Stochastic behavior of robots that navigate by interacting with their environment

Stager, Adam; Tanner, Herbert G.

doi:10.1109/cdc.2016.7799327

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…We utilize a low-level switching controller to ensure that the robot follows the trajectory and reaches the desired waypoint. At each instance when the sliding local map is updated, the controller switches between the following strategies: free-space, boundary following [29], [33], and flow-through [27] as described in Algorithm 3. From the switching strategy, the robot will determine whether the Bspline trajectory is suitable or it needs to generate a new reference trajectory as illustrated in Fig.…”

Section: B Maneuver Selectionmentioning

confidence: 99%

Motion Planning for Collision-resilient Mobile Robots in Obstacle-cluttered Unknown Environments with Risk Reward Trade-offs

Liu

Correa

et al. 2020

Preprint

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Collision avoidance in unknown obstacle-cluttered environments may not always be feasible. This paper focuses on an emerging paradigm shift in which potential collisions with the environment can be harnessed instead of being avoided altogether. To this end, we introduce a new sampling-based online planning algorithm that can explicitly handle the risk of colliding with the environment and can switch between collision avoidance and collision exploitation. Central to the planner's capabilities is a novel joint optimization function that evaluates the effect of possible collisions using a reflection model. This way, the planner can make deliberate decisions to collide with the environment if such collision is expected to help the robot make progress toward its goal. To make the algorithm online, we present a state expansion pruning technique that significantly reduces the search space while ensuring completeness. The proposed algorithm is evaluated experimentally with a built-in-house holonomic wheeled robot that can withstand collisions. We perform an extensive parametric study to investigate trade-offs between (user-tuned) levels of risk, deliberate collision decision making, and trajectory statistics such as time to reach the goal and path length.

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Section: B Maneuver Selectionmentioning

confidence: 99%

Motion Planning for Collision-resilient Mobile Robots in Obstacle-cluttered Unknown Environments with Risk Reward Trade-offs

Liu

Correa

et al. 2020

Preprint

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“…Physical interaction between a robot and its environment has been demonstrated to be potentially advantageous for mission completion ( Karydis et al, 2014 ; Stager and Tanner, 2020 ; Stager and Tanner, 2016 ; Stager and Tanner, 2019 ). In addition, some types of physical interaction between robots can significantly change the dynamics of the agents involved (e.g., when an aerial robot lifts a wheeled robot, the aerial robot’s inertial characteristics change and contact constraints for the ground robot are now lifted) ( Mellinger et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Cooperative planning for physically interacting heterogeneous robots

Sebok,

Tanner

2024

Front. Robot. AI

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Heterogeneous multi-agent systems can be deployed to complete a variety of tasks, including some that are impossible using a single generic modality. This paper introduces an approach to solving the problem of cooperative behavior planning in small heterogeneous robot teams where members can both function independently as well as physically interact with each other in ways that give rise to additional functionality. This approach enables, for the first time, the cooperative completion of tasks that are infeasible when using any single modality from those agents comprising the team.

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“…• We extend the DRR strategy to generate local trajectories when colliding with (non-)convex obstacles. The first iteration of the robot, inspired by the omnipuck robot [19], featured a passive collision ring and a singleboard computer for motion control [1], [2]. This iteration has a radius of 0.12 m and weighs 0.6 kg.…”

Section: Introductionmentioning

confidence: 99%

“…Fig.1: The evolution of our omni-directional holonomic robot prototypes used in our collision-inclusive motion planning and control research program. (a) The first iteration of the robot, inspired by the omnipuck robot[19], featured a passive collision ring and a singleboard computer for motion control[1],[2]. This iteration has a radius of 0.12 m and weighs 0.6 kg.…”

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confidence: 99%

Online Search-based Collision-inclusive Motion Planning and Control for Impact-resilient Mobile Robots

Lu¹,

Liu²,

Campbell³

et al. 2022

Preprint

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This paper focuses on the emerging paradigm shift of collision-inclusive motion planning and control for impactresilient mobile robots, and develops a unified hierarchical framework for navigation in unknown and partially-observable cluttered spaces. At the lower-level, we develop a deformation recovery control and trajectory replanning strategy that handles collisions that may occur at run-time, locally. The low-level system actively detects collisions (via embedded Hall effect sensors on a mobile robot built in-house), enables the robot to recover from them, and locally adjusts the postimpact trajectory. Then, at the higher-level, we propose a search-based planning algorithm to determine how to best utilize potential collisions to improve certain metrics, such as control energy and computational time. Our method builds upon A* with jump points. We generate a novel heuristic function, and a collision checking and adjustment technique, thus making the A* algorithm converge faster to reach the goal by exploiting and utilizing possible collisions. The overall hierarchical framework generated by combining the global A* algorithm and the local deformation recovery and replanning strategy, as well as individual components of this framework, are tested extensively both in simulation and experimentally. An ablation study draws links to related state-of-the-art searchbased collision-avoidance planners (for the overall framework), as well as search-based collision-avoidance and sampling-based collision-inclusive global planners (for the higher level). Results demonstrate our method's efficacy for collision-inclusive motion planning and control in unknown environments with isolated obstacles for a class of impact-resilient robots operating in 2D.

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Stochastic behavior of robots that navigate by interacting with their environment

Cited by 9 publications

References 14 publications

Motion Planning for Collision-resilient Mobile Robots in Obstacle-cluttered Unknown Environments with Risk Reward Trade-offs

Motion Planning for Collision-resilient Mobile Robots in Obstacle-cluttered Unknown Environments with Risk Reward Trade-offs

Cooperative planning for physically interacting heterogeneous robots

Online Search-based Collision-inclusive Motion Planning and Control for Impact-resilient Mobile Robots

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