T⋆: Time-Optimal Risk-Aware Motion Planning for Curvature-Constrained Vehicles

Song, Junnan; Gupta, Savita; Wettergren, Thomas A.

doi:10.1109/lra.2018.2876888

Cited by 19 publications

(21 citation statements)

References 17 publications

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“…Informed subsets [31] will also be incorporated into this non-uniform sampling framework to provide an even faster convergence rate. Finally, this work will be extended to provide these nonuniform sampling benefits to multi-speed non-holonomic vehicles where both travel time and collision risk [32] are considered in the cost [33], [20].…”

Section: Discussionmentioning

confidence: 99%

A Non-uniform Sampling Approach for Fast and Efficient Path Planning

Wilson¹,

Shen²,

Gupta³

2021

Preprint

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In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform partitioning scheme that divides the area into obstacle-free convex cells. The partitioning results in large cells in obstacle-free areas and small cells in obstacle-dense areas. Subsequently, the boundaries of these cells are used for sampling; thus significantly reducing the burden of uniform sampling. When compared with a standard uniform sampler, this smart sampler significantly 1) reduces the size of the sampling space while providing completeness and optimality guarantee, 2) provides sparse sampling in obstacle-free regions and dense sampling in obstacle-rich regions to facilitate faster exploration, and 3) eliminates the need for expensive collision-checking with obstacles due to the convexity of the cells. This sampling framework is incorporated into the RRT* path planner. The results show that RRT* with the non-uniform sampler gives a significantly better convergence rate and smaller memory footprint as compared to RRT* with a uniform sampler.

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Section: Discussionmentioning

confidence: 99%

A Non-uniform Sampling Approach for Fast and Efficient Path Planning

Wilson¹,

Shen²,

Gupta³

2021

Preprint

Self Cite

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“…Future research areas include: i) opportunistic scheduling [52] to enhance the speed of target discovery, ii) extension of the CARE algorithm to account for restricted communication, iii) integration of SLAM [53] with multi-robot control in the absence of localization devices, iv) consideration of threat levels in different tasks to compute the probability of success, and v) consideration of motion constraints [54][55] for the mobile robots.…”

Section: G Practical Applications Of Carementioning

confidence: 99%

CARE: Cooperative Autonomy for Resilience and Efficiency of robot teams for complete coverage of unknown environments under robot failures

Song

Gupta

2019

Auton Robot

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This paper addresses the problem of Multi-robot Coverage Path Planning (MCPP) for unknown environments in the presence of robot failures. Unexpected robot failures can seriously degrade the performance of a robot team and in extreme cases jeopardize the overall operation. Therefore, this paper presents a distributed algorithm, called Cooperative Autonomy for Resilience and Efficiency (CARE), which not only provides resilience to the robot team against failures of individual robots, but also improves the overall efficiency of operation via event-driven replanning. The algorithm uses distributed Discrete Event Supervisors (DESs), which trigger games between a set of feasible players in the event of a robot failure or idling, to make collaborative decisions for task reallocations. The game-theoretic structure is built using Potential Games, where the utility of each player is aligned with a shared objective function for all players. The algorithm has been validated in various complex scenarios on a high-fidelity robotic simulator, and the results demonstrate that the team achieves complete coverage under failures, reduced coverage time, and faster target discovery as compared to three alternative methods.

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“…If the class of CPT planners is expressive enough, we should be able to find a set of parameters that is able to to exactly mimic this drawn path. Since an arbitrary path P d belongs to a very high dimensional space 6 and the planner parameters are typically finite, any amount of parametric tuning may not produce good approximations. This is what we evaluate in the following.…”

Section: Cpt-planner Parameter Adaptationmentioning

confidence: 99%

“…Related Work: Traditional risk-aware path planning considers risk explicitly in forms such as motion and state uncertainty [5], collision time [6], or sensing uncertainty [7]. However, how these risks are perceived or relatively weighted has been an overlooked topic.…”

Section: Introductionmentioning

confidence: 99%

Planning under non-rational perception of uncertain spatial costs

Suresh¹,

Martínez²

2019

Preprint

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This work investigates the design of motionplanning strategies that can incorporate non-rational perception of risks associated with uncertain spatial costs. Our proposed method employs the Cumulative Prospect Theory (CPT) model to generate a perceived risk function across a given environment, which is scalable to high dimensional space. Using this, CPT-like perceived risks and path-length metrics are combined to define a cost function that is compliant with the requirements of asymptotic optimality of sampling-based motion planners (RRT*). The modeling capabilities of CPT are demonstrated in simulation by producing a rich set of meaningful paths, capturing a range of different risk perceptions in a custom environment. Furthermore, using a simultaneous perturbation stochastic approximation (SPSA) method, we investigate the capacity of these CPT-based risk-perception planners to approximate arbitrary paths drawn in the environment. We compare this adaptability with Conditional Value at Risk (CVaR), another popular risk perception model. Our simulations show that CPT is richer and able to capture a larger class of paths as compared to CVaR and expected risk in our setting.

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T⋆: Time-Optimal Risk-Aware Motion Planning for Curvature-Constrained Vehicles

Cited by 19 publications

References 17 publications

A Non-uniform Sampling Approach for Fast and Efficient Path Planning

A Non-uniform Sampling Approach for Fast and Efficient Path Planning

CARE: Cooperative Autonomy for Resilience and Efficiency of robot teams for complete coverage of unknown environments under robot failures

Planning under non-rational perception of uncertain spatial costs

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