Robot navigation methods tend to avoid contact, and consequently search for collision-free trajectories. For robots with high inertia and limited maneuverability, however, avoiding contact can require substantial steering effort and time, even when interactions with surrounding surfaces could be safely exploited. In this paper, we develop a planning method that deliberately uses controlled wall reflections to generate trajectories that can be easier and more efficient to execute than purely collision-free motion. We consider planar navigation in environments where a mobile robot is permitted to bounce off surrounding surfaces. To represent the resulting alternatives, we construct a reflection-augmented state graph in which paths are partitioned into distinct classes according to the sequence of walls used for reflection. This representation enables systematic enumeration of reflection strategies and identification of the lowest-cost path within each class. We show that, although a reflecting path cannot be shorter than the shortest collision-free path, it can reduce execution time and actuation effort by replacing costly changes in heading with controlled environmental interactions. The planned trajectories are executed using a contact-aware sampling-based controller with the robot's full dynamics. In our experiments, we demonstrate that in our simulated test scenario, the best reflecting class can reduce time and control effort. Our results show that controlled contact can provide dynamically advantageous navigation strategies that are excluded by conventional collision-avoidance formulations.
Figures & tables
Fig. 1 : Using collisions for robot navigation. Panel (a) shows a non-rigid robot (The SBlimp [ 3 ] ) that can bounce when colliding with a wall. Panel (b) illustrates a path that uses wall bouncing and Panel (c) shows a collision free path.
Fig. 2 : The state lattice. (a) The Nθ=16 headings are sampled as θk=arctan(j,i) , so every stride ends on a lattice vertex; the eight intermediate headings (blue) span more than one cell. (b) From (q,θ) only the three headings within Δmax=1 are expanded (black); the remaining strides (grey) generate no edge. Only a reflection may leave this set.
Fig. 3 : Top view of a planar bicopter. Thrusts FL,FR≥0 act in parallel along the body axis u^(θ) , offset by ±L (drawn unequal). Their difference is the only source of torque and no lateral force is available. The shaded disc of radius r is the collision geometry.
Fig. 4 : Every trajectory runs between the same qs and qg . In Panel (a), the path γ1′ meets s1 and s5 in the same order as γ1 , at different points. In Panel (b), the path γ2 inserts a bounce off s6 , and γ3 visits the same two segments as γ1 in the opposite order. As r is an ordered word, s1s5 , s6s1 and s5s1 are three distinct classes. In Panel (c), a path carrying the admissible word s5s1 whose next contact would return to s5 . The successor is rejected (dotted), and the trajectory is discarded before reaching the goal.
Fig. 5 : Generation of a reflected successor. The blocked ordinal stride δθ′=(1,1) (dashed) is replaced by its specular reflection about si , and the child inherits the parent’s word with si appended.
Fig. 6 : Contour and lag error. The marker σk selects a point pd(σk) on the reference and the unit tangent t(σk) there. The error ek to the robot at pk splits into a component along the tangent, the lag ekl , and one perpendicular to it, the contour ekc . Lag is disagreement between marker and robot about how far along the path they are; contour is how far the robot has drifted off it.
Fig. 7 : Best reflecting path (gold) and shortest reflection-free path (blue) for the same start–goal pair in each of the four test maps. The reflecting path is the lowest-cost representative of the top-ranked reflection class under ( 1 ); the reflection-free path is computed by a variant of the planner that discards blocked edges and permits ±45∘ turns at no cost.
Map
Grid
∣S∣
t [s]
Nexp
1
20×20
8
0.06
15 185
50×50
2.08
98 529
100×100
45.29
463 371
2
20×20
12
0.12
20 835
50×50
13.38
212 386
100×100
186.71
920 720
TABLE I : Wall-clock time to enumerate the twenty lowest-cost reflection classes, for the four maps of Fig. 7 at three grid resolutions. ∣S∣ is the number of wall segments (Def. 1 ), including the four boundary walls. Nexp is the total number of states expanded in the reflection-augmented graph.
Fig. 8 : Classes 1-9 for Map-4 found by our planner. Class 0, which is the best class, has been shown in 7(d) .
Fig. 9 : Execution metrics across the progress-reward sweep. Lines show medians, shaded regions show interquartile ranges, and points show individual successful runs.
Real-time collision avoidance for robotic manipulators requires fast reactions to unexpected obstacle motion and lookahead to avoid becoming trapped by near-future constraints. Full model predictive control can provide this foresight, but its online cost may grow quickly with horizon length, model fidelity, and the number of active geometric constraints. Conversely, horizon-free reactive methods are computationally efficient but can be short-sighted in dynamic clutter. We present a task-space receding-horizon controller that uses a short contact-consistent rollout to generate a terminal kinematic reference satisfying internal non-penetration constraints, then computes only the first input of a smooth minimum-acceleration transition toward that reference. Starting from a closed-loop inverse-kinematics regulation law, the rollout is performed with an iterative dynamics solver operating on inflated convex robot and obstacle geometries, so that robot-obstacle contacts, dynamic obstacle motion, and self-collisions can shape the terminal reference without requiring full constrained trajectory optimization. We analyze the contact-inactive closed loop and show local exponential task-space regulation under standard regularity assumptions. For contacts activated inside the rollout, we characterize the corresponding discrete updates and bound the effect of moving obstacles on regular operating sets. Simulations on a 40-DOF multi-chain system show that intermediate horizons balance anticipation, responsiveness, and computational cost. Hardware experiments on a 6-DOF platform demonstrate consistent sim-to-real behavior without accurate inertial parameter estimation, and comparisons against dynamic optimization fabrics and model predictive control (MPC) baselines show improved success rates in dynamic clutter while preserving solve times compatible with real-time execution in the tested regimes.
Mattia Penzotti, Marco Controzzi
Biorobotics Institute and the Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
This paper addresses the motion control problem for mobile robots in obstacle-cluttered environments. The mobile robot has partial environment information only, and aims to move from an initial position to a target position without collisions. For this purpose, a reactive planning based control strategy (RPCS) is proposed. First, the initial and target positions are connected as a reference trajectory. Then, a reactive planning strategy (RPS) is developed to ensure the collision avoidance by modifying the reference trajectory locally based on the partial environment information. Next, an adaptive tracking control strategy (ATCS) is proposed to track the reference trajectory with potentially local modifications via the discretization techniques. Finally, the RPS and ATCS are combined to establish the RPCS, whose efficacy and advantages are illustrated by numerical examples.
Li Tan, Junlin Xiong, Yan Wang +1
Department of Automation, University of Science and Technology of China, Hefei, 230031, China. · School of Intelligence Science and Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 515100, China · School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
Trajectory planning for mobile robots remains a major challenge, particularly in cluttered environments, where existing planning algorithms often fail or produce suboptimal paths. To address this issue, we propose an adaptive trajectory refinement algorithm, ART-TEB, comprising two main stages. First, to ensure safety at the path-segment level, a segment-wise conservative collision test is applied, recursively subdividing risky path segments until the collision risk is eliminated. Second, to guarantee pose-level safety, pose correction based on separation direction and line search is applied, ensuring that each pose in the trajectory is collision-free and maximally clear from obstacles. Simulation results demonstrate that the ART-TEB achieves up to 3.12x higher success rates and up to 23.7x faster average planning times than state-of-the-art approaches. Furthermore, real-world experiments confirm that the robot can safely pass through highly constrained environments while maintaining rapid planning performance.
Hahjin Lee, Young J. Kim
Department of Computer Science and Engineering at Ewha Womans University in Korea