Experimental Validation of High Speed Hazard Avoidance Control for Unmanned Ground Vehicles (original) (raw)

Hazard avoidance for high-speed mobile robots in rough terrain

Journal of Field Robotics, 2006

Unmanned ground vehicles have important applications in high speed rough terrain scenarios. In these scenarios, unexpected and dangerous situations can occur that require rapid hazard avoidance maneuvers. At high speeds, there is limited time to perform navigation and hazard avoidance calculations based on detailed vehicle and terrain models. This paper presents a method for high speed hazard avoidance based on the "trajectory space," which is a compact model-based representation of a robot's dynamic performance limits in rough, natural terrain. Simulation and experimental results on a small gasolinepowered unmanned ground vehicle demonstrate the method's effectiveness on sloped and rough terrain.

High-speed hazard avoidance for mobile robots in rough terrain

SPIE Proceedings, 2004

Mobile robots have important applications in high speed, rough-terrain scenarios. In these scenarios, unexpected and hazardous situations can occur that require rapid hazard avoidance maneuvers. At high speeds, there is limited time to perform re-planning based on detailed vehicle and terrain models. Furthermore, detailed models often do not accurately predict the robot's performance due to model parameter and sensor uncertainty. This paper presents a method for high speed hazard avoidance. The method is based on the concept of the trajectory space, which is a compact model-based representation of a robot's dynamic performance limits in uneven, natural terrain. A Monte Carlo method for analyzing system performance despite model parameter uncertainty is briefly presented, and its integration with the trajectory space is discussed. Simulation results for the hazard avoidance algorithm are presented and demonstrate the effectiveness of the method.

Potential Field Navigation of High Speed Unmanned Ground Vehicles on Uneven Terrain

2005

This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional "trajectory space" of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.

High-speed navigation of unmanned ground vehicles on uneven terrain using potential fields

Robotica, 2007

Many applications require unmanned ground vehicles (UGVs) to travel at high speeds on sloped, natural terrain. In this paper a potential field-based method is proposed for UGV navigation in such scenarios. In the proposed approach, a potential field is generated in the two-dimensional "trajectory space" of the UGV path curvature and longitudinal velocity. In contrast to traditional potential field methods, dynamic constraints and the effect of changing terrain conditions can be easily expressed in the proposed framework.

Autonomous terrain characterisation and modelling for dynamic control of unmanned vehicles

IEEE/RSJ International Conference on Intelligent Robots and System, 2002

An often-ignored aspect of unmanned cross-country vehicles is the dynamic response of the vehicle on dfferent terrain. We discuss techniques to predict the dynamic vehicle response to various natural obstacles. This method can then be used to adjust the vehicle dynamics to optimize pegormanee (e.g. speed) while ensuring that the vehicle is not damaged. This capability opens up a new area of obstacle negotiation for UGVs, where the vehicle moves over certain obstacles, rather than avoiding them, thereby resulting in more effective achievement of objectives. Robust obstacle negotiation and vehicle dynamics prediction requires several key technologies that will be discussed in this paper. We detect and segment (label) obstacles using a novel 3 0 obstacle algorithm. The material of each labelled obstacle (rock, vegetation, etc.) is then determined using a texture or color classification scheme. Terrain load-bearing surface models are then constructed using vertical springs to model the compressibility and traversability of each obstacle in front of the vehicle. The terrain model is then combined with the vehicle suspension model to yield an estimate of the maximum safe velocity, and predict the vehicle dynamics as the vehicle follows a path. This end-to-end obstacle negotiation system is envisioned to be usejsrl in optimized path planning and vehicle navigation in terrain conditions cluttered with vegetation, bushes, rocks, etc. Results on natural terrain with various natural materials are presented

Safe steering of UGVs in polygonal environments

2007 International Conference on Control, Automation and Systems, 2007

This paper presents an application of a model predictive control for trajectory generation of an unmanned ground vehicle. An optimal tracking problem while avoiding collision with obstacles is formulated in terms of cost minimization under constraints. The cost function includes terms corresponding to the deviation from the desired trajectory, magnitude of the control input, proximity to the obstacles and the final destination point, respectively. Information on obstacles can be incorporated online in the nonlinear model predictive framework and the resulting constrained optimization problem can be solved using nonlinear programming techniques such as augmented Lagrangian. Then kinematic constraints are treated by the Karush-Kuhn-Tucker (KKT) condition. This approach has been applied for generating safe trajectories for the nonlinear dynamics of a vehicle with a nonlinear tire model in a 2D polygonal environment. Simulation results show that the satisfactory performance was achieved in terms of short and safe trajectory satisfying input constraints.

Near-optimal navigation of high speed mobile robots on uneven terrain

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008

This paper proposes a method for near-optimal navigation of high speed mobile robots on uneven terrain. The method relies on a layered control strategy. A high-level planning layer generates an optimal desired trajectory through uneven terrain. A low-level navigation layer guides a robot along the desired trajectory via a potential field-based control algorithm. The high-level planner is guaranteed to yield optimal trajectories but is computationally intensive. The low-level navigation layer is sub-optimal but computationally efficient. To guard against failures at the navigation layer, a model-based lookahead approach is employed that utilizes a reduced form of the optimal trajectory generation algorithm. Simulation results show that the proposed method can successfully navigate a mobile robot over uneven terrain while avoiding hazards. A comparison of the method's performance to a similar algorithm is also presented.

Adaptive Motion Planning Based on Vehicle Characteristics and Regulations for Off-Road UGVs

IEEE Transactions on Industrial Informatics, 2018

In this paper, we propose a novel motion planning method for off-road unmanned ground vehicles, based on three-dimensional (3-D) terrain map information. Previous studies on the motion planning of a vehicle traveling on rough terrain dealt only with a relatively small environment. Furthermore, unique vehicle characteristics were not considered, and it was also impossible to incorporate regulations, such as maintaining driving speed and suppressing posture change. The proposed method enables vehicles to adaptively generate a path by considering vehicle characteristics and the regulations, in a large-scale environment, with rough terrain. A random sampling based scheme was applied to carrying out global path planning, based on a 3-D environmental model. Experimental results showed that the proposed off-road motion planner could generate an appropriate path, which satisfies vehicle characteristics and predefined regulations. Index Terms-Autonomous navigation, field robots, motion planning, unmanned ground vehicle (UGV). I. INTRODUCTION I N RECENT years, autonomous mobile robots and unmanned ground vehicles (UGVs) have attracted the attention of many researchers, and are becoming capable of dealing with various environments. Safe and reliable motion planning for mobility Manuscript

Methodology for the navigation optimization of a terrain-adaptive unmanned ground vehicle

International Journal of Advanced Robotic Systems, 2018

The goal of this article is to design a navigation algorithm to improve the capabilities of an all-terrain unmanned ground vehicle by optimizing its configuration (the angles between its legs and its body) for a given track profile function. The track profile function can be defined either by numerical equations or by points. The angles between the body and the legs can be varied in order to improve the adaptation to the ground profiles. A new dynamic model of an all-terrain vehicle for unstructured environments has been presented. The model is based on a half-vehicle and a quasi-static approach and relates the dynamic variables of interest for navigation with the topology of the mechanism. The algorithm has been created using a simple equation system. This is an advantage over other algorithms with more complex equations which need more time to be calculated. Additionally, it is possible to optimize to any ground-track-profile of any terrain. In order to prove the soundness of the ...

Trajectory Generation and Control Methodology for an Autonomous Ground Vehicle

AIAA Guidance, Navigation and Control Conference and Exhibit, 2008

The Overbot, originally designed to compete in the DARPA Grand Challenge, has been retasked as a testbed for autonomous vehicle research. A complete platform, the vehicle allows for development and validation of new control algorithms, actuators and sensors by allowing integration into an existing architecture. An overview of the vehicle is provided, describing the interaction of hardware and software. A new method is developed for manipulating spline-based paths in obstacle rich environments. The generated trajectories are forwarded to a new controller developed for the vehicle to follow the given paths. Various control techniques are tested on the vehicle and both the path planning and control are validated on a 600m offroad course. Nomenclature c Curvature dist(•, •) Euclidean distance f Trajectory curve i Integral k Gain P os Position W GP S Waypoint in GPS frame W P Initial waypoint