Autonomous quadrotor flight in cluttered environment is an key capability for tasks like inspection and search. Navigation policies trained in simulation and transferred to hardware can fly at high speed in open space, but they can still collide as obstacle clearance shrinks. We attribute most of these collisions to a single mechanism, namely motion in directions the onboard camera does not observe. Heading that is not aligned with the direction of travel and lateral excursion during obstacle avoidance both carry the vehicle toward obstacles outside its field of view. We introduce ClearNav, a navigation policy trained with two reward terms that constrain motion to the observed direction, a forward-looking heading target and a penalty on lateral velocity. A third term penalizes speed near obstacles so that an obstacle entering the field of view remains avoidable. In simulation, across 18 held-out scenarios with 2304 paired flights per policy unseen in training, ClearNav improves success by 6.8 points, cuts the collision rate by 65% relative to the state of the arts, and flies at maximum 5m/s. For the proximity term alone under matched commanded speeds, the collision reduction still holds at 70%. The effect is significant on static scenes and consistent in direction on moving obstacles, though not significant there at the suite level, showing that safer flight in clutter is possible even on the limited sensors and compute of small drones. We also validate our navigation policy in a real-world cluttered environment, showing that it enables the quadrotor to navigate through the cluttered environment successfully and safely.
Figures & tables
Figure 1: ClearNav, an end-to-end navigation policy trained through differentiable physics, enables a drone to fly through cluttered environment while dodging obstacles by aligning its camera with its direction of travel and slowing down near obstacles.
Figure 2: Frames used in our formulation. All frames use right handed convention with positive z-axis pointing opposite to gravity at hover. World frame is indicated with blue, start frame with red and body frame with green.
Figure 3: Policy architecture. The three modality branches are projected to a common width nf and summed, so the recurrent input dimension does not grow with the number of sensors.
Figure 4: Our modifications to the loss function include three components: a velocity-aware proximity penalty, which penalizes high speed near obstacles regardless of direction; a yaw lookahead reward, which encourages the policy to align its heading with the anticipated velocity direction; and a body-frame lateral-velocity penalty, which discourages sideways motion.
Figure 5: Real world trajectory flown by the ClearNav policy. The drone moves left to avoid the first obstacle, then after looking at it tries to go below, yaws to the right to avoid the second, wider obstacle, and continues to the goal.
Figure 6: Real world trajectory flown by the baseline policy. The drone drifts toward the net to the side of the course, crashes into it, and never reaches the goal.
Success rate (%)
Collision rate (%)
Mean speed (m/s)
Scenario
∣O∣
L
v⋆ band
DA
B
ClearNav
DA
B
ClearNav
B
ClearNav
Baseline
30
25
3.0–6.0
75.4
89.8
96.1
12.7
10.2
3.9
2.36
3.53
Sparse
10
25
3.0–6.0
90.6
94.5
99.2
2.3
5.5
0.8
2.40
3.59
Dense
60
25
3.0–6.0
53.6
87.5
95.3
24.9
11.7
4.7
2.33
3.46
Spheres Heavy
30
25
3.0–6.0
73.8
90.6
96.9
13.1
8.6
3.1
2.37
3.51
Cubes Heavy
30
25
3.0–6.0
82.1
91.4
99.2
6.9
8.6
0.8
2.36
3.56
Table I : Static generalization suite: 18 scenarios, 128 episodes per scenario per policy. DA = DiffAero v1 (cross-simulator; Sec. III-F ), B = DepthNav baseline, ClearNav = ours (baseline +Loa+Lslip+ look-ahead yaw of ( 15 )). Best of B/ClearNav in bold.
Success (%)
Collision (%)
Speed (m/s)
Scenario
B
OA-only
B
OA-only
B
OA-only
Baseline
89.1
100.0
9.4
0.0
2.03
2.71
Sparse
98.4
98.4
1.6
1.6
2.09
2.74
Dense
87.5
96.1
11.7
3.1
2.03
2.73
Very Dense
87.5
94.5
10.9
5.5
2.01
2.75
mean
90.6
97.2
8.4
2.6
2.04
2.73
Table II : Matched-velocity control, run on OA-only rather than the full-bundle ClearNav. Both policies are commanded the same v⋆∼U[2,4] m/s band, removing the commanded-speed difference between the two training configurations. 128 episodes per scenario per policy. The full-bundle ClearNav was not re-run under matched velocity, so this controls the confound for the proximity term alone, not for the bundle; see Sec. III-D .
Success (%)
Collision (%)
Succ., no sep.
Scenario
∣O∣
vo
B
Ours
B
Ours
B
Ours
Slow Drift
10
0.5
95.8
100.0
4.2
0.0
91.7
95.8
Crossing
10
2.0
70.8
70.8
29.2
29.2
79.2
70.8
Orbiting
12
0.9–8.8
45.8
41.7
54.2
58.3
45.8
62.5
Oscillating
12
0–4.8
66.7
95.8
33.3
4.2
62.5
100.0
Dense Swarm
18
1.3
66.7
83.3
33.3
16.7
79.2
91.7
Table III : Moving-obstacle suite. Obstacles are relocated at the control rate on trajectories seeded identically for both policies. 24 episodes per scenario per policy. Primary columns enforce obstacle–obstacle separation; the right-hand pair repeats without it. vo is a range for orbiters ( ∣ω∣r , ∣ω∣∈[0.3,0.8] rad/s) and oscillators (speed varies within an episode up to the quoted peak).
obstacle speed vo (m/s)
1
2
3
4
5
B success
83.3
79.2
50.0
70.8
37.5
OA-only success
95.8
54.2
58.3
70.8
33.3
B collision
16.7
20.8
50.0
29.2
62.5
OA-only collision
4.2
45.8
41.7
29.2
66.7
Table IV : Crossing-traffic speed sweep, run on OA-only. All five columns share obstacle seed 102, so the obstacle field and headings are identical and only the speed magnitude changes. Rates in %.
Obstacle avoidance for autonomous quadrotors in cluttered environments remains challenging, especially when both dense static obstacles and moving obstacles are present. Existing methods are often tailored to either static or dynamic settings, which always limit their adaptability across diverse environments. This paper presents TransNav, a transformer-based end-to-end navigation method for quadrotor obstacle avoidance in both static and dynamic environments. TransNav employs a patch-based point-cloud encoding scheme that combines local feature extraction with global contextual modeling, enabling compact yet informative environment representation. The method integrates perception and navigation in a single architecture and directly predicts a finite-horizon action sequence, from which the first action is executed. Moreover, we develop an unsupervised learning strategy by incorporating a lightweight point-mass dynamics model, which directly optimizes trajectory-level costs over the predicted sequence through numerical gradient propagation. Thus, the policy can be efficiently trained without expert demonstrations. Simulation and real-world experiments show that the proposed method achieves strong performance in dense static environments and competitive obstacle-avoidance capability in dynamic scenarios, while maintaining low inference cost for onboard deployment.
Jianghan Zhu, Zhuo Li, Yanjun Zhang +1
The authors are with the State Key Laboratory of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
Safe quadrotor navigation in cluttered and dynamic environments depends not only on instantaneous geometric perception, but more critically on anticipating collision risks induced by relative motion. Conventional modular pipelines frequently suffer from perception latency, while end-to-end learning methods relying on implicit scalar rewards often struggle to extract reliable spatio-temporal features without physics-grounded supervision. To address this, we propose an anticipatory risk-guided reinforcement learning framework. Leveraging privileged simulator states, we construct a directionally aligned future collision risk map based on the Closest Point of Approach (CPA). Through an asymmetric actor-critic architecture, the network is trained to self-predict this structured risk, which explicitly guides the visual policy during deployment. A lightweight spatio-temporal encoder extracts motion cues directly from onboard depth sequences, bypassing explicit object tracking or optical flow estimation. Extensive simulated and real-world experiments demonstrate that our method effectively improves safety margins and flight efficiency in dense dynamic clutters compared to existing baselines. Furthermore, the learned policy achieves robust zero-shot Sim-to-Real transfer on a physical quadrotor, relying purely on abstracted spatio-temporal depth sequences and its self-predicted risk priors, validating the effectiveness of our approach and its robust generalization from simulation to reality.
Yuchao Mei, Guohao Zhang, Luxia Ai +2
National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Quadrotor unmanned aerial vehicles (UAVs) are increasingly deployed in complex missions that demand reliable autonomous navigation and robust obstacle avoidance. However, traditional modular pipelines often incur cumulative latency, whereas purely reinforcement learning (RL) approaches typically provide limited formal safety guarantees. To bridge this gap, we propose an end-to-end RL framework augmented with model-based safety mechanisms. We incorporate physical priors in both training and deployment. During training, we design a physics-informed reward structure that provides global navigational guidance. During deployment, we integrate a real-time safety filter that projects the policy outputs onto a provably safe set to enforce strict collision-avoidance constraints. This hybrid architecture reconciles high-speed flight with robust safety assurances. Benchmark evaluations demonstrate that our method outperforms both traditional planners and recent end-to-end obstacle avoidance approaches based on differentiable physics. Extensive experiments demonstrate strong generalization, enabling reliable high-speed navigation in dense clutter and challenging outdoor forest environments at velocities up to 7.5 m/s}.
Jiarui Zhang, Chengyong Lei, Chengjiang Dai +4
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · Differential Robotics, Hangzhou 311121, China.