[Paper Review] EVA-Planner: Environmental Adaptive Quadrotor Planning

Lun Quan, Zhiwei Zhang+3 more|arXiv (Cornell University)|Nov 9, 2020

Robotic Path Planning Algorithms17 references4 citations

TL;DR

This paper proposes EVA-Planner, an environmental adaptive quadrotor trajectory planner that dynamically adjusts flight aggressiveness based on obstacle density and motion tendency using a multi-layered model predictive contouring control (Multi-MPCC) framework. By integrating environmental risk assessment with real-time motion dynamics, the method enables safer, smoother, and faster trajectories in complex 3D environments, outperforming prior adaptive planners in simulations and real-world tests with 100% success in challenging indoor and outdoor scenarios.

ABSTRACT

The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectively based on the obstacle distribution and quadrotor state. Firstly, we design an environmental adaptive safety aware method to assign the priority of the surrounding obstacles according to the environmental risk level and instantaneous motion tendency. Then, we apply it into a multi-layered model predictive contouring control (Multi-MPCC) framework to generate adaptive, safe, and dynamical feasible local trajectories. Extensive simulations and real-world experiments verify the efficiency and robustness of our planning framework. Benchmark comparison also shows superior performances of our method with another advanced environmental adaptive planning algorithm. Moreover, we release our planning framework as open-source ros-packages.

Motivation & Objective

• To address the lack of self-adaptive flight aggressiveness in existing quadrotor planners, especially in dynamic and dense environments.
• To develop a planner that automatically slows down in high-risk areas (e.g., dense forests) and accelerates in open spaces.
• To ensure trajectory smoothness, dynamical feasibility, and obstacle-free motion while maintaining real-time performance.
• To validate the planner’s robustness and efficiency through extensive simulations and real-world experiments.
• To release the framework as open-source ROS packages for broader research use.

Proposed method

• The method introduces an environmental adaptive safety-aware mechanism that assigns obstacle priority based on the gradient of the Euclidean Distance Transform (EDT) and the quadrotor’s instantaneous velocity vector.
• It computes an environmental risk level using the EDT gradient and motion tendency, enabling real-time danger assessment of surrounding obstacles.
• A multi-layered Model Predictive Contouring Control (Multi-MPCC) framework integrates the risk-aware obstacle prioritization into trajectory generation.
• The planner generates locally optimal, smooth, and dynamically feasible trajectories by optimizing for safety, aggressiveness, and smoothness in real time.
• The framework uses NLopt for nonlinear optimization and ensures sub-10ms computation time per MPC layer, enabling real-time operation.
• The system is implemented on-board and tested in both simulated and real-world environments, including complex indoor and outdoor scenarios.

Experimental results

Research questions

• RQ1 How can a quadrotor planner dynamically adjust flight aggressiveness based on real-time environmental risk and motion state?
• RQ2 What is the impact of integrating motion tendency and EDT gradient on obstacle prioritization and trajectory safety?
• RQ3 Can a unified multi-layered MPC framework generate smooth, safe, and dynamically feasible trajectories under varying environmental densities?
• RQ4 How does the proposed EVA-Planner compare to state-of-the-art adaptive planners like SDDM in terms of speed, safety, and adaptability?
• RQ5 What is the real-world performance of the planner in complex, unknown environments with limited sensor visibility?

Key findings

• EVA-Planner achieved 100% success rate in 10 repeated outdoor flights through dense bushes and open areas, demonstrating robustness in real-world unknown environments.
• In the narrow gate test, EVA-Planner showed a more reasonable velocity profile than SDDM, with smoother deceleration and acceleration, avoiding unnecessary conservatism.
• Benchmark results showed that EVA-Planner reduced average running time by 7.5% to 22.5% compared to SDDM across different obstacle densities, with increasing performance gap as density rose.
• The planner maintained real-time performance with optimization times under 10ms per MPC layer, enabling on-board execution.
• In complex indoor scenarios with sudden obstacles, EVA-Planner enabled safe avoidance where the non-adaptive version failed in 2 out of 3 trials.
• The method successfully enabled the quadrotor to fly at maximum speed in open areas while decelerating appropriately in high-risk zones, confirming adaptive aggressiveness.