Chance constraint based multi agent navigation under uncertainty

Gopalakrishnan, Bharath; Singh, Arun Kumar; Kaushik, Meha; Krishna, K. Madhava; Manocha, Dinesh

doi:10.1145/3132446.3134917

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…However, these approaches were deterministic and did not account for uncertainties in perception and motion. The concept of velocity obstacles was extended to handle motion uncertainties by using conservative bounding volumes [4]. Yet, the robot dynamics were not fully modeled and the motion was limited by planning a constant velocity motion.…”

Section: Related Workmentioning

confidence: 99%

Chance-Constrained Collision Avoidance for MAVs in Dynamic Environments

Zhu

Alonso–Mora

2019

IEEE Robot. Autom. Lett.

200

170

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Safe autonomous navigation of microair vehicles in cluttered dynamic environments is challenging due to the uncertainties arising from robot localization, sensing, and motion disturbances. This letter presents a probabilistic collision avoidance method for navigation among other robots and moving obstacles, such as humans. The approach explicitly considers the collision probability between each robot and obstacle and formulates a chance constrained nonlinear model predictive control problem (CCNMPC). A tight bound for approximation of collision probability is developed, which makes the CCNMPC formulation tractable and solvable in real time. For multirobot coordination, we describe three approaches, one distributed without communication (constant velocity assumption), one distributed with communication (of previous plans), and one centralized (sequential planning). We evaluate the proposed method in experiments with two quadrotors sharing thespace with two humans and verify the multirobot coordination strategy in simulation with up to sixteen quadrotors.

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Section: Related Workmentioning

confidence: 99%

Chance-Constrained Collision Avoidance for MAVs in Dynamic Environments

Zhu

Alonso–Mora

2019

IEEE Robot. Autom. Lett.

200

170

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“…Bounding volume expansion methods retain the linearity of ORCA constraints; hence, they are fast but tend to be conservative. They do not differentiate samples close to the mean from those farther away from the mean [13], [21]. Hence, they can lead to infeasible solutions in dense scenarios [13].…”

Section: B Uncertainty Modelingmentioning

confidence: 99%

SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms under Uncertainty

Arul¹,

Manocha²

2020

Preprint

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We present decentralized collision avoidance algorithms for quadrotor swarms operating under uncertain state estimation. Our approach exploits the differential flatness property and feedforward linearization to approximate the quadrotor dynamics and reciprocal collision avoidance. We account for the uncertainty in position and velocity by formulating the collision constraints as chance constraints, which describe a set of velocities that avoid collisions with a specified confidence level. We present two different methods for formulating and solving the chance constraint: our first method assumes a Gaussian noise distribution. Our second method is its extension to the non-Gaussian case by using a Gaussian Mixture Model (GMM). We reformulate the linear chance constraints into equivalent deterministic constraints on mean and covariance. Subsequently, the deterministic constraints are introduced in the MPC framework to compute a local collision-free trajectory for each quadrotor. We evaluate the proposed algorithm in simulations on benchmark scenarios and highlight its benefits over prior methods. We observe that both the Gaussian and non-Gaussian methods provide improved collision avoidance performance over the deterministic method. Further, the non-Gaussian method results in a relatively shorter path length compared to Gaussian formulations. On average, the Gaussian method requires ∼ 5ms ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ∼ 7ms ms on average in the presence of 4 agents.

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“…In most of the similar works, collision avoidance in dynamic environments is considered, considering the velocity of obstacles [23], decentralized Nonlinear Model Predictive Control (NMPC) [24] and sequential NMPC [25]. Uncertainties can be handled by enlarging bounding volumes [26], which might lead to conservative or infeasible solutions. Moreover, the chanceconstrained approaches are computationally intensive and thus not eligible for real-time collision avoidance [27].…”

mentioning

confidence: 99%

An Integrated Real-time UAV Trajectory Optimization and Potential Field Approach for Dynamic Collision Avoidance

Rao

Habibi

Sánchez-López

et al. 2023

2023 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents an integrated approach that combines trajectory optimization and Artificial Potential Field (APF) method for real-time optimal Unmanned Aerial Vehicle (UAV) trajectory planning and dynamic collision avoidance. A minimum-time trajectory optimization problem is formulated with initial and final positions as boundary conditions and collision avoidance as constraints. It is transcribed into a nonlinear programming problem using Chebyshev pseudospectral method. The state and control histories are approximated by using Lagrange polynomials and the collocation points are used to satisfy constraints. A novel sigmoid-type collision avoidance constraint is proposed to overcome the drawbacks of Lagrange polynomial approximation in pseudospectral methods that only guarantees inequality constraint satisfaction only at nodal points. Automatic differentiation of cost function and constraints is used to quickly determine their gradient and Jacobian, respectively. An APF method is used to update the optimal control inputs for guaranteeing collision avoidance. The trajectory optimization and APF method are implemented in a closed-loop fashion continuously, but in parallel at moderate and high frequencies, respectively. The initial guess for the optimization is provided based on the previous solution. The proposed approach is tested and validated through indoor experiments.

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Chance constraint based multi agent navigation under uncertainty

Cited by 5 publications

References 17 publications

Chance-Constrained Collision Avoidance for MAVs in Dynamic Environments

Chance-Constrained Collision Avoidance for MAVs in Dynamic Environments

SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms under Uncertainty

An Integrated Real-time UAV Trajectory Optimization and Potential Field Approach for Dynamic Collision Avoidance

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