaxing assumptions required in previous works. The obstacle avoidance is fulfilled by introducing additional terms in the value functional, rather than imposing state space constraints, with the aim of maintaining the convexity of the state and output spaces. Simulations results considering an autonomous vehicle subject to input and state constraints are carried out to illustrate the performance of the proposed control strategy.
Computationally aware control of autonomous vehicles: a hybrid model predictive control approach
Model predictive control (MPC) is a common approach to the control of trajectory-following systems. For nonlinear plants such as car-like robots, methods for path planning and following have the advantage of concurrently solving problems of obstacle avoidance, feasible trajectory selection, and trajectory following. A prediction function for the plant is used to simulate the trajectory with a candidate stream of inputs. Constraints on control inputs and state values, used to ensure safe trajectories and to avoid obstacles, are encoded into a cost function, and optimization routines (at runtime) compute the trajectories and their corresponding control inputs. Such approaches are computationally intensive, and in the nonlinear case the computational burden generally grows as a predictive model more closely approximates a nonlinear plant. In situations where system safety is paramount, guaranteeing model accuracy (in order to achieve more accurate behavior) comes at the cost of increased computation time, which results in increased travel time without a new solution. While the computational burden of predictive methods can be addressed through model reduction, the cost of modeling error over the prediction horizon is high and can lead to unfeasible results. In this paper, we consider the problem of controlling a ground vehi-B Kun Zhang
2021
This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical s...
Model-predictive active steering and obstacle avoidance for autonomous ground vehicles
Control Engineering Practice, 2009
This paper presents a model-predictive approach for trajectory generation of unmanned ground vehicles (UGVs) combined with a tire model. An optimal tracking problem while avoiding collision with obstacles is formulated in terms of cost minimization under constraints. Information on obstacles is incorporated online in the nonlinear model-predictive framework as they are sensed within a limited sensing range. The overall problem is solved online with nonlinear programming. For the local path regeneration upon detecting new obstacles, the cost function is augmented using the obstacle information in two methods. The first method uses the distance from the UGV to the nearest detected obstacle, and the second method uses the parallax information from the vehicle about the detected obstacles. Simulation results in cluttered and dynamic environments show that the modified parallax method effectively reflects the threat of the obstacles to the UGV considering the dimension and state variables of the vehicle, showing clear improvements over the distance-based methods.
Towards Autonomy in Unmanned Vehicles Using Receding Horizon Strategies
Mecánica Computacional, 2019
In this article we propose to use receding horizon strategies, like model predictive control (MPC) and moving horizon estimation (MHE), to design guidance, navigation and path-planning tasks, which play an essential role in autonomy of unmanned vehicles. As we propose to design these tasks using MPC and MHE, the physical and dynamical constraints can be included at the design stage, thus leading to optimal and feasible results. In order to evaluate the performance of the proposed framework, we have used Gazebo simulator in order to drive a Jackal unmanned ground vehicle (UGV) along a desired path computed by the path-planning module. The results we have obtained are successful as the estimation and guidance errors are small and the Jackal UGV is able to follow the desired path satisfactorily and it is also capable to avoid the obstacles which are in its way.
A Receding Horizon Framework for Autonomy in Unmanned Vehicles
ArXiv, 2019
In this article we present a unified framework based on receding horizon techniques that can be used to design the three tasks (guidance, navigation and path-planning) which are involved in the autonomy of unmanned vehicles. This tasks are solved using model predictive control and moving horizon estimation techniques, which allows us to include physical and dynamical constraints at the design stage, thus leading to optimal and feasible results. In order to demonstrate the capabilities of the proposed framework, we have used Gazebo simulator in order to drive a Jackal unmanned ground vehicle (UGV) along a desired path computed by the path-planning task. The results we have obtained are successful as the estimation and guidance errors are small and the Jackal UGV is able to follow the desired path satisfactorily and it is also capable to avoid the obstacles which are in its way.