Enhanced locomotion control for a planetary rover

Peynot, Thierry; Lacroix, Simon

doi:10.1109/iros.2003.1250646

Cited by 17 publications

(11 citation statements)

References 8 publications

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“…radioed bearings), no single localization algorithm that uses on-board data can be robust enough to fulfill the various localization needs during long range navigation: a set of concurrent and complementary algorithms have to be developed and integrated for that purpose. Various developments have been integrated on board the robots Lama and Dala: 3D odometry [23], visual motion estimation [24]. More recently, we investigated visual SLAM approaches 3 , integrating stereovision and panoramic imaging [25].…”

Section: E Rover Localizationmentioning

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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Section: E Rover Localizationmentioning

confidence: 99%

Decisional autonomy of planetary rovers

Ingrand

Lacroix

Lemai-Chenevier

et al. 2007

Journal of Field Robotics

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“…The two locomotion modes that can be used on a rover like Lama are rolling (with an enhanced wheels speed command [11]), and peristaltism. On robots which have several reconfiguration abilities other locomotion modes may be exploited.…”

Section: B Locomotion Modesmentioning

confidence: 99%

“…This method is based on a locomotion monitoring process presented in [11]. It provides partial probabilities of being in one of the three following states: efficient locomotion, slipping situation, and locomotion fault (which means that the current locomotion of the rover is no more efficient at all).…”

Section: ) Locomotion Efficiency Monitoringmentioning

confidence: 99%

“…Three speeds coherence indicators are used as features in a probabilistic classification procedure: a Markov process evaluates the robot situation on-line according to the previous probabilities calculated, the current values of the features, and their comparison with prototypes recorded during a supervised learning stage. These speeds coherence indicators are obtained by comparing different evaluations and measurements of linear and rotation speeds on board the robot that should be equivalent in the absence of slippages (see [11] for more details). Fig.…”

Section: ) Locomotion Efficiency Monitoringmentioning

confidence: 99%

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A probabilistic framework to monitor a multi-mode outdoor robot

Peynot

Lacroix

2005

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents an approach to autonomously monitor the behavior of a robot endowed with several navigation and locomotion modes, adapted to the terrain to traverse. The mode selection process is done in two steps: the best suited mode is firstly selected on the basis of initial information or a qualitative map built on-line by the robot. Then, the motions of the robot are monitored by various processes that update mode transition probabilities in a Markov system. The paper focuses on this latter selection process: the overall approach is depicted, and preliminary experimental results are presented.

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“…Wheel actuation commands can be derived for a desired rover motion by means of an inverse kinematics model [5]. [6] and [7] included rover kinematics in the estimation of the wheel-ground contact angle and [8] developed a kinematic observer for articulated rovers. A kinematic model can provide valuable information because the ability of articulated rovers to adapt to uneven terrain makes it difficult to relate rover motion to wheel motion and requires the wheels to move at different speeds.…”

Section: Introductionmentioning

confidence: 99%

Characterization and Comparison of Rover Locomotion Performance Based on Kinematic Aspects

Thueer

Siegwart

Springer Tracts in Advanced Robotics

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Summary. Evaluation and comparison of locomotion performance of rovers is a difficult, though very important issue. The performance is influenced by a large number of parameters. In this work, three different rovers were analyzed from a kinematic point of view. Based on a kinematic model, the optimal velocities at the actual position were calculated for all wheels and used for characterization of the suspension of the different rovers. Simulation results show significant differences between the rovers and thus, the utility of the chosen metric. It is shown that a substantial reduction of slip can be achieved by integrating kinematics in a modelbased velocity controller.

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Enhanced locomotion control for a planetary rover

Cited by 17 publications

References 8 publications

Decisional autonomy of planetary rovers

Decisional autonomy of planetary rovers

A probabilistic framework to monitor a multi-mode outdoor robot

Characterization and Comparison of Rover Locomotion Performance Based on Kinematic Aspects

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