PG2P: a perception-guided path planning approach for long range autonomous navigation in unkown natural environments

Gancet, Jérémi; Lacroix, Simeon

doi:10.1109/iros.2003.1249326

Cited by 11 publications

(8 citation statements)

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“…These algorithms assume a simple contact sensor and simple rules to navigate through unknown environments. Kamon et al [52] Most similar to our work is the work by Gancet and Lacroix [31]. They present in their paper the Perception-guided path planning (PG2P) approach which is a hybrid sensing and path planning method.…”

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confidence: 75%

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Extending the Path-Planning Horizon

Nabbe¹,

Hebert

2007

The International Journal of Robotics Research

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SINCE typical mobile robotic vehicles have mobility sensors (such as LADAR or stereo) that can only acquire data up to a few tens of meters, a navigation system has no knowledge about the world beyond this sensing horizon. As a result, path planners that rely only on this knowledge to compute paths are unable to anticipate obstacles sufficiently early and has no choice than to plan inefficient paths that trace obstacle boundaries. To alleviate this problem, We present an opportunistic navigation and view planning strategy that incorporates look-ahead sensing of possible obstacle configurations. This planning strategy is based on a "what-if" analysis of hypothetical future configurations of the environment. Candidate vantage positions are evaluated based on their ability of observing anticipated obstacles. These vantage positions identified by this forward-simulation framework are used by the planner as intermediate waypoints.The validity of the strategy is supported by results from simulations as well as field experiments with a real robotic platform. These results also show that opportunistically significant reduction in path length can be achieved by using this framework. The reconstructed epipolar geometry from the very wide baseline100 A.9The ratio of the height over the width of the window on the left is 1.5456 and the window on the right 1.8356, which gives us an indication of the reconstruction quality. This effect is due to the fact that the planner is limited by the maximum range of the mobility sensors. Since typical mobility sensors, such as Laser Radar (LADAR) or passive stereo vision, will only acquire data up to a few tens of meters, the planner has no knowledge about what to encounter beyond the sensed perimeter. As a result, the planner is unable to anticipate obstacles sufficiently early and has no choice but to plan paths close to obstacle boundaries.For autonomous navigation over long distances, this issue degrades the performance of the system by greatly increasing the length of the path traveled by the vehicle. Consequently, the power consumed is increased and, more importantly, CHAPTER 1. INTRODUCTION FIGURE 1.1. Typical example of poor performance due to lack of sensor planning and mid-range sensing (Left: Overhead view of terrain; Right: Executed path with detected obstacles shown as shaded regions). The path from S1 to G1 intersects a large hill which is discovered only when the vehicle enters a large cul-de-sac, causing the executed path to be substantially more expensive than the path that would have been followed, had the obstruction been discovered earlier. (From [114]) the risk of exposure to threats is also increased. In addition, the relative short range of the mobility sensors forces the vehicle to drive closer to terrain obstructions than is safe or necessary.One solution is to use sensors with longer range to acquire information about the terrain further ahead. However, to use these sensors one needs to take into account certain constraints about the sensing geom...

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confidence: 75%

“…Using the COP function together with the label probability, a traversability cost is defined [31]. The result is that the terrain's traversability is reinforced by the confidence in this measurement.…”

mentioning

confidence: 99%

Extending the Path-Planning Horizon

Nabbe¹,

Hebert

2007

The International Journal of Robotics Research

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“…Other work seeks to combine navigation and view planning by hallucinating potential look-ahead sensor observations to extend the path planning horizon and achieve a reduction in path length [7]. Also related is work [8] that computes a 'confidence-of-perception' measure to decide where to next perform sensing, effectively tackling the view planning problem. Further related work explicitly considers visual servo performance during the planning process by augmenting the robot configuration with image-features to arrive at the so-called perceptual control manifold [9].…”

Section: Related Workmentioning

confidence: 99%

Humanoid navigation planning using future perceptive capability

Michel

Chestnutt

Kagami

et al. 2008

Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots

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We present an approach to navigation planning for humanoid robots that aims to ensure reliable execution by augmenting the planning process to reason about the robot's ability to successfully perceive its environment during operation. By efficiently simulating the robot's perception system during search, our planner generates a metric, the so-called perceptive capability, that quantifies the 'sensability' of the environment in each state given the task to be accomplished. We have applied our method to the problem of planning robust autonomous walking sequences as performed by an HRP-2 humanoid. A fast GPU-accelerated 3D tracker is used for perception, with a footstep planner incorporating reasoning about the robot's perceptive capability. When combined with a controller capable of adaptively adjusting the height of swing leg trajectories, HRP-2 is able to navigate around obstacles and climb stairs in dynamically changing environments. Reasoning about the future perceptive capability ensures that sensing remains operational throughout the walking sequence and yields higher task success rates than perception-unaware planning.

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“…Indeed, the decisions related to motion and perception must be taken jointly, as they are strongly interdependent: executing a motion requires a model of e environment beforehand, and to acquire some specific data, motion is often necessary to reach the adequate observation position. Our approach to jointly specify the sub-goal to reach and the perception task to apply exploits the graph defined by the probabilistic model depicted in section III-B, and a model of the perception process expressed in terms of information gain -details on this approach can be seen in [22].…”

Section: Long Range Traversesmentioning

confidence: 99%

Decisional autonomy of planetary rovers

Ingrand

Lacroix

Lemai-Chenevier

et al. 2007

Journal of Field Robotics

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To achieve the ever increasing demand for science return, planetary exploration rovers require more autonomy to successfully perform their missions. Indeed, the communication delays are such that teleoperation is unrealistic. Although the current rovers (such as MER) demonstrate a limited navigation autonomy, and mostly rely on ground mission planning, the next generation (e.g. NASA Mars Science Laboratory and ESA Exomars) will have to regularly achieve long range autonomous navigation tasks.However, fully autonomous long range navigation in partially known planetary-like terrains is still an open challenge for robotics. Navigating hundreds of meters without any human intervention requires the robot to be able to build adequate representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to control its motions and to localize itself as it moves.All these activities have to be planned, scheduled, and performed according to the rover context, and controlled so that the mission is correctly fulfilled. To achieve these objectives, we have developed a temporal planner and an execution controller, that exhibit plan repair and replanning capabilities. The planner is in charge of producing plans composed of actions for navigation, science activities (moving and operating instruments), communication with Earth and with an orbiter or a lander, while managing resources (power, memory, etc) and respecting temporal constraints (communication visibility windows, rendezvous, etc). High level actions also need to be refined and their execution temporally and logically controlled. Last, in such critical applications, we believe it is important to deploy a component which protects the system against dangerous or even fatal situations resulting from unexpected interactions between subsystems (e.g. move the robot while the robot arm is unstowed) and/or software components (e.g. take and store a picture in a buffer while the previous one is still being processed).In this article we review the aforementioned capabilities, that have been developed, tested and evaluated on board our rovers (Lama and Dala). After an overview of the architecture design principle adopted, we summarize the perception, localization and motion generation capabilities -required by autonomous navigation -and their integration and concurrent operation in a global architecture. We then detail the decisional components : a high level temporal planner which produces the robot activity plan on board, and temporal and procedural execution controllers. We show how some failures or execution delays are being taken care of with online local repair, or replanning.

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PG2P: a perception-guided path planning approach for long range autonomous navigation in unkown natural environments

Cited by 11 publications

References 11 publications

Extending the Path-Planning Horizon

Extending the Path-Planning Horizon

Humanoid navigation planning using future perceptive capability

Decisional autonomy of planetary rovers

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